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

Heike Eichenauer; Ulrike Ehlert

doi:10.1186/s13148-023-01557-y

. 2023 Sep 16;15:152. doi: 10.1186/s13148-023-01557-y

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

Heike Eichenauer ¹, Ulrike Ehlert ^1,^✉

PMCID: PMC10505322 PMID: 37716973

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-023-01557-y.

Keywords: DNA methylation, Mental disorders, Prenatal famine exposure, Epigenetic, Pregnancy

Background

Unfavorable environmental conditions during pregnancy have been shown to promote the onset of mental disorders in the offspring [1–3] via epigenetic mechanisms [4–6]. One epigenetic mechanism that can be changed by adverse intrauterine exposure and influences the development of offspring health is deoxyribonucleic acid (DNA) methylation [5, 7–10]. DNA methylation is the addition of methyl groups to cytosine-guanine dinucleotides (CpG), with the potential to regulate gene expression [11–15]. 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 [17–19] 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 [25–27]. 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 — 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

References	Cohort	Sample description of groups with prenatal, non-prenatal or non-exposure	Assessment of symptoms of psychopathology	Main results
Zhou et al. [42]	Chinese Famine*^a	Prenatal exposure N = 1575, ♀ not stated, mean age 50	CES-D	Increased depressive symptoms after prenatal exposure and non-prenatal exposure compared to non-exposure***
		Non-prenatal exposure N = 9138, ♀ not stated, age 57–69
		Non-exposure N = 1968, ♀ not stated, mean age 47
He et al. [43]	Chinese Famine*^a	Prenatal exposure N = 76, ♀ = 48, mean age not stated	GDS	Increased risk of depression after prenatal exposure compared to non-prenatal exposure*
He et al. [43]	Chinese Famine*^a	Non-prenatal exposure N = 80, ♀ = 28, mean age not stated	GDS
Franzek et al. [57]	Dutch Famine*^b	Prenatal exposure N = 5549	Case records of individuals with addictive behaviors in the database of the Dutch mental health care organizations	Increased risk of addictive behaviors after prenatal exposure during 1st trim (in men)* and 3rd trim (in women)* compared to non-exposure
		1st trim = 1738, ♀ = 812
		2nd trim = 568, ♀ = 287
		3rd trim = 3243, ♀ = 1608
		Non-exposure N = 11,630, ♀ = 5676
		Age not stated
van den Broek et al. [38]	Dutch Famine*^b	Prenatal exposure N = 23, ♀ = 11	MHI-5	Poorer mental health after prenatal exposure compared to non-prenatal exposure** and non-exposure*
		Non-prenatal exposure N = 41, ♀ = 19
		Non-exposure N = 83, ♀ = 34
		Mean age of entire sample 57
Franke et al. [45]	Dutch Famine*^b	Prenatal exposure N = 41, ♀ = 22, mean age 67	HADS-A/-D	No significant differences between prenatal exposure, non-prenatal exposure and non-exposure in anxiety and depressive symptoms
		Non-prenatal exposure N = 35, ♀ = 21, mean age 69
		Non-exposure N = 42, ♀ = 23, mean age 67
He et al. [50]	Chinese Famine*^a	Rural population N = 239,055, ♀ = 119,217, mean age not stated	Diagnosis of schizophrenia with ICD-10 semi-structured symptom checklist for mental disorders	Only in rural population, increased risk of schizophrenia after prenatal exposure compared to non-exposure*
		Urban population N = 148,038, ♀ = 73,471, mean age not stated
		N for prenatal exposure, non-prenatal exposure and non-exposure not stated
Li et al. [40]	Chinese Famine*^a	Prenatal exposure N = 996	CES-D	More depressive symptoms after prenatal exposure during 1st and 2nd trim compared to non-exposure*
		Non-exposure N = 356
		Trim and ♀ not stated
		Age of entire sample > 45
Li et al. [39]	Chinese Famine*^a	Prenatal exposure N = 1847, ♀ = 1019	CES-D	Only in women, more depressive symptoms after prenatal exposure compared to non-exposure (significant, but p not stated)
		Non-exposure N = 2698, ♀ = 1671
		Age of entire sample > 45
Wang et al. [47]	Chinese Famine*^a	Prenatal exposure N = 81,279, ♀ = 40,509	Diagnosis of schizophrenia with ICD-10 semi-structured symptom checklist for mental disorders	Increased risk of schizophrenia after prenatal exposure compared to non-prenatal exposure* and non-exposure*
		Non-prenatal exposure N = 120,287, ♀ = 59,650
		Non-exposure N = 150,429, ♀ = 75,470
		Age not stated
Huang et al. [46]	Chinese Famine*^a	Prenatal exposure N = 1477, ♀ = 752	GHQ-12 and the presence (yes/no) of eight additional risk factors for mental disorders	In women, increased GHQ-12 scores and risk of mental disorders and in men, decreased GHQ-12 scores** after prenatal exposure compared to non-exposure
		Non-exposure N = 1029, ♀ = 514
		Age not stated
de Rooij et al. [44]	Dutch Famine*^b	Prenatal exposure N = 334	HADS-A/-D	Only in men, higher HADS-D and HADS-A scores after prenatal exposure during 1st trim compared to non-prenatal exposure* and non-exposure*
		1st trim = 75, ♀ = 44, mean age 58
		2nd trim = 121, ♀ = 76, mean age 58
		3rd trim = 138, ♀ = 77, mean age 59
		Non-prenatal exposure N = 253, ♀ = 136, mean age 59
		Non-exposure N = 232, ♀ = 117, mean age 57
Song et al. [51]	Chinese Famine*^a	Prenatal exposure N = 81,318, ♀ = 40,415	Diagnosis of schizophrenia based on CCMD with a semi- structured interview	Increased risk of developing schizophrenia after non-exposure compared to prenatal exposure*
		Non-prenatal exposure N = 102,068, ♀ = 50,422
		Non-exposure N = 110,970, ♀ = 56,706
		Age of entire sample 22–32
Stein et al. [41]	Dutch Famine*^b	Prenatal exposure N = 411, mean age 59	CES-D	Increased depressive symptoms after periconceptional and prenatal exposure compared to time and sibling controls (significant, but p not stated)
		Periconceptional exposure N = 91
		Time controls N = 218, mean age 59
		Sibling controls N = 294, mean age 57
		♀ not stated
Xu et al. [36]	Chinese Famine*^a	Prenatal exposure N = 126,579	Case records (1971–2001) of schizophrenia patients from Longquanshan hospital	Increased risk of schizophrenia after prenatal exposure compared to non-prenatal exposure* and non-exposure*
		Non-prenatal exposure N = 329,189
		Non-exposure N = 494,684
		Age and ♀ not stated
Franzek et al. [56]	Dutch Famine*^b	Prenatal exposure N = 2202, ♀ = 1055	Case records of addictive disorder patients in the database of the Dutch mental health care organization	Increased risk of addictive disorders, especially in men, after prenatal exposure during 1st trim compared to non-exposure*
		Non-exposure N = 5441, ♀ = 2753
		Age not stated
St. Clair et al. [35]	Chinese Famine*^a	Prenatal exposure N = 141,713	Case records (1971–2001) of schizophrenia patients from Fourth People’s hospital	Increased risk of schizophrenia after prenatal exposure compared to non-prenatal exposure* and non-exposure*
		Non-prenatal exposure N = 176,335
		Non-exposure N = 243,647
		Age and ♀ not stated
Brown et al. [52]^c	Dutch Famine*^b	Prenatal exposure N = 41,969	Case records of patients with major affective disorder from the Dutch national psychiatric registry from 1970 to 1996	Increased risk of major affective disorder requiring hospitalization after prenatal exposure during 2nd*and 3rd trim for men and during 3rd trim* for women compared to non-exposure
		1st trim = 9656, ♀ = 4672
		2nd trim = 14,645, ♀ = 7185
		3rd trim = 17,668, ♀ = 8727
		Non-exposure N = 115,877, ♀ = 56,472
		Age of entire sample ≥ 18
Neugebauer et al. [54]	Dutch Famine*^b	Severe prenatal exposure N = 14,310	Non-standardized diagnosis of ASPD in men at time of medical examination for military induction	Increased risk of ASPD after severe prenatal exposure during 1st and/ or 2nd trim compared to non-prenatal and non-exposure (significant, but p not stated)
		1st and/or 2nd trim = 9252, 3rd trim = 5058
		Non-prenatal and non-exposure N = 45,007
		Age of entire sample ≥ 18, ♀ not stated
Hoek et al. [55]	Dutch Famine*^b	Prenatal exposure (Aug-Oct 1945) N = 2610	Diagnosis of schizoid personality disorder in men with ICD-6 and ICD-9	Increased risk of schizoid personality disorder after prenatal exposure (Oct-Dec) compared to non-prenatal and non-exposure*
		Prenatal exposure (Oct-Dec 1945) N = 2056
		Non-prenatal and non-exposure N = 64,265
		Age of entire sample > 18, ♀ not stated
Susser et al. [48]^c	Dutch Famine*^b	Conception at peak N = 4190, ♀ = 2006	Case records of patients with schizophrenia from the Dutch national psychiatric registry from 1970 to 1992	Only for conception at peak of famine, increased risk of schizophrenia compared to non-prenatal and non-exposure**
		Conception not at peak N = 5466, ♀ = 2666
		Non-prenatal and non-exposure N = 136,691, ♀ = 66,748
		Age of entire sample 24–48
Brown et al. [53]^c	Dutch Famine*^b	Prenatal exposure N = 41,969	Case records of patients with major affective disorders from the Dutch national psychiatric registry from 1978 to 1991	Only in men, increased risk of major affective disorders after prenatal exposure during 2nd trim compared to non-prenatal and non-exposure*
		1st trim = 9656, ♀ = 4672
		2nd trim = 14,645, ♀ = 7185
		3rd trim = 17,668, ♀ = 8727
		Non-prenatal and non-exposure N = 397,052
		1st trim = 136,691, ♀ = 66,748
		2nd trim = 131,702, ♀ = 64,235
		3rd trim = 128,659, ♀ = 62,693
		Age of entire sample 32–47
Susser et al. [49]^c	Dutch Famine*^b	Prenatal exposure 1st trim = 9656, ♀ = 4672	Case records of patients with schizophrenia from the Dutch national psychiatric registry from 1978 to 1989	Only in women, increased risk of schizophrenia after prenatal exposure during 1st trim compared to non-prenatal and non-exposure (significant, but p not stated)
		Non-prenatal and non-exposure N = 116,934, ♀ = 57,034
		Age of entire sample ≥ 19

