Pre-pregnancy obesity is a major burden throughout the world, especially in developing countries (1). Obesity in increasing worldwide, especially in Asia (2) and the prevalence of obesity among women is higher than in men (3). Pre-pregnancy obesity is a predictor of adverse pregnancy outcomes, with many studies reporting that pre-pregnancy obesity is associated with higher odds of having gestational diabetes mellitus (GDM) [odd ratio (OR) =3.98]; gestational hypertension disorders (OR =3.68); preeclampsia (OR =3.20), macrosomia (OR =2.17) (4-6); preterm delivery [relative risk (RR) =1.35]; and caesarean section (RR =1.66) as compared to women with normal weight (6).
Studies have reported that pre-pregnancy obesity is associated with dietary preference, sedentary lifestyle and lack of awareness in metabolic management (7,8), however the underlying mechanism for these associated factors can influence metabolism in women still remains unclear. Genetic factors are now regarded as a highly plausible explanation for explaining the association between pre-pregnancy obesity and aforementioned associated factors (9-11) as studies have shown that genetic factors had contributed e to 40% to 70% of variation in the risk of developing obesity (9-12).
Candidate gene studies are hypothesis-driven, and numerous of genes have been tested for obesity. Evidence from studies worldwide across different populations has been used to establish a human obesity gene map (13,14). Nevertheless, interest remains in the analysis of candidate genes for the reason that certain candidate genes may have overlapping functions across various traits and diseases (15). To this end, we address this issue for obesity-susceptibility by constructing a custom of single nucleotide polymorphism (SNP) array containing thirteen candidate genes that were previously tested and found to have an association with either obesity or psychiatric symptoms.
This custom SNPs provides excellent coverage of many previously tested neuroendocrine disorder-related candidate genes for obesity, including brain-derived neurotrophic factor (BDNF) (16,17), FKBP5 (18), NPY5R (19), EPHX2 (20) and TPH2 (21). In contrast, genetic association studies of obesity with the following neuroendocrine disorder-related candidate genes, such as ANO2 (22), HTR2C (23), LHPP (24), NRG1 (25), OXTR (26), RORA (27), SDK2 (22), TEX51 (22) and PLEKHG1 (22) have not been evaluated. It is well known that obesity is closely related to psychiatry symptoms, since a large proportion of individuals with psychiatric symptoms such as depression or anxiety also tend to be obese (28-30); Similarly, those who are obese are at higher risk of developing depression or anxiety symptoms (28,31,32). In addition, there is increasing support for the notion that obesity is a neuroendocrine disorder in which increased leptin, insulin, glucose, triglycerides, and inflammatory cytokines lead to alterations in hypothalamic pituitary adrenal axis, serotonergic and dopaminergic system, increasing the risk of behavioural and mental health disorders (33-35). Thus, the relevance of neuroendocrine disorders-related candidate genes in predisposal for pre-pregnancy obesity is worth investigating.
The aim of the present study was to perform neuroendocrine disorder-related candidate gene analysis via mass array to evaluate the association between pre-pregnancy obesity and thirteen candidate genes adjusted for socio-demographical background, maternal and clinical profile among GDM women using a stratification approach. The association analysis between the candidate genes and pre-pregnancy obesity was as defined by Asian and International criteria-based body mass index (BMI) groups and independently analysed. We present the following article in accordance with the STREGA reporting checklist (available at http://dx.doi.org/10.21037/atm-20-1579) (36).
We performed a post-hoc case-control analysis of a cross-sectional study among GDM women (n=312) to check for candidate SNPs that may be associated with obesity in this particular population according to the Asian and International criteria-based BMI.
The study participants were women with GDM who were enrolled for a cross-sectional study (37). All participants were native Malaysian with GDM and residents of surrounding areas. They were recruited during second or third trimester care at two tertiary hospitals in Klang Valley, Malaysia between 1st June 2018 and 31st October 2018. The inclusion criteria were previously described in the study by Lee et al., 2019 (37). In brief, the participant must be a Malaysian woman, pregnant, 18 years of age or older and with a diagnosis of GDM according to Malaysian Clinical Practice Guidelines (38,39).
