Melanin-concentrating hormone receptor 1 polymorphisms are associated with components of energy balance in the Complex Diseases in the Newfoundland Population: Environment and Genetics (CODING) study

Mass spectrometry-based proteomics identify novel serum osteoarthritis biomarkers

Background

Osteoarthritis (OA) is a slowly developing and debilitating disease, and there are no validated specific biomarkers for its early detection. To improve therapeutic approaches, identification of specific molecules/biomarkers enabling early determination of this disease is needed. This study aimed at identifying, with the use of proteomics/mass spectrometry, novel OA-specific serum biomarkers. As obesity is a major risk factor for OA, we discriminated obesity-regulated proteins to target only OA-specific proteins as biomarkers.

Methods

Serum from the Osteoarthritis Initiative cohort was used and divided into 3 groups: controls (n=8), OA-obese (n=10) and OA-non-obese (n=10). Proteins were identified and quantified from the liquid chromatography–tandem mass spectrometry analyses using MaxQuant software. Statistical analysis used the Limma test followed by the Benjamini-Hochberg method. To compare the proteomic profiles, the multivariate unsupervised principal component analysis (PCA) followed by the pairwise comparison was used. To select the most predictive/discriminative features, the supervised linear classification model sparse partial least squares regression discriminant analysis (sPLS-DA) was employed. Validation of three differential proteins was performed with protein-specific assays using plasma from a cohort derived from the Newfoundland Osteoarthritis.

Results

In total, 509 proteins were identified, and 279 proteins were quantified. PCA-pairwise differential comparisons between the 3 groups revealed that 8 proteins were differentially regulated between the OA-obese and/or OA-non-obese with controls. Further experiments using the sPLS-DA revealed two components discriminating OA from controls (component 1, 9 proteins), and OA-obese from OA-non-obese (component 2, 23 proteins). Proteins from component 2 were considered related to obesity. In component 1, compared to controls, 7 proteins were significantly upregulated by both OA groups and 2 by the OA-obese. Among upregulated proteins from both OA groups, some of them alone would not be a suitable choice as specific OA biomarkers due to their rather non-specific role or their strong link to other pathological conditions. Altogether, data revealed that the protein CRTAC1 appears to be a strong OA biomarker candidate. Other potential new biomarker candidates are the proteins FBN1, VDBP, and possibly SERPINF1. Validation experiments revealed statistical differences between controls and OA for FBN1 (p=0.044) and VDPB (p=0.022), and a trend for SERPINF1 (p=0.064).

Conclusion

Our study suggests that 4 proteins, CRTAC1, FBN1, VDBP, and possibly SERPINF1, warrant further investigation as potential new biomarker candidates for the whole OA population.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13075-022-02801-1.

Interaction of Neighborhood and Genetic Risk on Waist Circumference in African-American Adults: A Longitudinal Study

BACKGROUND

Understanding determinants of metabolic risk has become a national priority given the increasingly high prevalence rate of this condition among U.S. adults.

PURPOSE

This study's aim was to assess the impact of gene-by-neighborhood social environment interactions on waist circumference (WC) as a primary marker of metabolic risk in underserved African-American adults. Based on a dual-risk model, it was hypothesized that those with the highest genetic risk and who experienced negative neighborhood environment conditions would demonstrate higher WC than those with fewer risk factors.

METHODS

This study utilized a subsample of participants from the Positive Action for Today's Health environmental intervention to improve access and safety for walking in higher-crime neighborhoods, who were willing to provide buccal swab samples for genotyping stress-related genetic pathways. Assessments were conducted with 228 African-American adults at baseline, 12, 18, and 24 months.

RESULTS

Analyses indicated three significant gene-by-environment interactions on WC outcomes within the sympathetic nervous system (SNS) genetic pathway. Two interactions supported the dual-risk hypotheses, including the SNS genetic risk-by-neighborhood social life interaction (b = -0.11, t(618) = -2.02, p = .04), and SNS genetic risk-by-informal social control interaction (b = -0.51, t(618) = -1.95, p = .05) on WC outcomes. These interactions indicated that higher genetic risk and lower social-environmental supports were associated with higher WC. There was also one significant SNS genetic risk-by-neighborhood satisfaction interaction (b = 1.48, t(618) = 2.23, p = .02) on WC that was inconsistent with the dual-risk pattern.

CONCLUSIONS

Findings indicate that neighborhood and genetic factors dually influence metabolic risk and that these relations may be complex and warrant further study.