Open in a new tab

*^aChinese Famine:1959–1961; *^bDutch Famine:1944–1945; ^cpossible sample overlap between [48, 49] as well as [52, 53]; ASPD Antisocial Personality Disorder, CCMD Chinese Classification of Mental Disorders, CES-D Center for Epidemiologic Studies Depression Scale, GDS Geriatric Depression Scale, GHQ -12 General Health Questionnaire, HADS-A/-D Hospital Anxiety and Depression Scale, ICD International Statistical Classification of Diseases and Related Health Problems, MHI-5 Mental Health Inventory, trim Trimester; *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001

Table 2.

Effects of prenatal exposure to famine on (epi)genome-wide DNA methylation of the offspring

References	Cohort	Sample description of groups with prenatal, non-prenatal or non-exposure	Chromosome	Gene	No. CpG	DNA methylation analysis from blood	Main results
Li et al. [65]	Chinese Famine*^a	Prenatal exposure N = 79, ♀ = 49, mean age 57	–	–	–	Illumina Infinium Methylation EPIC BeadChip	No significant differences in DNA methylation (DMRs and CpGs) between prenatal exposure and non-exposure after controlling for multiple testing
Li et al. [65]	Chinese Famine*^a	Non-exposure N = 105, ♀ = 31, mean age 53	–	–	–	Illumina Infinium Methylation EPIC BeadChip
Jiang et al. [63]	Chinese Famine*^a	Prenatal exposure N = 46, ♀ = 24, mean age 52	–	–	–	Illumina Infinium Human Methylation 850 K BeadChip	601 DMRs with significant* hypermethylation and 360 DMRs with significant* hypomethylation after prenatal exposure compared to time controls (for more details, see Fig. 1 in [63])
Jiang et al. [63]	Chinese Famine*^a	Time controls N = 46, ♀ = 24, mean age 53	–	–	–	Illumina Infinium Human Methylation 850 K BeadChip
He et al. [58]	Chinese Famine*^a	Early prenatal exposure N = 25, ♀ = 15, mean age 50	chr3:48481268–48481793	CCDC51/TMA7	13	Illumina Infinium Human Methylation 450 BeadChip	613 DMRs with significant methylation (significance and direction of effect not stated): specifically, hypomethylation of CCDC51/TMA7**, ENO2**** and ZNF226** after early prenatal exposure compared to non-exposure
		Non-exposure N = 54, ♀ = 33, mean age 47	chr12:7023752–7024121	ENO2	4
			chr19:44669146–44669354	ZNF226	5
Tobi et al. [64]^d	Dutch Famine*^b	Prenatal exposure N = 348, ♀ = 188, mean age 59	chr21:43655316	ABCG1	1	Illumina Infinium Human Methylation 450 BeadChip	Of 342,596 CpGs, 17 CpGs with significant differences in methylation between prenatal exposure and sibling controls: hypermethylation of ABCG1*, CCDC155***, FAM150B***, METTL8*, PNPO***, PPAP2C***, SLC38A2**, SYNGR1, TACC1*** and ZNF385A*** and hypomethylation of CRELD2, LRRC8D, LOC100132354, OSBPL5/MRGPRG, TXNIP* and PFKFB3*
		Prenatal exposure N = 348, ♀ = 188, mean age 59	chr19:49891270	CCDC155	1
		Sibling controls N = 463, ♀ = 264, mean age 58	chr19:49891574	CCDC155	1
			chr22:50327986	CRELD2	1
			chr2:366113	FAM150B	1
			chr6:43894639	LOC100132354	1
			chr1:90288099	LRRC8D	1
			chr2:172203847	METTL8	1
			chr11:3225076	OSBPL5/MRGPRG	1
			chr10:6214026	PFKFB3	1
			chr17:46022809	PNPO	1
			chr19:292167	PPAP2C	1
			chr12:46737123	SLC38A2	1
			chr22:39759864	SYNGR1	1
			chr1:145441552	TXNIP	1
			chr8:38586183	TACC1	1
			chr12:54764265	ZNF385A	1
Finer et al. [59]	Bangladesh Famine*^c	Prenatal exposure N = 40 Non-prenatal exposure N = 49 Non-exposure N = 54	–	–	–	Illumina Infinium Human Methylation 450 BeadChip	No significant differences in DNA methylation between prenatal exposure, non-prenatal exposure and non-exposure
Tobi et al. [60]^d	Dutch Famine*^b	Prenatal exposure during any week N = 348, ♀ = 188, mean age 59; conception N = 74, ♀ not stated; weeks 1–10 = 73, ♀ = 39; weeks 11–20 = 123, ♀ = 66; weeks 21–30 = 143, ♀ = 72; weeks 31-delivery = 128, ♀ = 66	chr2:366113	FAM150B/TMEM18	1	Illumina Infinium Human Methylation 450 BeadChip	Hypomethylation of TMEM105/SLC38A10* after exposure during conception compared to time and sibling controls; hypermethylation of FAM150B/TMEM18, PPAP2C, SLC38A2*** and hypomethylation of OSBPL5/ MRGPRG* after exposure during weeks 1–10 compared to time and sibling controls; hypermethylation of ZNF385A* and TACC1* after exposure during any week compared to time and sibling controls
			chr11:3225076	OSBPL5/MRGPRG	1
			chr19:292167	PPAP2C	1
			chr12:46737123	SLC38A2	1
			chr8:38586183	TACC1	1
			chr17:79283915	TMEM105/SLC38A10	1
			chr12:54764265	ZNF385A	1
		Time controls N = 160, ♀ = 88, mean age 59
		Sibling controls N = 303, ♀ = 176, mean age 57
Tobi et al. [61]	Dutch Famine*^b	Periconceptional exposure N = 24, ♀ = 12, mean age 58	–	–	–	RRBS	181 DMRs with 60.8% significantly hypermethylated and 39.2% hypomethylated after periconceptional exposure compared to sibling controls (for more details and significance see S1 in [61])
Tobi et al. [61]	Dutch Famine*^b	Sibling controls N = 24, ♀ = 12, mean age 57	–	–	–	RRBS
Tobi et al. [66]	Dutch Famine*^b	Periconceptional exposure N = 60, ♀ = 32, mean age 58	–	LINE-1^e	–	Pyrosequencing	No significant difference in global DNA methylation between periconceptional exposure and sibling controls
Tobi et al. [66]	Dutch Famine*^b	Sibling controls N = 60, ♀ = 32, mean age 57	–	LINE-1^e	–	Pyrosequencing
Lumey et al. [62]	Dutch Famine*^b	Prenatal exposure N = 350, ♀ = 189, mean age 59	chr17	Sat2	–	MethyLight	No significant differences in global DNA methylation between prenatal exposure and time and sibling controls
		Time controls N = 290, ♀ = 154, mean age 59	–	LINE-1^e		Pyrosequencing
		Sibling controls N = 307, ♀ = 175, mean age 57				LUMA

