Hepatic, lipid and genetic factors associated with obesity: crosstalk with alcohol dependence?

<p><b>Objectives:</b> Alcohol dependence represents a leading cause of mortality and morbidity. Understanding the variables that contribute to this diagnosis and its severity is critical. An overlap between factors that may predispose people to become obese and those that may increase the risk of alcohol dependence may exist. However, data in the literature are not conclusive. Therefore, this study aimed to identify the association between alcohol dependence and obesity-related factors, including biochemical and genetic factors.</p> <p><b>Methods:</b> In a case–control study with 829 participants, factors involved with metabolism and obesity were assessed, including biochemical lipid and liver markers, and the fat mass and obesity-associated (FTO) single nucleotide polymorphism (SNP) rs8050136.</p> <p><b>Results:</b> Increased triglycerides, having one or two minor A alleles for rs8050136 and being a smoker were associated with increased risk of alcohol dependence, while increased low-density lipoprotein cholesterol was associated with decreased risk. In addition, having abnormal gamma-glutamyl transferase and being female were factors associated with an increased severity of alcohol dependence.</p> <p><b>Conclusions:</b> Our preliminary findings suggest a link between alcohol dependence and obesity-related biochemical and genetic factors. Future studies are needed to better understand if these factors may play a predictive role and/or may act as biomarkers for treatment response.</p

Comparison of demographic variables between genotype groups.

<p>There were no genotype group differences between groups in all demographic variables except African and European ethnic factor scores (²P<0.001; ³P = 0.001). Also note that Ns for BDI scores¹ were 49 and 11 for A allele homozygotes and G allele carriers, respectively.</p

Mean rich and lean hit rates across blocks for AA homozygotes and G allele carriers.

<p>There was a genotype group×block interaction for rich hit rates (A). AA homozygotes (black bars) had increased rich hit rates over time while G allele carriers (gray bars) had decreased rich hit rates over time. There were no effects of genotype group or interactions for lean hit rates (B). Error bars represent standard errors.</p

Association analysis of AFQT scores and SNPs.

<p>The z-transformed (normalized) AFQT-scores were used to evaluate the association between BDNF genotypes and general intelligence at pre-injury, early- recovery and late- recovery. All 7 SNPs were analyzed using three-way ANOVA. Importantly, SNP rs908867 was excluded from the analysis because of low number of minor allele carriers. SNP rs12273363 was excluded from further analysis because of lack of consistency with Hardy-Weinberg equilibrium test.</p><p>*Effect survives Bonferroni correction (P = 0.05/6 ≈ 0.0083).</p

Frequencies of haplotypes: Only haplotypes with frequencies ≥0.1 were used.

<p>*Frequencies of 6-SNP haplotypes (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0027389#pone-0027389-g001" target="_blank">Fig.1</a>).</p><p>**Block 1 Haplotype Frequencies (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0027389#pone-0027389-g001" target="_blank">Fig.1 left panel</a>, SNPs 1–3).</p><p>***Block 2 Haplotype Frequencies (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0027389#pone-0027389-g001" target="_blank">Fig.1 right panel</a>, SNPs 4–6).</p

Studied BDNF SNPs: chromosome position, location, minor allele frequencies and genotyping quality control values are present.

1<p>Positions on chromosome 11 correspond to dbSNP build 126B and human genome build 36.3.</p>2<p>Reference minor allele frequencies are for Caucasian population (CEU, HapMap Release 28, Phase II+III, August 2010, NCBI B36 Assembly).</p>3<p>Predicted and </p><p>⁴observed heterozygote percentages.</p>5<p>SNP rs12273363 was excluded from the analysis because of lack of consistency with Hardy-Weinberg equilibrium test.</p

Association analysis of significant SNPs.

<p>The z-transformed (normalized) AFQT-scores of pre-injury, phase II and III for SNP are illustrated for the significant SNPs, rs7124442 (A) and SNP rs1519480 (B). Note that zero-line represents our normal control.</p

Haplotype association analysis.

<p>Haplotype association analysis illustrating z-Normalized AFQT scores among different genotype carriers pre-injury, Phase II and Phase III. Haplotype 6 SNPs (A) include all the SNPs in LD. Block 2 (B) includes SNPs rs7934165, rs11030121 and rs908867. Block 1 (C) includes SNPs rs1519480, rs7124442 and rs6265. In addition we analyzed the combination of haplotype 112 and 222 from block 1 and compared this to group 111 (D).</p

Association analysis of haplotype and AFQT z-scores at different time points.

<p>For graphical representation see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0027389#pone-0027389-g003" target="_blank">Fig 3</a>. </p><p>* Block 1 includes SNPs rs1519480, rs7124442 and rs6265. </p><p>** Block 2 includes SNPs rs7934165, rs11030121 and rs908867. </p><p>*** haplotypes 112 and 222 of block 1 were combined and compared with haplotype 111. </p><p>* Effect survives Bonferroni correction (P = 0.05/3 ≈ 0.017).</p

Overlay lesion map for all 109 subjects.

<p>Lesion map for the 109 subjects where lesions were overlaid on a standard brain template. The number of overlapping lesions is illustrated by different colors coding increasing frequencies from blue to red.</p