TRIAL REGISTRATION

NCT01025726.

Endotypes of primary osteoarthritis identified by plasma metabolomics analysis

OBJECTIVE

To identify endotypes of osteoarthritis (OA) by a metabolomics analysis.

METHODS

Study participants included hip/knee OA patients and controls. Fasting plasma samples were metabolomically profiled. Common factor analysis and K-means clustering were applied to the metabolomics data to identify the endotypes of OA patients. Logistic regression was utilized to identify the most significant metabolites contributing to the endotypes. Clinical and epidemiological factors were examined in relation to the identified OA endotypes.

RESULTS

Six hundred and fifteen primary OA patients and 237 controls were included. Among the 186 metabolites measured, 162 passed the quality control analysis. The 615 OA patients were classified in three clusters (A, 66; B, 200; and C, 349). Patients in cluster A had a significantly higher concentration of butyrylcarnitine (C4) than other clusters and controls (all P < 0.0002). Elevated C4 is thought to be related to muscle weakness and wasting. Patients in cluster B had a significantly lower arginine concentration than other clusters and controls (all P < 7.98 × 10-11). Cluster C patients had a significantly lower concentration of lysophosphatidylcholine (with palmitic acid), which is a pro-inflammatory bioactive compound, than other clusters and controls (P < 3.79 × 10-6). Further, cluster A had a higher BMI and prevalence of diabetes than other clusters (all P ≤ 0.0009), and also a higher prevalence of coronary heart disease than cluster C (P = 0.04). Cluster B had a higher prevalence of coronary heart disease than cluster C (P = 0.003) whereas cluster C had a higher prevalence of osteoporosis (P = 0.009).

CONCLUSION

Our data suggest three possible clinically actionable endotypes in primary OA: muscle weakness, arginine deficit and low inflammatory OA.

Identification of Genetic Variants in 65 Obesity Related Genes in a Cohort of Portuguese Obese Individuals

Obesity is a major public health problem, which has a strong genetic component that interplays with environmental factors. Several genes are known to be implicated in the regulation of body weight. The identification of alleles that can be associated with obesity is a key element to control this pandemic. On the basis of a Portuguese population, 65 obesity-related genes are sequenced using Next-Generation Sequencing (NGS) in 72 individuals with obesity, in order to identify variants associated with monogenic obesity and potential risk factors. A total of 429 variants are identified, 129 of which had already been associated with the phenotype. Comparing our results with the European and Global frequencies, from 1000 Genomes project, 23 potential risk variants are identified. Six new variants are discovered in heterozygous carriers: four missense (genes ALMS1-NM_015120.4:c.5552C>T; SORCS1-NM_001013031.2:c.1072A>G and NM_001013031.2: c.2491A>C; TMEM67-NM_153704.5:c.158A>G) and two synonymous (genes BBS1-NM_024649.4:c.1437C>T; TMEM67-NM_153704.5:c.2583T>C). Functional studies should be performed to validate these new findings and evaluate their penetrance and pathogenicity. Regardless of no cases of monogenic obesity being identified, this kind of investigational study is important when we are still trying to understand the aetiology and pathophysiology of obesity. This will allow the identification of rare variants associated with obesity and the study of their prevalence in specific populational groups.

The genomic basis of mood instability: identification of 46 loci in 363,705 UK Biobank participants, genetic correlation with psychiatric disorders, and association with gene expression and function

Genome-wide association studies (GWAS) of psychiatric phenotypes have tended to focus on categorical diagnoses, but to understand the biology of mental illness it may be more useful to study traits which cut across traditional boundaries. Here, we report the results of a GWAS of mood instability as a trait in a large population cohort (UK Biobank, n = 363,705). We also assess the clinical and biological relevance of the findings, including whether genetic associations show enrichment for nervous system pathways. Forty six unique loci associated with mood instability were identified with a SNP heritability estimate of 9%. Linkage Disequilibrium Score Regression (LDSR) analyses identified genetic correlations with Major Depressive Disorder (MDD), Bipolar Disorder (BD), Schizophrenia, anxiety, and Post Traumatic Stress Disorder (PTSD). Gene-level and gene set analyses identified 244 significant genes and 6 enriched gene sets. Tissue expression analysis of the SNP-level data found enrichment in multiple brain regions, and eQTL analyses highlighted an inversion on chromosome 17 plus two brain-specific eQTLs. In addition, we used a Phenotype Linkage Network (PLN) analysis and community analysis to assess for enrichment of nervous system gene sets using mouse orthologue databases. The PLN analysis found enrichment in nervous system PLNs for a community containing serotonin and melatonin receptors. In summary, this work has identified novel loci, tissues and gene sets contributing to mood instability. These findings may be relevant for the identification of novel trans-diagnostic drug targets and could help to inform future stratified medicine innovations in mental health.