Open in a new tab

*^aChinese Famine: 1959–1961, *^bDutch Famine: 1944–1945; *^cBangladesh Famine: 1974–1975, ^dsample overlap between [60, 64], ^eestimate of global methylation; ABCG1 ATP Binding Cassette Subfamily G Member 1, CCDC51 Coiled-Coil Domain Containing 51, CCDC155 Coiled-Coil Domain Containing 155, CRELD2 Cysteine Rich with EGF-Like Domains 2, DMR Differentially Methylated Region, ENO2 Enolase 2, FAM150B Family with sequence similarity 150 member B, LINE-1 Long interspersed nucleotide element-1, LOC100132354 LOC100132354, LRRC8D Leucine Rich Repeat Containing 8 VRAC Subunit D, LUMA Luminometric methylation assay, METTL8 Methyltransferase 8, MRGPRG MAS-related GPR family member G, OSBPL5 Oxysterol binding protein-like 5, PFKFB3 6-Phosphofructo-2-Kinase/Fructose-2,6-Biphosphatase 3, PNPO Pyridoxamine 5′-Phosphate Oxidase, PPAP2C Phosphatidic acid phosphatase 2c, RRBS Reduced representation bisulfite sequencing, Sat2 Satellite repeat-2, SLC38A2 Solute carrier family 38 member 2, SLC38A10 Solute Carrier Family 38 Member 10, SYNGR1 Synaptogyrin 1, TACC1 Transforming Acidic Coiled-Coil Containing Protein 1, TMA7 Translation machinery-associated protein 7, TMEM18 Transmembrane protein 18, TMEM105 TMEM105 long non-coding RNA, TXNIP Thioredoxin Interacting Protein, ZNF226 Zinc finger protein 226, ZNF385A Zinc finger protein 385A; *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001

Table 3.

Effects of prenatal exposure to famine on targeted DNA methylation of the offspring

References	Cohort	Sample description of groups with prenatal, non-prenatal or non-exposure	Chromosome	Gene	No. CpG	DNA methylation analysis from blood	Main results
Jiang et al. [63]	Chinese Famine*^a	Prenatal exposure N = 194, ♀ = 89, mean age 52	chr3:148416100–148416355	AGTR1	1	Bisulfite sequencing	Hypomethylation of AGTR1 (cg13528513), AGTR1 (cg20906621), and PRKCA*** after prenatal exposure compared to time controls
		Prenatal exposure N = 194, ♀ = 89, mean age 52	chr3:148418205–148418530	AGTR1	1
		Time controls N = 192, ♀ = 94, mean age 52	chr17:64649040–64649570	PRKCA	1
Wang et al. [72]^d	Chinese Famine*^a	Prenatal exposure N = 75, ♀ = 38, mean age 55	chr11:2126035–2126372	IGF2	8	EpiTYPER	Hypermethylation of IGF2 CpG2* and INSR CpG1, 4, 5 and 7 after prenatal exposure compared to time controls; no significant differences for other CpGs
		Prenatal exposure N = 75, ♀ = 38, mean age 55	chr19:7110130–7110574	INSR	9
		Time controls N = 160, ♀ = 80, mean age 55
Wang et al. [74]^d	Chinese Famine*^a	Prenatal exposure N = 75, ♀ = 38, mean age 55	chr11:68286513–68286952	CPT1A	11	EpiTYPER	Hypermethylation of INSR CpG1*, 4, 5* and 7*** after prenatal exposure compared to time controls; no significant differences for CPT1A
		Prenatal exposure N = 75, ♀ = 38, mean age 55	chr19:7110130–7110574	INSR	9
		Time controls N = 160, ♀ = 80, mean age 55
Finer et al. [59]	Bangladesh Famine*^c	Prenatal exposure N = 13	chr6:151646312–151647133	AKAP12	9	Bisulfite Pyrosequencing	Hypomethylation of VTRNA2-1* and EXD3* after prenatal exposure compared to non-prenatal exposure; hypermethylation of PAX8* and hypomethylation of ZFP57* and PRDM9*** after prenatal exposure compared to non-prenatal and non-exposure; no significant differences for other genes
		Non-prenatal exposure N = 30	chr12:57040045–57040204	ATP5B	3
		Non-exposure N = 18	chr2:74357713–74357851	BOLA	2
		Age and ♀ not stated	chr9:140311919–140311437	EXD3	3
			chr6:32729442–32729847	HLA-DQB2	15
			chr5:191242–192103	LRRC14B	11
			chr18:77918588–77918142	PARD6G	4
			chr2:113992762–113993313	PAX8	8
			chr17:17109570–17110120	PLD6	8
			chr5:23507030–23507752	PRDM9	7
			chr4:155702411–155702351	RBM46	11
			chr13:36944640–36944649	SPG20	2
			chr5:135415762–135416613	VTRNA2-1	15
			chr6:29648345–29649024	ZFP57	18
			chr4:2366672–2367137	ZFYVE28	7
			chr1:227746294–227746111	ZNF678	3
Tobi et al. [61]^d	Dutch Famine*^b	Periconceptional exposure N = 60, ♀ = 32, mean age 58	chr10:73227653–73227914	CDH23	4	EpiTYPER	Hypermethylation of CDH23 CpG1*, 2, 3–4*, CPT1A* CpG 8–10, 12, INSR CpG2*, SMAD7* CpG1*, 2, 3–4*, 5–7 and hypomethylation of KLF13 CpG2, 4- 7,9* after periconceptional exposure compared to sibling controls; no significant differences for RFTN1
		Periconceptional exposure N = 60, ♀ = 32, mean age 58	chr11:68286598–68286810	CPT1A	8
		Sibling controls N = 60, ♀ = 32, mean age 57	chr19:7110140–7110418	INSR	3
			chr18:44677194–44677679	SMAD7	7
			chr15:29425223–29425563	KLF13	6
			chr3:16394247–16394578	RFTN1	11
Tobi et al. [66]^d	Dutch Famine*^b	Periconceptional exposure N = 60, ♀ = 32, mean age 58	chr11:1975948–1976360	H19 DMR	9	EpiTYPER	Hypomethylation of IGF2 DMR0 upstr. CpG1*, 2, 3*, 4, IGF2 DMR0*, IGF2* DMR0 downstr. CpG8, 12–13*, IGF2* DMR2 CTCF CpG1,4, INSIGF* and hypermethylation of IGF2AS DMR1 CpG41** and IGF2AS DMR1 CTCF CpG20*, 22 after periconceptional exposure compared to sibling controls; no significant differences for IGF DMR2 S.L. and H19
		Periconceptional exposure N = 60, ♀ = 32, mean age 58	chr11:2138912–2139216	INSIGF	n.s.
		Sibling controls N = 60, ♀ = 32, mean age 57	chr11:2111300–2111791	IGF2 DMR2 S.L	8
			chr11:2112023–2112312	IGF2 DMR2 CTCF	3
			chr11:2117482–2117948	IGF2AS	12
			chr11:2118126–2118422	IGF2AS CTCF	12
			chr11:2125961–2126065	IGF2 DMR0 upstr	5
			chr11:2126035–2126372	IGF2 DMR0	n.s.
			chr11:2127117–2127220	IGF2 DMR0 downstr	3
Veenendaal et al. [73]	Dutch Famine*^b	Prenatal exposure N = 319	chr5:142782821–142783152	GR 1-C	n.s.	PCR	No significant differences for GR1-C, LPL, PI3kinase and PPARγ in each trimester compared to non-prenatal exposure and non-exposure
		1st trim = 73, ♀ = 42, mean age 58; 2nd trim = 112, ♀ = 68, mean age 58; 3rd trim = 134, ♀ = 75, mean age 59	chr8:19796366–19796515	LPL
			chr5:67521933–67522282	PI3kinase
			chr3:12392392–12392591	PPARy
		Non-prenatal exposure N = 235, ♀ = 127, mean age 59
		Non-exposure N = 205, ♀ = 103, mean age 57
Tobi et al. [70]^d	Dutch Famine*^b	Group 1	chr9:106730323–106730642	ABCA1	22	EpiTYPER	Group 1
		Periconceptional exposure N = 60, ♀ = 32, mean age 58	chr19:50109726–50110115	APOC1	6		Hypermethylation of ABCA1, IL-10*, LEP and GNASAS* and hypomethylation of INSIGF* after periconceptional exposure compared to sibling controls; no significant differences for other genes
		Periconceptional exposure N = 60, ♀ = 32, mean age 58	chr8:67253246–67253686	CRH	4
		Sibling controls N = 60, ♀ = 32, mean age 57	chr16:52383225–52383575	FTO	6
			chr20:56896823–56897145	GNASA/B	15
			chr20:56859210–56859503	GNASAS	17
			chr7:50818080–50818483	GRB10	7
		Group 2	chr6:160346346–160346595	IGF2R	10		Group 2
		Prenatal exposure 3rd trim N = 62, ♀ = 34, mean age 59	chr1:205012634–205012962	IL-10	4		Hypomethylation of GNASAS*** after exposure during 3rd trim compared to sibling controls; no significant differences for other genes
		Prenatal exposure 3rd trim N = 62, ♀ = 34, mean age 59	chr11:2138912–2139216	INSIGF	4
		Sibling controls N = 62, ♀ = 34, mean age 57	chr11:2677737–2678040	KCNQ1OT1	17
			chr7:127668290–127668646	LEP	9
			chr14:100361166–100361395	MEG3	9
			chr5:142763741–142764104	NR3C1	17
			chr6:2790712–2791113	TNF	7
Heijmans et al. [29]^d	Dutch Famine*^b	Group 1	chr11:2126035–2126372	IGF2	5	EpiTYPER	Group 1
		Periconceptional exposure N = 60, ♀ = 32, mean age 58					Hypomethylation of IGF2 CpG 1*, 2–3 and 5** after periconceptional exposure compared to sibling controls
		Sibling controls N = 60, ♀ = 32, mean age 57
		Group 2					Group 2
		Prenatal exposure 3rd trim N = 62, ♀ = 34, mean age 59					No significant differences for IGF2
		Sibling controls N = 62, ♀ = 34, mean age 57					No significant differences for IGF2