Socio-demographic background and clinical characteristics
Socio-demographic backgrounds and clinical characteristics were recorded at enrollment to obtain information related to maternal profile, past-obstetrics history, concurrent medical problems, family history and psychiatric symptoms (including depression, anxiety and stress). These data were obtained from the self-administered questionnaire and medical records.
Measurement of pre-pregnancy obesity
The anthropometric data of participants were obtained from each mother’s health records. Pre-pregnancy weight and height were self-reported by the pregnant mothers and recorded by a medical assistant during the first antenatal booking. Pre-pregnancy obesity is defined as women with a BMI ≥30 kg/m2 before the pregnancy visit by using the international BMI classification (40). It is calculated by dividing weight at pre-pregnancy weight in kilograms (kg) by height in meters squared (m2) (41). BMI it is used to estimate the total body fat and assesses the risk for diseases related to increased body fat. The WHO criteria for International criteria-based BMI classifies a BMI of <18.5 kg/m2 as underweight; 18.5–24.9 kg/m2 (as normal); 25.0–29.9 kg/m2 (overweight); and >30 kg/m2 as obese (42-44).
Studies have showed that Asian people may have increased health risks at a lower BMI compared to Caucasians; therefore, the Asian criteria-based BMI was modified specifically for Asian adults. Its cut-off points are lower than those defined for International criteria. For instance, WHO recommended cut-points for Asian criteria-based BMI categories as follows: <18.5 kg/m2 (underweight); 18.5–22.9 kg/m2 (normal); 23.0–27.4 kg/m2 (overweight) and ≥27.5 (obesity) (45,46). This categorizing scheme follows National Institute for Health and Care Excellence (NICE) recommendations for Asians (47,48).
Regarding patients and controls, we analyzed the association between candidate genes and obesity using two different criteria-based BMI categories which are the Asian and International criteria based BMI categories. Participants in control group were those patients with normal weight and those overweight as defined using BMI value, while participants in the patient group were those defined as being obese. Upon completion of sample collection and analysis, data for baseline BMI and polymorphisms of candidate genes were readily available for a total of 312 participants.
Study outcomes, predictors and potential confounders
The study outcomes were association between genetic polymorphism in neuroendocrine disorder-related candidate genes and pre-pregnancy obesity. The association was presented in crude OR and adjusted OR (95% confidence interval). The predictors in this study were neuroendocrine disorder-related candidate genes. The potential confounders were socio-demographic background and clinical characteristics.
Blood sample collection, DNA extraction and Mass-array genotyping
Detailed blood sampling and DNA extraction methods have been previously described (49). In brief, 5 mL of blood samples of participants were collected by a phlebotomist and genomic DNA was isolated by using the QIAamp Blood DNA Mini Kit (QIAGEN, Hilden, Germany). The genotyping analysis for candidate genes polymorphism was conducted using the Agene® MassARRAY platform. SNP analysis performed using a Typer Analyzer.
We performed Bonferroni correction for multiple statistical significance tests to minimize bias arising from multiple testing errors.
Sample size calculation
The sample size was calculated using the following formula:
n = Za/22p^(1− p^)/e2 (50)
n = Za/22p^(1− p^)/e2
If Za/2(0.95) =1.96; p^ =0.237 and e =0.07, then the sample size is:
n =(1.96)2 (0.237) (0.763)/(0.07)2
n =3.8416 (0.1808)/0.005
n =139. Thus, around 139 obese GDM women to estimate p with 95% CI was needed.
Data on socio-demographic background, clinical characteristics and candidate genes are presented in term of N (%). Dependent variables were categorized into two groups: normal or overweight group and obese group. Data on age and monthly family income are presented in mean ± standard deviation.