A classification modeling approach for determining metabolite signatures in osteoarthritis

Multiple factors can help predict knee osteoarthritis (OA) patients from healthy individuals, including age, sex, and BMI, and possibly metabolite levels. Using plasma from individuals with primary OA undergoing total knee replacement and healthy volunteers, we measured lysophosphatidylcholine (lysoPC) and phosphatidylcholine (PC) analogues by metabolomics. Populations were stratified on demographic factors and lysoPC and PC analogue signatures were determined by univariate receiver-operator curve (AUC) analysis. Using signatures, multivariate classification modeling was performed using various algorithms to select the most consistent method as measured by AUC differences between resampled training and test sets. Lists of metabolites indicative of OA [AUC > 0.5] were identified for each stratum. The signature from males age > 50 years old encompassed the majority of identified metabolites, suggesting lysoPCs and PCs are dominant indicators of OA in older males. Principal component regression with logistic regression was the most consistent multivariate classification algorithm tested. Using this algorithm, classification of older males had fair power to classify OA patients from healthy individuals. Thus, individual levels of lysoPC and PC analogues may be indicative of individuals with OA in older populations, particularly males. Our metabolite signature modeling method is likely to increase classification power in validation cohorts.

An evolutionary learning and network approach to identifying key metabolites for osteoarthritis

Metabolomics studies use quantitative analyses of metabolites from body fluids or tissues in order to investigate a sequence of cellular processes and biological systems in response to genetic and environmental influences. This promises an immense potential for a better understanding of the pathogenesis of complex diseases. Most conventional metabolomics analysis methods exam one metabolite at a time and may overlook the synergistic effect of combining multiple metabolites. In this article, we proposed a new bioinformatics framework that infers the non-linear synergy among multiple metabolites using a symbolic model and subsequently, identify key metabolites using network analysis. Such a symbolic model is able to represent a complex non-linear relationship among a set of metabolites associated with osteoarthritis (OA) and is automatically learned using an evolutionary algorithm. Applied to the Newfoundland Osteoarthritis Study (NFOAS) dataset, our methodology was able to identify nine key metabolites including some known osteoarthritis-associated metabolites and some novel metabolic markers that have never been reported before. The results demonstrate the effectiveness of our methodology and more importantly, with further investigations, propose new hypotheses that can help better understand the OA disease.

Biomedical research has entered a new era where a large number of molecules and different components in biological systems can be quantitatively examined to investigate the causes of common human diseases. However, given the complexity of biological systems, those causes may not contribute to diseases individually but through interactions. The identification of those interactions, or the synergy of multiple factors, is a very challenging task due to the computational limitation, as well as the lack of effective methodologies for investigating multiple factors simultaneously. In this study, we proposed to model such an interaction effect through a self-learning algorithm using mechanisms inspired by natural evolution. Moreover, by constructing a synergy network using those evolved models, we were able to identify a set of interacting factors associated with a particular disease.

Higher serum choline and betaine levels are associated with better body composition in male but not female population

Background

Animal studies proved that choline and betaine have beneficial effect on reducing body fat. However, evidence in humans is scarce. We aim to investigate the association between serum choline and betaine levels with body composition in general population.

Methods

This is an observational cross-sectional study performed in 1081 subjects from the CODING (Complex Disease in Newfoundland population: Environment and Genetics) study. Serum choline and betaine levels were measured based on liquid chromatography coupled with tandem mass spectrometry technology. Body composition was measured using dual-energy X-ray absorptiometry following a 12-hour fast. Major confounding factors including age, sex, total calorie intake and physical activity level were controlled in all analyses.

Results

Significantly inverse correlations were found between serum betaine levels and all obesity measurements in males (r ranged from -0.12 to -0.23, and p<0.01 for all) but not in females. Serum choline was negatively associated with total percent body fat (%BF), percent trunk fat (%TF), weight, body mass index (BMI), waist circumference (WC), and waist-to-hip ratio (r ranged from -0.11 to -0.19, and p<0.05 for all) in males and positively associated with weight, BMI and WC (r ranged from 0.09 to 0.10, and p<0.05 for all) in females. The negative associations between serum choline and betaine levels with obesity in males were more profound in those not on any medication than those taking medications. Moreover, obese males had the lowest serum choline and betaine levels, followed by overweight males, and normal weight males having the highest serum choline and betaine levels, especially in those not taking medications (p<0.05). Likewise, subjects with the highest serum levels of both had the lowest obesity indexes, especially those not taking medications.