Open in a new tab

*^aChinese Famine: 1959–1961; *^bDutch Famine: 1944–1945; *^cBangladesh Famine: 1974–1975; ^dsample overlap between [72, 74], and between [29, 61, 66, 70]; n.s. not stated; ABCA1 ATP-binding cassette subfamily A member 1, AGTR1 Angiotensin II Receptor Type 1, AKAP12 A-kinase anchoring protein 12, APOC1 Apolipoprotein C1, ATP5B ATP synthase subunit beta, BOLA bolA family member, CDH23 Cadherin-related 23, CPT1A Carnitine palmitoyltransferase 1A, CRH Corticotropin-releasing hormone, CTCF CCCTC-Binding Factor, DMR differentially methylated region, EXD3 Exonuclease 3′-5′ domain-containing 3, FTO Alpha-ketoglutarate-dependent dioxygenase, GR 1-C Glucocorticoid receptor, GNASA/B G protein alpha S, GNASAS GNAS antisense RNA, GRB10 Growth factor receptor-bound protein 10, HLA-DQB2 Histocompatibility complex Class 2 DQ Beta 2, IGF2 Insulin-like growth factor 2, IGF2R Insulin-like growth factor 2 receptor, IL-10 Interleukin-10, INSIGF Insulin-induced gene, INSR Insulin receptor, KCNQ1OT1 KCNQ1 opposite strand/antisense transcript 1, KLF13 Kruppel-like factor 13, LEP Leptin, LPL Lipoprotein lipase, LRRC14B Leucine rich repeat containing 14B, MEG3 Maternally Expressed 3, NR3C1 Nuclear receptor subfamily 3 group C member 1, 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, PPARy Peroxisome proliferator-activated receptor gamma, PRDM9 PR/SET domain 9, PRKCA Protein Kinase C Alpha, RBM46 RNA-binding motif protein 46, RFTN1 Raftlin lipid raft linker 1, SMAD7 SMAD family member 7, SPG20 Spartin gene, TNF Tumor necrosis factor, VTRNA2-1 Vault RNA 2–1, ZFP57 Zinc-finger transcription factor 57, ZFYVE28 Zinc finger FYVE-type containing 28, ZNF678 Zinc-finger protein 678; *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001

Table 4.

Effects of prenatal exposure to famine on genome-wide DNA methylation and mental disorders

References	Cohort	Sample description of groups with prenatal or non-exposure	Assessment of symptoms of sychopathology	Chromosome	Gene	No CpG	DNA methylation analysis from blood	Main results
Boks et al. [75]	Chinese Famine*^a	Prenatally exposed controls N = 25, ♀ = 15, mean age 50	Non-standardized diagnosis accordingto DSM IV criteria	chr6: 291687–293285	DUSP22	10	Infinium HumanMethylation 450BeadChip	Hypermethylation of DUSP22** in prenatal exposed SZ patients compared to all other groups
		Prenatally exposed SZ patients N = 23, ♀ = 5, mean age 50
		Non-exposed controls N = 54, ♀ = 33, mean age 47
		Non-exposed SZ patients N = 51, ♀ = 23, mean age 47

Open in a new tab

*^aChinese Famine: 1959–1961; DUSP22 Dual Specificity Phosphatase 22; DSM diagnostic and statistical manual of mental disorders, SZ schizophrenia; **p ≤ 0.01

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 [39–43] 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, 47–50]. 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 — 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 [58–66]. 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 [81–87]. 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 [94–96], 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 [102–104]. 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 [109–112]. The IL-10 gene, an anti-inflammatory cytokine of the immune system, influences the HPA axis activity [112–114] 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 [115–117]. 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 [127–129], and may inform the development of nutritional intervention programs for pregnant women affected by famine.

Supplementary Information

13148_2023_1557_MOESM1_ESM.doc^{(69KB, doc)}

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].

13148_2023_1557_MOESM2_ESM.xls^{(38.5KB, xls)}

Additional file 2: Table S4. Risk of bias assessment for the effect of famine on symptoms of psychopathology/mental disorders, and DNA methylation.

Acknowledgements

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

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

Author contributions

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.

Funding

Not applicable.