We used IBM SPSS Statistics version 21.0 to perform the data analysis. A chi-square goodness-of-fit test was performed to assess the agreement of the genotype distribution among candidate genes using Hardy-Weinberg equilibrium, in which if the P value for chi-square goodness-of-fit tests is significant (P<0.05), the population is not in Hardy-Weinberg equilibrium. If the genotype distribution of candidate genes does not fit Hardy-Weinberg equilibrium based on equal distribution, the expected values for genotype distribution will be adjusted according to the global population. Univariate analysis was used to analyse the association between candidate genes and obesity among the GDM mothers. Significant difference is set at a P value <0.05. In addition, we tested the candidate gene polymorphism associations with obesity and any polymorphism adjusted for socio-demographical and clinical moderator effects. Variables with a P value of less than 0.25 in univariate analysis underwent Bonferroni correction for multiple statistical significance tests. Variables with P value of less than 0.25 after a Bonferroni adjustment were entered into the multiple logistic regression analysis (51), adjusting for the fact that a rigidly set P value at <0.05 may miss many clinically important variables (52,53). A backward stepwise regression method was used (54). All analyses were made with a 95% CI, and the level of significance was set at P<0.05.
The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by The Medical Research Ethics Committee, Ministry of Health Malaysia (No. NMRR-17-2264-37814) and informed consent was taken from all the patients.
We found that 60.9% of GDM patients were obese using the Asian criteria-based BMI higher than the percentage of GDM patients with obesity (44.2%) using the International criteria-based BMI. We found a significant association only in the association between specific SNP (rs6265) of gene BDNF and pre-pregnancy obesity using International criteria-based BMI but not in Asian criteria-based BMI.
Analyses of the socio-demographic characteristics, past obstetric history, concurrent medical problems and family history of the 312 participants as stratified by Asian and International criteria-based BMI were performed and is shown in Table 1. Among the independent variables that were investigated, a significant difference was observed only in concurrent medical problems which were asthma and anaemia after a Bonferroni adjustment in the context of family-wise error for Asian criteria-based BMI categorization among GDM women (P<0.05). Asthma (P<0.05) was the only independent variable with a significant difference after a Bonferroni adjustment in the context of family-wise error for International criteria-based BMI categorization.
Analyses of the NRG1, FKBP5, RORA, OXTR, BDNF, PLEKHG1 and HTR2C genotypes among the GDM patients with and without obesity (n=312) as stratified by Asian and International criteria-based BMI using the univariate analysis is shown in Table 2. Analyses of the LHPP, SDK2, TEX51, EPHX2, NPY5R and ANO2 genotype among GDM women with or without obesity that were stratified by Asian and International criteria-based BMI are shown in Table S1, because these candidate genes have a P value >0.25 using univariate analysis.
Notably, the proportion of the AG or AA genotypes was higher than that of the GG genotype in SNP of BDNF (G > A in rs6265) among obese GDM women (57.7% versus 42.3%; P=0.024 after a Bonferroni adjustment) as shown in Table 2. On the other hand, there were no significant associations between SNPs for candidate genes (NRG1, FKBP5, RORA, OXTR, PLEKHG1 and HTR2C) and pre-pregnancy obesity (P>0.05) in both stratification groups.
The associations between specific SNP’s genotype of candidate genes and pre-pregnancy obesity adjusted for socio-demographic characteristics and concurrent medical problems are shown in Table 3 for Asian criteria-based BMI classification, and Table 4 for International criteria-based BMI classification. GDM patients with the AA or AG genotypes in specific SNP of BDNF (G > A in rs6265) have a 2.2 times higher odds to be obese compared to those who carry GG genotype in the SNP adjusted for parity, underlying with asthma, heart disease, anaemia, education background, smoking habit and monthly family income in the International criteria-based BMI stratification group. GDM patients with underlying asthma appeared to be significantly associated with pre-pregnancy obesity in both stratification groups, with GDM patients with underlying asthma having a 5.7 times and 2.7 times higher odds to be obese compared to those without underlying asthma in Asian and International criteria-based BMI, respectively.
We performed additional analysis to determine the association, if any between candidate gene BDNF (G > A in rs6265) and psychiatric symptoms (depression, anxiety and stress symptoms). The results are presented in Table 5. The analysis showed that there was no statistically significant association between BDNF (G > A in rs6265) and psychiatric symptoms among Malaysian women with GDM.