Conclusions

Higher serum choline and betaine levels were associated with a more favorable body composition (lower body fat and higher lean body mass) in males and the favorable association was more pronounced in non-medication users.

High dietary selenium intake is associated with less insulin resistance in the Newfoundland population

As an essential nutrient, Selenium (Se) is involved in many metabolic activities including mimicking insulin function. Data on Se in various biological samples and insulin resistance are contradictory, moreover there is no large study available regarding the relationship of dietary Se intake with insulin resistance in the general population. To investigate the association between dietary Se intake and variation of insulin resistance in a large population based study, a total of 2420 subjects without diabetes from the CODING (Complex Diseases in the Newfoundland Population: Environment and Genetics) study were assessed. Dietary Se intake was evaluated from the Willett Food Frequency questionnaire. Fasting blood samples were used for the measurement of glucose and insulin. Insulin resistance was determined with the homeostasis model assessment (HOMA-IR). Body composition was measured using dual energy X-ray absorptiometry. Analysis of covariance showed that high HOMA-IR groups in both males and females had the lowest dietary Se intake (μg/kg/day) (p < 0.01), being 18% and 11% lower than low HOMA-IR groups respectively. Insulin resistance decreased with the increase of dietary Se intake in females but not in males after controlling for age, total calorie intake, physical activity level, serum calcium, serum magnesium, and body fat percentage (p < 0.01). Partial correlation analysis showed that dietary Se intake was negatively correlated with HOMA-IR after adjusting for the Se confounding factors in subjects whose dietary Se intake was below 1.6 μg/kg/day (r = -0.121 for males and -0.153 for females, p < 0.05). However, the negative correlation was no longer significant when dietary Se intake was above 1.6 μg/kg/day. Our findings suggest that higher dietary Se intake is beneficially correlated with lower insulin resistance when total dietary Se intake was below 1.6 μg/kg/day. Above this cutoff, this beneficial effect disappears.

A systematic SNP selection approach to identify mechanisms underlying disease aetiology: linking height to post-menopausal breast and colorectal cancer risk

Data from GWAS suggest that SNPs associated with complex diseases or traits tend to co-segregate in regions of low recombination, harbouring functionally linked gene clusters. This phenomenon allows for selecting a limited number of SNPs from GWAS repositories for large-scale studies investigating shared mechanisms between diseases. For example, we were interested in shared mechanisms between adult-attained height and post-menopausal breast cancer (BC) and colorectal cancer (CRC) risk, because height is a risk factor for these cancers, though likely not a causal factor. Using SNPs from public GWAS repositories at p-values < 1 × 10⁻⁵ and a genomic sliding window of 1 mega base pair, we identified SNP clusters including at least one SNP associated with height and one SNP associated with either post-menopausal BC or CRC risk (or both). SNPs were annotated to genes using HapMap and GRAIL and analysed for significantly overrepresented pathways using ConsensuspathDB. Twelve clusters including 56 SNPs annotated to 26 genes were prioritised because these included at least one height- and one BC risk- or CRC risk-associated SNP annotated to the same gene. Annotated genes were involved in Indian hedgehog signalling (p-value = 7.78 × 10⁻⁷) and several cancer site-specific pathways. This systematic approach identified a limited number of clustered SNPs, which pinpoint potential shared mechanisms linking together the complex phenotypes height, post-menopausal BC and CRC.

Lysophosphatidylcholines to phosphatidylcholines ratio predicts advanced knee osteoarthritis

OBJECTIVE

To identify novel biomarker(s) for predicting advanced knee OA.

METHODS

Study participants were derived from the Newfoundland Osteoarthritis Study and the Tasmania Older Adult Cohort Study. All knee OA cases were patients who underwent total knee replacement (TKR) due to primary OA. Metabolic profiling was performed on fasting plasma. Four thousand and eighteen plasma metabolite ratios that were highly correlated with that in SF in our previous study were generated as surrogates for joint metabolism.