Availability of data and materials

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

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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;216:85–89. doi: 10.1192/bjp.2019.190. [DOI] [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;55:709–716. doi: 10.1016/j.jaac.2016.05.014. [DOI] [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;15:92–99. doi: 10.1111/bdi.12015. [DOI] [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. doi: 10.1016/j.neubiorev.2014.11.013. [DOI] [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. doi: 10.1016/j.neubiorev.2017.05.016. [DOI] [PubMed] [Google Scholar]
6.Linnér A, Almgren M. Epigenetic programming—the important first 1000 days. Acta Paediatr Int J Paediatr. 2020;109:443–452. doi: 10.1111/apa.15050. [DOI] [PubMed] [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;47:1910–1937. doi: 10.1093/ije/dyy153. [DOI] [PMC free article] [PubMed] [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. doi: 10.4161/epi.3.2.6034. [DOI] [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. doi: 10.1186/s13148-017-0376-9. [DOI] [PMC free article] [PubMed] [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. doi: 10.1186/s13148-019-0683-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Siegfried Z, Simon I. DNA methylation and gene expression. Wiley Interdiscip Rev Syst Biol Med. 2010;2:362–371. doi: 10.1002/wsbm.64. [DOI] [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. doi: 10.3389/fgene.2018.00640. [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Klose RJ, Bird AP. Genomic DNA methylation: the mark and its mediators. Trends Biochem Sci. 2006;31:89–97. doi: 10.1016/j.tibs.2005.12.008. [DOI] [PubMed] [Google Scholar]
14.Handel AE, Ebers GC, Ramagopalan SV. Epigenetics: molecular mechanisms and implications for disease. Trends Mol Med. 2010;16:7–16. doi: 10.1016/j.molmed.2009.11.003. [DOI] [PubMed] [Google Scholar]
15.Razin A, Cedar H. DNA methylation and gene expression. Microbiol Rev. 1991;55:451–458. doi: 10.1128/mr.55.3.451-458.1991. [DOI] [PMC free article] [PubMed] [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. doi: 10.1080/15592294.2015.1088630. [DOI] [PMC free article] [PubMed] [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;70:1177–1185. doi: 10.1007/s12031-020-01525-8. [DOI] [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;12:750–755. doi: 10.1037/tra0000574. [DOI] [PubMed] [Google Scholar]
19.Klengel T, Pape J, Binder EB, Mehta D. The role of DNA methylation in stress-related psychiatric disorders. Neuropharmacology. 2014;80:115–132. doi: 10.1016/j.neuropharm.2014.01.013. [DOI] [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;265:341–348. doi: 10.1016/j.psychres.2018.04.064. [DOI] [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–T144. doi: 10.1530/JOE-18-0683. [DOI] [PubMed] [Google Scholar]
22.Thapar A, Rutter M. Do natural experiments have an important future in the study of mental disorders? Psychol Med. 2019;49:1079–1088. doi: 10.1017/S0033291718003896. [DOI] [PMC free article] [PubMed] [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. doi: 10.1136/bmjopen-2020-042078. [DOI] [PMC free article] [PubMed] [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;3:227–240. doi: 10.26502/jppd.2572-519X0077. [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Szyf M. The early life environment and the epigenome. Biochim Biophys Acta Gen Subj. 2009;1790:878–885. doi: 10.1016/j.bbagen.2009.01.009. [DOI] [PubMed] [Google Scholar]
26.Perera F, Herbstman J. Prenatal environmental exposures, epigenetics, and disease. Reprod Toxicol. 2011;31:363–373. doi: 10.1016/j.reprotox.2010.12.055. [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Waterland RA, Michels KB. Epigenetic epidemiology of the developmental origins hypothesis. Annu Rev Nutr. 2007;27:363–388. doi: 10.1146/annurev.nutr.27.061406.093705. [DOI] [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. doi: 10.1111/jcpp.12589. [DOI] [PMC free article] [PubMed] [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;105:17046–17049. doi: 10.1073/pnas.0806560105. [DOI] [PMC free article] [PubMed] [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. doi: 10.1016/j.ijsu.2021.105906. [DOI] [PubMed] [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;46:1157–1170. doi: 10.1093/ije/dyx013. [DOI] [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;56:455–463. doi: 10.1111/j.0006-341x.2000.00455.x. [DOI] [PubMed] [Google Scholar]
35.St. Clair D, He L. Rates of adult schizophrenia following of 1959–1961. JAMA. 2005;294:557–562. doi: 10.1001/jama.294.5.557. [DOI] [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;35:568–576. doi: 10.1093/schbul/sbn168. [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Li Y, Sunder N. What doesn’t kill her, will make her depressed. Econ Hum Biol. 2021;43:101064. doi: 10.1016/j.ehb.2021.101064. [DOI] [PubMed] [Google Scholar]
38.van den Broek T, Fleischmann M. Prenatal famine exposure and mental health in later midlife. Aging Ment Heal. 2019;23:166–170. doi: 10.1080/13607863.2017.1402293. [DOI] [PubMed] [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;23:952–957. doi: 10.1080/13548506.2018.1434314. [DOI] [PubMed] [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;213:579–586. doi: 10.1192/bjp.2018.116. [DOI] [PMC free article] [PubMed] [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. doi: 10.1097/EDE.0b013e3181b5f227. [DOI] [PMC free article] [PubMed] [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. doi: 10.1186/s12877-022-02990-z. [DOI] [PMC free article] [PubMed] [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;274:223–228. doi: 10.1016/j.jad.2020.05.082. [DOI] [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;2:136–143. doi: 10.1017/S2040174411000055. [DOI] [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;173:460–471. doi: 10.1016/j.neuroimage.2017.10.047. [DOI] [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;97:259–266. doi: 10.1016/j.socscimed.2012.09.051. [DOI] [PMC free article] [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;184:21–25. doi: 10.1016/j.schres.2016.11.030. [DOI] [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. doi: 10.1001/archpsyc.1996.01830010027005. [DOI] [PubMed] [Google Scholar]
49.Susser ES, Shang P. Schizophrenia after prenatal exposure to the dutch hunger winter of 1944–1945. Arch Gen Psychiatry. 1992;49:983–988. doi: 10.1001/archpsyc.1992.01820120071010. [DOI] [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;51:42–47. doi: 10.1016/j.eurpsy.2018.01.003. [DOI] [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;68:1315–1321. doi: 10.1016/j.socscimed.2009.01.027. [DOI] [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;157:190–195. doi: 10.1176/appi.ajp.157.2.190. [DOI] [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;166:601–606. doi: 10.1192/bjp.166.5.601. [DOI] [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;282:455–462. doi: 10.1001/jama.282.5.455. [DOI] [PubMed] [Google Scholar]
55.Hoek HW, Lumey LH, Buck KA, Gorman JM. Schizoid personality disorder after prenatal exposure to famine. Am J Psychiatry. 1996;153:1637–1639. doi: 10.1176/ajp.153.12.1637. [DOI] [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;103:433–438. doi: 10.1111/j.1360-0443.2007.02084.x. [DOI] [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;1:21–26. [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. doi: 10.1186/s13148-019-0680-7. [DOI] [PMC free article] [PubMed] [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. doi: 10.1136/bmjopen-2016-011768. [DOI] [PMC free article] [PubMed] [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;44:1211–1223. doi: 10.1093/ije/dyv043. [DOI] [PMC free article] [PubMed] [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. doi: 10.1038/ncomms6592. [DOI] [PMC free article] [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;41:116–123. doi: 10.1093/ije/dyr137. [DOI] [PMC free article] [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;12:12206–12221. doi: 10.18632/aging.103397. [DOI] [PMC free article] [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. doi: 10.1126/sciadv.aao4364. [DOI] [PMC free article] [PubMed] [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;113:3907–3918. doi: 10.1016/j.ygeno.2021.09.018. [DOI] [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. doi: 10.1371/journal.pone.0037933. [DOI] [PMC free article] [PubMed] [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. doi: 10.4161/epi.6.6.16196. [DOI] [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. doi: 10.1186/1756-8935-6-4. [DOI] [PMC free article] [PubMed] [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. doi: 10.1186/s13059-016-1066-1. [DOI] [PMC free article] [PubMed] [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;18:4046–4053. doi: 10.1093/hmg/ddp353. [DOI] [PMC free article] [PubMed] [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. doi: 10.1111/1471-0528.15908. [DOI] [PubMed] [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. doi: 10.1038/s41598-020-69120-w. [DOI] [PMC free article] [PubMed] [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;3:450–457. doi: 10.1017/S2040174412000396. [DOI] [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. doi: 10.1038/s41598-019-38596-6. [DOI] [PMC free article] [PubMed] [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. doi: 10.1038/s41537-018-0058-4. [DOI] [PMC free article] [PubMed] [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. doi: 10.1038/s41398-022-02125-3. [DOI] [PMC free article] [PubMed] [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;215:730–735. doi: 10.1192/bjp.2019.114. [DOI] [PMC free article] [PubMed] [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;199:163–167. doi: 10.1016/j.schres.2018.04.024. [DOI] [PubMed] [Google Scholar]
79.Brown AS. The environment and susceptibility to schizophrenia. Prog Neurobiol. 2011;93:23–58. doi: 10.1016/j.pneurobio.2010.09.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
80.Weinberger DR. Implications of normal brain development for the pathogenesis of schizophrenia. Arch Gen Psychiatry. 1987;44:660–669. doi: 10.1001/archpsyc.1987.01800190080012. [DOI] [PubMed] [Google Scholar]
81.Murray RM, Lewis S. Is schizophrenia a neurodevelopmental disorder? Br Med J (Clin Res Ed) 1987;295:681. doi: 10.1136/bmj.295.6600.681. [DOI] [PMC free article] [PubMed] [Google Scholar]
82.Owen MJ, O’Donovan MC, Thapar A, Craddock N. Neurodevelopmental hypothesis of schizophrenia. Br J Psychiatry. 2011;198:173–175. doi: 10.1192/bjp.bp.110.084384. [DOI] [PMC free article] [PubMed] [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. doi: 10.1080/07853890310010005. [DOI] [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;37:831–845. doi: 10.1016/j.neubiorev.2013.03.020. [DOI] [PubMed] [Google Scholar]
85.Bassett AS, Chow EWC, Oneill S, Brzustowicz LM. Genetic insights into the neurodevelopmental origins of schizophrenia. Schizophr Bull. 2001;17:417–430. doi: 10.1093/oxfordjournals.schbul.a006884. [DOI] [PubMed] [Google Scholar]
86.Weinberger DR. Future of days past: neurodevelopment and schizophrenia. Schizophr Bull. 2017;43:1164–1168. doi: 10.1093/schbul/sbx118. [DOI] [PMC free article] [PubMed] [Google Scholar]
87.Fatemi SH, Folsom TD. The neurodevelopmental hypothesis of Schizophrenia, revisited. Schizophr Bull. 2009;35:528–548. doi: 10.1093/schbul/sbn187. [DOI] [PMC free article] [PubMed] [Google Scholar]
88.Reik W, Dean W, Walter J. Epigenetic reprogramming in mammalian development. Science. 2001;293:1089–1093. doi: 10.1126/science.1063443. [DOI] [PubMed] [Google Scholar]
89.Owen MJ, Sawa A, Mortensen PB. Schizophrenia. Lancet. 2016;388:86–97. doi: 10.1016/S0140-6736(15)01121-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
90.Owen MJ, O’Donovan MC. Schizophrenia and the neurodevelopmental continuum: evidence from genomics. World Psychiatry. 2017;16:227–235. doi: 10.1002/wps.20440. [DOI] [PMC free article] [PubMed] [Google Scholar]
91.Szyf M, McGowan PO, Meaney MJ. The social environment and the epigenome. Environ Mol Mutagen. 2008;49:46–60. doi: 10.1002/em.20357. [DOI] [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. doi: 10.1186/s13148-019-0676-3. [DOI] [PMC free article] [PubMed] [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. doi: 10.1016/j.pneurobio.2020.101906. [DOI] [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;7:1125–1132. doi: 10.4161/epi.21855. [DOI] [PMC free article] [PubMed] [Google Scholar]
95.Chao W, D’Amore PA. IGF2: Epigenetic regulation and role in development and disease. Cytokine Growth Factor Rev. 2008;19:111–120. doi: 10.1016/j.cytogfr.2008.01.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
96.O’Kusky J, Ye P. Neurodevelopmental effects of insulin-like growth factor signaling. Front Neuroendocrinol. 2012;33:230–251. doi: 10.1016/j.yfrne.2012.06.002. [DOI] [PMC free article] [PubMed] [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;9:215–222. doi: 10.1017/S2040174417000800. [DOI] [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;6:171–176. doi: 10.4161/epi.6.2.13516. [DOI] [PMC free article] [PubMed] [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. doi: 10.1016/j.neures.2018.10.012. [DOI] [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. doi: 10.1016/bs.pmbts.2018.01.006. [DOI] [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. doi: 10.1016/j.jocn.2017.05.022. [DOI] [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;46:1235–1238. doi: 10.2337/diab.46.7.1235. [DOI] [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. doi: 10.1016/j.ygcen.2012.01.009. [DOI] [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;9:553–559. [Google Scholar]
105.Tate PH, Bird AP. Effects of DNA methylation on DNA-binding proteins and gene expression. Curr Opin Genet Dev. 1993;3:226–231. doi: 10.1016/0959-437x(93)90027-m. [DOI] [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. doi: 10.3389/fpsyt.2022.793910. [DOI] [PMC free article] [PubMed] [Google Scholar]
107.Pariante CM, Lightman SL. The HPA axis in major depression: classical theories and new developments. Trends Neurosci. 2008;31:464–468. doi: 10.1016/j.tins.2008.06.006. [DOI] [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. doi: 10.3390/brainsci11101298. [DOI] [PMC free article] [PubMed] [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;27:161–170. doi: 10.1007/s10571-006-9137-6. [DOI] [PMC free article] [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;100:140–148. doi: 10.1016/s0165-5728(99)00206-4. [DOI] [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. doi: 10.1152/physrev.1999.79.1.1. [DOI] [PubMed] [Google Scholar]
112.Tsigos C, Chrousos GP. Hypothalamic-pituitary-adrenal axis, neuroendocrine factors and stress. J Psychosom Res. 2002;53:865–871. doi: 10.1016/s0022-3999(02)00429-4. [DOI] [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;49:841–849. doi: 10.1007/s00127-014-0887-z. [DOI] [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. doi: 10.1111/j.1749-6632.2012.06633.x. [DOI] [PMC free article] [PubMed] [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. doi: 10.1186/1471-244X-13-273. [DOI] [PMC free article] [PubMed] [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;29:384–396. doi: 10.1016/j.euroneuro.2018.12.011. [DOI] [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;192:247–254. doi: 10.1016/j.schres.2017.04.043. [DOI] [PMC free article] [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. doi: 10.1002/ajmg.b.32278. [DOI] [PMC free article] [PubMed] [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. doi: 10.1038/s41398-023-02370-0. [DOI] [PMC free article] [PubMed] [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;42:406–414. doi: 10.1093/schbul/sbv074. [DOI] [PMC free article] [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;112:345–352. doi: 10.1016/j.neubiorev.2020.02.017. [DOI] [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. doi: 10.1111/j.1467-8624.2012.01793.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
123.Morris TJ, Beck S. Analysis pipelines and packages for Infinium HumanMethylation450 BeadChip (450k) data. Methods. 2015;72:3–8. doi: 10.1016/j.ymeth.2014.08.011. [DOI] [PMC free article] [PubMed] [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. doi: 10.1080/15592294.2015.1132136. [DOI] [PMC free article] [PubMed] [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. doi: 10.1186/s13148-022-01351-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
126.Smil V. The great Chinese famine. BMJ. 1999;319:1619–1621. doi: 10.1136/bmj.319.7225.1619. [DOI] [PMC free article] [PubMed] [Google Scholar]
127.Szyf M. Towards a pharmacology of DNA methylation. Trends Pharmacol Sci. 2001;22:350–354. doi: 10.1016/s0165-6147(00)01713-2. [DOI] [PubMed] [Google Scholar]
128.Szyf M, Pakneshan P, Rabbani SA. DNA methylation and breast cancer. Biochem Pharmacol. 2004;68:1187–1197. doi: 10.1016/j.bcp.2004.04.030. [DOI] [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. doi: 10.3390/ijms19103026. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