Over the years, an increasing number of polymorphisms in candidate genes related to obesity have been discovered. In this study, we performed univariate logistic regression for every candidate gene, followed by multiple logistic regressions to elucidate the association between candidate genes and pre-pregnancy obesity among GDM patients. To our knowledge, this is the first study to examine the candidate genes for pre-pregnancy obesity among GDM women in Malaysia. It is also the first study to use stratification approach by both Asian and International criteria-based BMI in performing the association analysis for candidate genes.
It is worth mentioning that 60.9% of GDM patients in this study were obese using the Asian-criteria-based BMI, while only around two-fifth were obese using International criteria-based BMI. Even though the percentage of obesity among GDM patient using International criteria-based BMI appeared to be lower than that when using Asian-criteria-based BMI, it is noteworthy that types of criteria-based BMI used often has an influence on the association analysis between candidate genes and obesity. For instance, we found out that there were only five candidate genes with a P value <0.25 in univariate analysis that were entered multiple regressions analysis, which included candidate genes of NRG1, OXTR, BNDF, FKBP5 and PLEKHG1 using the Asian criteria-based BMI. The five candidate genes with P value <0.25 in univariate analysis entered into the multiple regressions analysis using the International criteria-based BMI were NRG1, FKBP5, RORA, BNDF and HTR2C.
In this study, BDNF was found to have an association with pre-pregnancy obesity using the International criteria-based BMI. A possible explanation is that BDNF is a type of neurotrophic protein that contributes to suppressed food intake through hippocampal signalling (55,56). Polymorphism in BNDF gene could possibly decrease BNDF expression and thus assist in promoting food intake and exhibit hyperphagic behaviour which may subsequently contributes to significant weight gain (57).
The association between BDNF rs6265 genotypes and obesity is inconsistent among populations, as shown also in this study, where the carrier of A allele is associated with obesity in GDM patients. This finding is consistent with studies done on German (58), Belgian (16) and Estonian populations (59). However, our findings contradict the findings of studies done on American (60) and British populations (61). These studies discovered that those who carry G allele exhibited higher BMI than carriers of the A allele. These inconsistent findings may be due to differences in dietary intake and lifestyle factors, which could modify the association between genotype and obesity traits.
Study strength and limitations
This study has generated exciting findings for an association between genetic variant in SNP of BDNF gene and maternal obesity, which further establishes the role of SNP of BDNF (rs6265) in obesity in women adjusted for socio-demographic characteristics and concurrent medical problems.
Limitations may also be present in our study. The association between candidate genes and pre-pregnancy obesity traits could be modulated by the gene-diet-lifestyle interactions; however information on diet intake, lifestyle factors and physical activity was not captured in this study. Therefore the association between candidate genes and pre-pregnancy obesity as shown in this study should be interpreted cautiously.
In summary, our study found a significant association between BNDF rs6265 variant and pre-pregnancy obesity among GDM patients. The BDNF genotype appears to interact with concurrent medical problems in the Malaysian population, especially among GDM patients. The results indicate a role for BDNF in obesity. Larger studies considering dietary intake and lifestyle factors are required to determine whether there is a true association between BDNF gene and obesity.
Funding: This work was supported by the Universiti Putra Malaysia under Putra Graduate Initiative (UPM/700-2/1/GP-IPS/2018/9593800), High Impact Grant (UPM/800-3/3/1/GPB/2018/9659600) and Graduate Research Fellowship (UPM/SPS/GS48750). The funder had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
Reporting Checklist: The authors have completed the STREGA reporting checklist. Available at http://dx.doi.org/10.21037/atm-20-1579
Data Sharing Statement: Available at http://dx.doi.org/10.21037/atm-20-1579
Peer Review File: Available at http://dx.doi.org/10.21037/atm-20-1579
Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/atm-20-1579). The authors have no conflicts of interest to declare.
Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by The Medical Research Ethics Committee, Ministry of Health Malaysia (No. NMRR-17-2264-37814) and informed consent was taken from all the patients.
Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.
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