RESULTS

The discovery cohort included 64 TKR cases and 45 controls and the replication cohorts included a cross-sectional cohort of 72 TKR cases and 76 controls and a longitudinal cohort of 158 subjects, of whom 36 underwent TKR during the 10-year follow-up period. We confirmed the previously reported association of the branched chain amino acids to histidine ratio with advanced knee OA (P = 9.3 × 10(-7)) and identified a novel metabolic marker-the lysophosphatidylcholines (lysoPCs) to phosphatidylcholines (PCs) ratio-that was associated with advanced knee OA (P = 1.5 × 10(-7)) after adjustment for age, sex and BMI. When the subjects of the longitudinal cohort were categorized into two groups based on the optimal cut-off of the ratio of 0.09, we found the subjects with the ratio ⩾0.09 were 2.3 times more likely to undergo TKR than those with the ratio <0.09 during the 10-year follow-up (95% CI: 1.2, 4.3, P = 0.02).

CONCLUSION

We identified the ratio of lysoPCs to PCs as a novel metabolic marker for predicting advanced knee OA. Further studies are required to examine whether this ratio can predict early OA change.

Serum metabolic biomarkers distinguish metabolically healthy peripherally obese from unhealthy centrally obese individuals

Background

Metabolic abnormalities are more associated with central obesity than peripheral obesity, but the underlying mechanisms are largely unknown. The present study was to identify serum metabolic biomarkers which distinguish metabolically unhealthy centrally obese (MUCO) from metabolically healthy peripherally obese (MHPO) individuals.

Methods

A two-stage case–control study design was employed. In the discovery stage, 20 individuals (10 MHPO and 10 MUCO) were included and in the following validation stage, 79 individuals (20 normal weight (NW), 30 MHPO, 29 MUCO) were utilized. Study groups were matched for age, sex, physical activity and total dietary calorie intake with MHPO and MUCO additionally matched for BMI. Metabolic abnormality was defined as: 1) HOMA-IR > 4.27 (90^th percentile), 2) high-density lipoprotein cholesterol < 1.03 mmol/L in men and < 1.30 mmol/L in women, 3) fasting blood glucose ≥ 5.6 mmol/L, and 4) waist circumference > 102 cm in men and > 88 cm in women. MUCO individuals had all of these abnormalities whereas MHPO and NW individuals had none of them. A targeted metabolomics approach was performed on fasting serum samples, which can simultaneously identify and quantify 186 metabolites.

Results

In the discovery stage, serum leucine, isoleucine, tyrosine, valine, phenylalanine, alpha-aminoadipic acid, methioninesulfoxide and propionylcarnitine were found to be significantly higher in MUCO, compared with MHPO group after multiple testing adjustment. Significant changes of five metabolites (leucine, isoleucine, valine, alpha-aminoadipic acid, propionylcarnitine) were confirmed in the validation stage.

Conclusions

Significantly higher levels of serum leucine, isoleucine, valine, alpha-aminoadipic acid, propionylcarnitine are characteristic of metabolically unhealthy centrally obese patients. The finding provides novel insights into the pathogenesis of metabolic abnormalities in obesity.

Electronic supplementary material

The online version of this article (doi:10.1186/s12986-016-0095-9) contains supplementary material, which is available to authorized users.

Higher Dietary Choline and Betaine Intakes Are Associated with Better Body Composition in the Adult Population of Newfoundland, Canada

Background

Choline is an essential nutrient and betaine is an osmolyte and methyl donor. Both are important to maintain health including adequate lipid metabolism. Supplementation of dietary choline and betaine increase muscle mass and reduce body fat in animals. However, little data is available regarding the role of dietary choline and betaine on body composition in humans.

Objective

To investigate the association between dietary choline and betaine intakes with body composition in a large population based cross-sectional study.

Design

A total of 3214 subjects from the CODING (Complex Disease in Newfoundland population: Environment and Genetics) study were assessed. Dietary choline and betaine intakes were computed from the Willett Food Frequency questionnaire. Body composition was measured using dual-energy X-ray absorptiometry following a 12-hour fast. Major confounding factors including age, sex, total calorie intake and physical activity level were controlled in all analyses.

Result

Significantly inverse correlations were found between dietary choline and betaine intakes, with all obesity measurements: total percent body fat (%BF), percent trunk fat (%TF), percent android fat (%AF), percent gynoid fat (%GF) and anthropometrics: weight, body mass index, waist circumference, waist-to-hip ratio in both women and men (r range from -0.13 to -0.47 for choline and -0.09 to -0.26 for betaine, p<0.001 for all). Dietary choline intake had stronger association than betaine. Moreover, obese subjects had the lowest dietary choline and betaine intakes, with overweight subjects in the middle, and normal weight subjects consumed the highest dietary choline and betaine (p<0.001). Vice versa, when subjects were ranked according to dietary choline and betaine intakes, subjects with the highest intake of both had the lowest %TF, %AF, %GF, %BF and highest %LM among the groups in both sexes.