13148_2023_1557_MOESM1_ESM.doc^{(69KB, doc)}

13148_2023_1557_MOESM2_ESM.xls^{(38.5KB, xls)}

Additional file 2: Table S4. Risk of bias assessment for the effect of famine on symptoms of psychopathology/mental disorders, and DNA methylation.

Data Availability Statement

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

[CR1] 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;216:85–89. doi: 10.1192/bjp.2019.190. [DOI] [PubMed] [Google Scholar]

[CR2] 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;55:709–716. doi: 10.1016/j.jaac.2016.05.014. [DOI] [PubMed] [Google Scholar]

[CR3] 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;15:92–99. doi: 10.1111/bdi.12015. [DOI] [PubMed] [Google Scholar]

[CR4] 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. doi: 10.1016/j.neubiorev.2014.11.013. [DOI] [PubMed] [Google Scholar]

[CR5] 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. doi: 10.1016/j.neubiorev.2017.05.016. [DOI] [PubMed] [Google Scholar]

[CR6] 6.Linnér A, Almgren M. Epigenetic programming—the important first 1000 days. Acta Paediatr Int J Paediatr. 2020;109:443–452. doi: 10.1111/apa.15050. [DOI] [PubMed] [Google Scholar]

[CR7] 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;47:1910–1937. doi: 10.1093/ije/dyy153. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR8] 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. doi: 10.4161/epi.3.2.6034. [DOI] [PubMed] [Google Scholar]

[CR9] 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. doi: 10.1186/s13148-017-0376-9. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR10] 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. doi: 10.1186/s13148-019-0683-4. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR11] 11.Siegfried Z, Simon I. DNA methylation and gene expression. Wiley Interdiscip Rev Syst Biol Med. 2010;2:362–371. doi: 10.1002/wsbm.64. [DOI] [PubMed] [Google Scholar]

[CR12] 12.Kumar S, Chinnusamy V, Mohapatra T. Epigenetics of modified DNA bases: 5-methylcytosine and beyond. Front Genet. 2018;9:1–14. doi: 10.3389/fgene.2018.00640. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR13] 13.Klose RJ, Bird AP. Genomic DNA methylation: the mark and its mediators. Trends Biochem Sci. 2006;31:89–97. doi: 10.1016/j.tibs.2005.12.008. [DOI] [PubMed] [Google Scholar]

[CR14] 14.Handel AE, Ebers GC, Ramagopalan SV. Epigenetics: molecular mechanisms and implications for disease. Trends Mol Med. 2010;16:7–16. doi: 10.1016/j.molmed.2009.11.003. [DOI] [PubMed] [Google Scholar]

[CR15] 15.Razin A, Cedar H. DNA methylation and gene expression. Microbiol Rev. 1991;55:451–458. doi: 10.1128/mr.55.3.451-458.1991. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR16] 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. doi: 10.1080/15592294.2015.1088630. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR17] 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;70:1177–1185. doi: 10.1007/s12031-020-01525-8. [DOI] [PubMed] [Google Scholar]

[CR18] 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;12:750–755. doi: 10.1037/tra0000574. [DOI] [PubMed] [Google Scholar]

[CR19] 19.Klengel T, Pape J, Binder EB, Mehta D. The role of DNA methylation in stress-related psychiatric disorders. Neuropharmacology. 2014;80:115–132. doi: 10.1016/j.neuropharm.2014.01.013. [DOI] [PubMed] [Google Scholar]

[CR20] 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;265:341–348. doi: 10.1016/j.psychres.2018.04.064. [DOI] [PubMed] [Google Scholar]

[CR21] 21.Roseboom TJ. Epidemiological evidence for the developmental origins of health and disease: effects of prenatal undernutrition in humans. J Endocrinol. 2019;242:T135–T144. doi: 10.1530/JOE-18-0683. [DOI] [PubMed] [Google Scholar]

[CR22] 22.Thapar A, Rutter M. Do natural experiments have an important future in the study of mental disorders? Psychol Med. 2019;49:1079–1088. doi: 10.1017/S0033291718003896. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR23] 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. doi: 10.1136/bmjopen-2020-042078. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR24] 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;3:227–240. doi: 10.26502/jppd.2572-519X0077. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR25] 25.Szyf M. The early life environment and the epigenome. Biochim Biophys Acta Gen Subj. 2009;1790:878–885. doi: 10.1016/j.bbagen.2009.01.009. [DOI] [PubMed] [Google Scholar]

[CR26] 26.Perera F, Herbstman J. Prenatal environmental exposures, epigenetics, and disease. Reprod Toxicol. 2011;31:363–373. doi: 10.1016/j.reprotox.2010.12.055. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR27] 27.Waterland RA, Michels KB. Epigenetic epidemiology of the developmental origins hypothesis. Annu Rev Nutr. 2007;27:363–388. doi: 10.1146/annurev.nutr.27.061406.093705. [DOI] [PubMed] [Google Scholar]

[CR28] 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. doi: 10.1111/jcpp.12589. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR29] 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;105:17046–17049. doi: 10.1073/pnas.0806560105. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR30] 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. doi: 10.1016/j.ijsu.2021.105906. [DOI] [PubMed] [Google Scholar]

[CR31] 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;46:1157–1170. doi: 10.1093/ije/dyx013. [DOI] [PubMed] [Google Scholar]

[CR32] 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]

[CR33] 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

[CR34] 34.Duval S, Tweedie R. Trim and fill: a simple funnel-plot-based method. Biometrics. 2000;56:455–463. doi: 10.1111/j.0006-341x.2000.00455.x. [DOI] [PubMed] [Google Scholar]

[CR35] 35.St. Clair D, He L. Rates of adult schizophrenia following of 1959–1961. JAMA. 2005;294:557–562. doi: 10.1001/jama.294.5.557. [DOI] [PubMed] [Google Scholar]

[CR36] 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;35:568–576. doi: 10.1093/schbul/sbn168. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR37] 37.Li Y, Sunder N. What doesn’t kill her, will make her depressed. Econ Hum Biol. 2021;43:101064. doi: 10.1016/j.ehb.2021.101064. [DOI] [PubMed] [Google Scholar]

[CR38] 38.van den Broek T, Fleischmann M. Prenatal famine exposure and mental health in later midlife. Aging Ment Heal. 2019;23:166–170. doi: 10.1080/13607863.2017.1402293. [DOI] [PubMed] [Google Scholar]

[CR39] 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;23:952–957. doi: 10.1080/13548506.2018.1434314. [DOI] [PubMed] [Google Scholar]

[CR40] 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;213:579–586. doi: 10.1192/bjp.2018.116. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR41] 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. doi: 10.1097/EDE.0b013e3181b5f227. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR42] 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. doi: 10.1186/s12877-022-02990-z. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR43] 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;274:223–228. doi: 10.1016/j.jad.2020.05.082. [DOI] [PubMed] [Google Scholar]

[CR44] 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;2:136–143. doi: 10.1017/S2040174411000055. [DOI] [PubMed] [Google Scholar]

[CR45] 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;173:460–471. doi: 10.1016/j.neuroimage.2017.10.047. [DOI] [PubMed] [Google Scholar]

[CR46] 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;97:259–266. doi: 10.1016/j.socscimed.2012.09.051. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR47] 47.Wang C, Zhang Y. Schizophrenia in mid-adulthood after prenatal exposure to the Chinese Famine of 1959–1961. Schizophr Res. 2017;184:21–25. doi: 10.1016/j.schres.2016.11.030. [DOI] [PubMed] [Google Scholar]

[CR48] 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. doi: 10.1001/archpsyc.1996.01830010027005. [DOI] [PubMed] [Google Scholar]

[CR49] 49.Susser ES, Shang P. Schizophrenia after prenatal exposure to the dutch hunger winter of 1944–1945. Arch Gen Psychiatry. 1992;49:983–988. doi: 10.1001/archpsyc.1992.01820120071010. [DOI] [PubMed] [Google Scholar]

[CR50] 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;51:42–47. doi: 10.1016/j.eurpsy.2018.01.003. [DOI] [PubMed] [Google Scholar]

[CR51] 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;68:1315–1321. doi: 10.1016/j.socscimed.2009.01.027. [DOI] [PubMed] [Google Scholar]

[CR52] 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;157:190–195. doi: 10.1176/appi.ajp.157.2.190. [DOI] [PubMed] [Google Scholar]

[CR53] 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;166:601–606. doi: 10.1192/bjp.166.5.601. [DOI] [PubMed] [Google Scholar]

[CR54] 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;282:455–462. doi: 10.1001/jama.282.5.455. [DOI] [PubMed] [Google Scholar]

[CR55] 55.Hoek HW, Lumey LH, Buck KA, Gorman JM. Schizoid personality disorder after prenatal exposure to famine. Am J Psychiatry. 1996;153:1637–1639. doi: 10.1176/ajp.153.12.1637. [DOI] [PubMed] [Google Scholar]

[CR56] 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;103:433–438. doi: 10.1111/j.1360-0443.2007.02084.x. [DOI] [PubMed] [Google Scholar]

[CR57] 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;1:21–26. [Google Scholar]

[CR58] 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. doi: 10.1186/s13148-019-0680-7. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR59] 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. doi: 10.1136/bmjopen-2016-011768. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR60] 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;44:1211–1223. doi: 10.1093/ije/dyv043. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR61] 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. doi: 10.1038/ncomms6592. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR62] 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;41:116–123. doi: 10.1093/ije/dyr137. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR63] 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;12:12206–12221. doi: 10.18632/aging.103397. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR64] 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. doi: 10.1126/sciadv.aao4364. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR65] 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;113:3907–3918. doi: 10.1016/j.ygeno.2021.09.018. [DOI] [PubMed] [Google Scholar]

[CR66] 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. doi: 10.1371/journal.pone.0037933. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR67] 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. doi: 10.4161/epi.6.6.16196. [DOI] [PubMed] [Google Scholar]

[CR68] 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. doi: 10.1186/1756-8935-6-4. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR69] 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. doi: 10.1186/s13059-016-1066-1. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR70] 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;18:4046–4053. doi: 10.1093/hmg/ddp353. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR71] 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. doi: 10.1111/1471-0528.15908. [DOI] [PubMed] [Google Scholar]

[CR72] 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. doi: 10.1038/s41598-020-69120-w. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR73] 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;3:450–457. doi: 10.1017/S2040174412000396. [DOI] [PubMed] [Google Scholar]

[CR74] 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. doi: 10.1038/s41598-019-38596-6. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR75] 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. doi: 10.1038/s41537-018-0058-4. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR76] 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. doi: 10.1038/s41398-022-02125-3. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR77] 77.Guo C, He P, Song X, Zheng X. Long-term effects of prenatal exposure to earthquake on adult schizophrenia. Br J Psychiatry. 2019;215:730–735. doi: 10.1192/bjp.2019.114. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR78] 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;199:163–167. doi: 10.1016/j.schres.2018.04.024. [DOI] [PubMed] [Google Scholar]

[CR79] 79.Brown AS. The environment and susceptibility to schizophrenia. Prog Neurobiol. 2011;93:23–58. doi: 10.1016/j.pneurobio.2010.09.003. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR80] 80.Weinberger DR. Implications of normal brain development for the pathogenesis of schizophrenia. Arch Gen Psychiatry. 1987;44:660–669. doi: 10.1001/archpsyc.1987.01800190080012. [DOI] [PubMed] [Google Scholar]