Conclusion

Our findings indicate that high dietary choline and betaine intakes are significantly associated with favorable body composition in humans.

Metabolomic analysis of human plasma reveals that arginine is depleted in knee osteoarthritis patients

OBJECTIVE

To identify novel biomarker(s) for knee osteoarthritis (OA) using a metabolomics approach.

METHOD

We utilized a two-stage case-control study design. Plasma samples were collected from knee OA patients and healthy controls after 8-h fasting and metabolically profiled using a targeted metabolomics assay kit. Linear regression was used to identify novel metabolic markers for OA. Receiver operating characteristic (ROC) analysis was used to examine diagnostic values. Gene expression analysis was performed on human cartilage to explore the potential mechanism for the novel OA marker(s).

RESULTS

Sixty-four knee OA patients and 45 controls were included in the discovery stage and 72 knee OA patients and 76 age and sex matched controls were included in the validation stage. We identified and confirmed six metabolites that were significantly associated with knee OA, of which arginine was the most significant metabolite (P < 3.5 × 10(-13)) with knee OA patients having on average 69 μM lower than that in controls. ROC analysis showed that arginine had the greatest diagnostic value with area under the curve (AUC) of 0.984. The optimal cutoff of arginine concentration was 57 μM with 98.3% sensitivity and 89% specificity. The depletion of arginine in OA patients was most likely due to the over activity of arginine to ornithine pathway, leading to imbalance between cartilage repair and degradation.

CONCLUSION

Arginine is significantly depleted in refractory knee OA patients. Further studies within a longitudinal setting are required to examine whether arginine can predict early OA changes.

Significant Beneficial Association of High Dietary Selenium Intake with Reduced Body Fat in the CODING Study

Selenium (Se) is a trace element which plays an important role in adipocyte hypertrophy and adipogenesis. Some studies suggest that variations in serum Se may be associated with obesity. However, there are few studies examining the relationship between dietary Se and obesity, and findings are inconsistent. We aimed to investigate the association between dietary Se intake and a panel of obesity measurements with systematic control of major confounding factors. A total of 3214 subjects participated in the study. Dietary Se intake was determined from the Willett food frequency questionnaire. Body composition was measured using dual-energy X-ray absorptiometry. Obese men and women had the lowest dietary Se intake, being 24% to 31% lower than corresponding normal weight men and women, classified by both BMI and body fat percentage. Moreover, subjects with the highest dietary Se intake had the lowest BMI, waist circumference, and trunk, android, gynoid and total body fat percentages, with a clear dose-dependent inverse relationship observed in both gender groups. Furthermore, significant negative associations discovered between dietary Se intake and obesity measurements were independent of age, total dietary calorie intake, physical activity, smoking, alcohol, medication, and menopausal status. Dietary Se intake alone may account for 9%–27% of the observed variations in body fat percentage. The findings from this study strongly suggest that high dietary Se intake is associated with a beneficial body composition profile.

Candidate gene study for smoking, alcohol use, and body weight in a sample of pregnant women

OBJECTIVE

Prenatal smoking, alcohol use, and obesity have significant effects on maternal and fetal health. However, not much is known about the genetic contributions to these risk factors among pregnant women. We evaluate the associations between several candidate genes and smoking, alcohol use, pre-pregnancy body weight, and weight gain during pregnancy in a sample of pregnant women.

METHODS

The study analyzes a sample of about 1900 mothers from the Danish National Birth Cohort. We test the association between 1450 SNPs in/near 117 genes/loci and various risk factor measures.

RESULTS

Only a few SNPs in FTO were significantly associated with pre-pregnancy obesity and body mass index (4 and 2 SNPs, respectively) after SNP-level correction for multiple testing. A few loci were significantly related to various smoking measures (any smoking, quitting and cigarette number) with gene/locus-level correction for multiple testing, but not after SNP-level correction. Similarly, some loci were significant for the alcohol measures at the gene/locus-level but not at SNP-level correction.

CONCLUSION

The study suggests that the majority of the evaluated candidate genes may not play an important role in influencing these risk factors among pregnant women, highlighting the importance of other genetic factors and non-genetic contributors to their etiology.