[CR81] 81.Murray RM, Lewis S. Is schizophrenia a neurodevelopmental disorder? Br Med J (Clin Res Ed) 1987;295:681. doi: 10.1136/bmj.295.6600.681. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR82] 82.Owen MJ, O’Donovan MC, Thapar A, Craddock N. Neurodevelopmental hypothesis of schizophrenia. Br J Psychiatry. 2011;198:173–175. doi: 10.1192/bjp.bp.110.084384. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR83] 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. doi: 10.1080/07853890310010005. [DOI] [PubMed] [Google Scholar]

[CR84] 84.Kofink D, Boks MPM, Timmers HTM, Kas MJ. Epigenetic dynamics in psychiatric disorders: environmental programming of neurodevelopmental processes. Neurosci Biobehav Rev. 2013;37:831–845. doi: 10.1016/j.neubiorev.2013.03.020. [DOI] [PubMed] [Google Scholar]

[CR85] 85.Bassett AS, Chow EWC, Oneill S, Brzustowicz LM. Genetic insights into the neurodevelopmental origins of schizophrenia. Schizophr Bull. 2001;17:417–430. doi: 10.1093/oxfordjournals.schbul.a006884. [DOI] [PubMed] [Google Scholar]

[CR86] 86.Weinberger DR. Future of days past: neurodevelopment and schizophrenia. Schizophr Bull. 2017;43:1164–1168. doi: 10.1093/schbul/sbx118. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR87] 87.Fatemi SH, Folsom TD. The neurodevelopmental hypothesis of Schizophrenia, revisited. Schizophr Bull. 2009;35:528–548. doi: 10.1093/schbul/sbn187. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR88] 88.Reik W, Dean W, Walter J. Epigenetic reprogramming in mammalian development. Science. 2001;293:1089–1093. doi: 10.1126/science.1063443. [DOI] [PubMed] [Google Scholar]

[CR89] 89.Owen MJ, Sawa A, Mortensen PB. Schizophrenia. Lancet. 2016;388:86–97. doi: 10.1016/S0140-6736(15)01121-6. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR90] 90.Owen MJ, O’Donovan MC. Schizophrenia and the neurodevelopmental continuum: evidence from genomics. World Psychiatry. 2017;16:227–235. doi: 10.1002/wps.20440. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR91] 91.Szyf M, McGowan PO, Meaney MJ. The social environment and the epigenome. Environ Mol Mutagen. 2008;49:46–60. doi: 10.1002/em.20357. [DOI] [PubMed] [Google Scholar]

[CR92] 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. doi: 10.1186/s13148-019-0676-3. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR93] 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. doi: 10.1016/j.pneurobio.2020.101906. [DOI] [PubMed] [Google Scholar]

[CR94] 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;7:1125–1132. doi: 10.4161/epi.21855. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR95] 95.Chao W, D’Amore PA. IGF2: Epigenetic regulation and role in development and disease. Cytokine Growth Factor Rev. 2008;19:111–120. doi: 10.1016/j.cytogfr.2008.01.005. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR96] 96.O’Kusky J, Ye P. Neurodevelopmental effects of insulin-like growth factor signaling. Front Neuroendocrinol. 2012;33:230–251. doi: 10.1016/j.yfrne.2012.06.002. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR97] 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;9:215–222. doi: 10.1017/S2040174417000800. [DOI] [PubMed] [Google Scholar]

[CR98] 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;6:171–176. doi: 10.4161/epi.6.2.13516. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR99] 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. doi: 10.1016/j.neures.2018.10.012. [DOI] [PubMed] [Google Scholar]

[CR100] 100.Liu C, Jiao C, Wang K, Yuan N. DNA methylation and psychiatric disorders. Prog Mol Biol Transl Sci. 2018;157:175–232. doi: 10.1016/bs.pmbts.2018.01.006. [DOI] [PubMed] [Google Scholar]

[CR101] 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. doi: 10.1016/j.jocn.2017.05.022. [DOI] [PubMed] [Google Scholar]

[CR102] 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;46:1235–1238. doi: 10.2337/diab.46.7.1235. [DOI] [PubMed] [Google Scholar]

[CR103] 103.Roubos EW, Dahmen M, Kozicz T, Xu L. Leptin and the hypothalamo-pituitary-adrenal stress axis. Gen Comp Endocrinol. 2012;177:28–36. doi: 10.1016/j.ygcen.2012.01.009. [DOI] [PubMed] [Google Scholar]

[CR104] 104.Heiman ML, Chen Y, Caro JF. Leptin participates in the regulation of glucocorticoid and growth hormone axes. J Nutr Biochem. 1998;9:553–559. [Google Scholar]

[CR105] 105.Tate PH, Bird AP. Effects of DNA methylation on DNA-binding proteins and gene expression. Curr Opin Genet Dev. 1993;3:226–231. doi: 10.1016/0959-437x(93)90027-m. [DOI] [PubMed] [Google Scholar]

[CR106] 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. doi: 10.3389/fpsyt.2022.793910. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR107] 107.Pariante CM, Lightman SL. The HPA axis in major depression: classical theories and new developments. Trends Neurosci. 2008;31:464–468. doi: 10.1016/j.tins.2008.06.006. [DOI] [PubMed] [Google Scholar]

[CR108] 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. doi: 10.3390/brainsci11101298. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR109] 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;27:161–170. doi: 10.1007/s10571-006-9137-6. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR110] 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;100:140–148. doi: 10.1016/s0165-5728(99)00206-4. [DOI] [PubMed] [Google Scholar]

[CR111] 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. doi: 10.1152/physrev.1999.79.1.1. [DOI] [PubMed] [Google Scholar]

[CR112] 112.Tsigos C, Chrousos GP. Hypothalamic-pituitary-adrenal axis, neuroendocrine factors and stress. J Psychosom Res. 2002;53:865–871. doi: 10.1016/s0022-3999(02)00429-4. [DOI] [PubMed] [Google Scholar]

[CR113] 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;49:841–849. doi: 10.1007/s00127-014-0887-z. [DOI] [PubMed] [Google Scholar]

[CR114] 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. doi: 10.1111/j.1749-6632.2012.06633.x. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR115] 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. doi: 10.1186/1471-244X-13-273. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR116] 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;29:384–396. doi: 10.1016/j.euroneuro.2018.12.011. [DOI] [PubMed] [Google Scholar]

[CR117] 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;192:247–254. doi: 10.1016/j.schres.2017.04.043. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR118] 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. doi: 10.1002/ajmg.b.32278. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR119] 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. doi: 10.1038/s41398-023-02370-0. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR120] 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;42:406–414. doi: 10.1093/schbul/sbv074. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR121] 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;112:345–352. doi: 10.1016/j.neubiorev.2020.02.017. [DOI] [PubMed] [Google Scholar]

[CR122] 122.Szyf M, Bick J. DNA methylation: a mechanism for embedding early life experiences in the genome. Child Dev. 2013;84:49–57. doi: 10.1111/j.1467-8624.2012.01793.x. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR123] 123.Morris TJ, Beck S. Analysis pipelines and packages for Infinium HumanMethylation450 BeadChip (450k) data. Methods. 2015;72:3–8. doi: 10.1016/j.ymeth.2014.08.011. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR124] 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. doi: 10.1080/15592294.2015.1132136. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR125] 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. doi: 10.1186/s13148-022-01351-2. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR126] 126.Smil V. The great Chinese famine. BMJ. 1999;319:1619–1621. doi: 10.1136/bmj.319.7225.1619. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR127] 127.Szyf M. Towards a pharmacology of DNA methylation. Trends Pharmacol Sci. 2001;22:350–354. doi: 10.1016/s0165-6147(00)01713-2. [DOI] [PubMed] [Google Scholar]

[CR128] 128.Szyf M, Pakneshan P, Rabbani SA. DNA methylation and breast cancer. Biochem Pharmacol. 2004;68:1187–1197. doi: 10.1016/j.bcp.2004.04.030. [DOI] [PubMed] [Google Scholar]

[CR129] 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. doi: 10.3390/ijms19103026. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

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

Heike Eichenauer

Ulrike Ehlert

Abstract

Background

Methods

Results

Conclusion

Supplementary Information

Background

Methods

Search strategy

Screening and selection procedure

Data extraction

Data analysis

Results

Search results

Fig. 1.

Study characteristics

Table 1.

Table 2.

Table 3.

Table 4.

Risk of bias assessment

Effects of prenatal famine exposure on offspring symptoms/mental disorders

Fig. 2.

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

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

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

Discussion

Conclusion

Supplementary Information

Acknowledgements

Abbreviations

Author contributions

Funding

Availability of data and materials

Declarations

Ethics approval and consent to participate

Consent for publication

Competing interests

Footnotes

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases