Atypical antipsychotics have become a common therapeutic option in both schizophrenia and bipolar disorder. However, these medications come with a high risk of metabolic side effects, particularly dyslipidemia and insulin resistance. Therefore, identification of patients who are at increased risk for metabolic side effects is of great importance. The genetics of fatty acid metabolism is one area of research that may help identify such patients. Therefore, in this present study, we aimed to determine the effect of one commonly studied genetic polymorphism from both fatty acid desaturase 1 (
The use of antipsychotics, particularly the atypical antipsychotics (AAPs), is considered the standard of care in schizophrenia symptom management and is becoming a common therapeutic choice in the management of bipolar disorder [
Fatty acids (FAs) serve many important physiological functions including energy reserves, structural components of cell membranes, precursors of eicosanoids, and regulators of gene expression. The role FAs play in cell membranes is of particular interest as they influence translocation of glucose transporters and insulin receptor binding and signaling in addition to cell membrane fluidity and permeability. This indicates that FAs may play an important role in the development of insulin resistance and type 2 diabetes mellitus [
Therefore, in this present study, we aimed to determine the effect of one commonly studied genetic polymorphism (SNP) from both the
Male and female participants were recruited from outpatient mental health clinics in the Southeastern Michigan area. Subjects were considered for inclusion if they met the following criteria: (1) aged 18–80 and diagnosed with schizophrenia, schizoaffective disorder, or bipolar disorder, (2) currently taking an antipsychotic, and (3) no medication changes for the previous 6 weeks. Subjects were excluded based on the following criteria: (1) having an active substance abuse or dependence diagnosis, (2) currently taking a medication for diabetes (to avoid bias in the insulin resistance measure), or (3) unwilling or unable to participate. The study was approved by the University of Michigan Institutional Review Board.
Participants came to the University of Michigan Clinical Research Unit (MCRU) for a single visit. Study visits were completed in the morning, within 2 hours of the participants’ usual wakening time. Participants were required to fast overnight for the visit. After obtaining an informed consent, participants underwent the structured clinical interview for DSM-IV-TR diagnoses (SCID) performed by a trained research assistant in order to confirm their psychiatric diagnosis. Psychiatric diagnoses were also confirmed by medical chart review when possible. Subjects were asked about basic demographic information (e.g., age, race, and gender), current medications (also confirmed by pharmacy records), and current or past cigarette smoking. A registered nurse took height and waist measurements along with a blood pressure measurement and a blood draw. Body mass index (BMI, kg/m2) was calculated from this information. The blood draw was collected for genetic analysis and fasting labs such as lipids (which included total cholesterol (TC), triglycerides (TG), high-density lipoproteins (HDL), and low-density lipoprotein (LDL)), blood glucose, and insulin measurements were measured. Insulin resistance was calculated using the homeostatic model assessment of insulin resistance [
Whole blood was used for DNA extraction using the salt precipitation method [
Linkage analysis between the
Statistical analyses were performed with JMP Pro 9.0 software (JMP, Version 9.0. SAS Institute Inc., Cary, NC, 1989–2012). Hardy-Weinberg equilibrium (HWE) was evaluated using Haploview 4.2. One-way analysis of variance (ANOVA) was used to assess differences in mean values of clinical and metabolic variables within psychiatric diagnosis and genetic variant groups (
A total of 320 subjects with schizophrenia (
Demographic and metabolic characteristics of the schizophrenia, bipolar, and combined samples.
Schizophrenia spectrum ( |
Bipolar disorder ( |
Combined ( |
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Age (year) |
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Gender (% female) | 37 | 63 | 44.7¥ |
Race (% Caucasian/% African American/% other*) | 65/26/9 | 81/12/7 | 70/21/9¥ |
Current smokers (%) | 53 | 32 | 47¥ |
Currently on AAP (%) | 85 | 74 | 82¥ |
BMI (kg/m2) |
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SBP (mmHg) |
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DBP (mmHg) |
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TC (mg/dL) |
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TG (mg/dL) |
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HDL (mg/dL) |
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LDL (mg/dL) |
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Glucose (mg/dL) |
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Insulin (µIU/mL) |
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HOMA-IR |
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Means ± S.D. or percentage.
AAP: atypical antipsychotic, BMI: body mass index, SBP: systolic blood pressure, DBP: diastolic blood pressure, TC: total cholesterol, TG: triglycerides, HDL: high-density lipoprotein, LDL: low-density lipoprotein, and HOMA-IR: homeostasis model assessment-insulin resistance.
The
Schizophrenia spectrum | Bipolar disorder | Combined | ||
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GG genotype | 54.5 (122) | 48.9 (46) | 52.8 (168) |
T allele | 45.5 (102) | 51.2 (58) | 47.2 (150) | |
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CC genotype | 75.2 (170) | 71.3 (67) | 74.1 (237) |
T allele | 24.8 (56) | 28.7 (27) | 26.0 (83) |
% (
Haplotype frequencies for combined sample.
Haplotype number |
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Frequency |
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1 | G | C | 0.708 |
2 | G | T | 0.006 |
3 | T | C | 0.159 |
4 | T | T | 0.127 |
Empirical haplotype frequencies. Gives total count for all haplotypes inferred.
Table
Demographic and metabolic characteristics based on
SNP/haplotypes |
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11 | 13 | 14 | 33 | 34 | ||
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Genotype/allele/haplotype genotypes | GG genotype ( |
T allele ( |
CC genotype ( |
T allele ( |
GC/GC ( |
GC/TC ( |
GC/TT ( |
TC/TC ( |
TC/TT ( |
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Age (years) |
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Gender (% female)
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37 | 52 | 44 | 48 | 37 | 54 | 50 | 73 | 33 |
Race (% Caucasian/% African American/% other*) |
57/34/9 | 83/8/9 | 65/27/8 | 81/8/11 | 57/35/8 | 82/10/8 | 78/11/11 | 100/0/0 | 94/6/0 |
Current smokers (%) | 48 | 47 | 47 | 47 | 48 | 51 | 43 | 27 | 61 |
Currently on AAP (%) | 78 | 85 | 81 | 84 | 78 | 88 | 85 | 91 | 73 |
BMI (kg/m2) |
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SBP (mmHg) |
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DBP (mmHg) |
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TC (mg/dL) |
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TG (mg/dL) |
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HDL (mg/dL) |
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LDL (mg/dL) |
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Glucose (mg/dL) |
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Insulin (µIU/mL) |
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HOMA-IR |
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Our candidate gene hypotheses regarding the influence of
No statistically significant associations were found for the
When using haplotypes as the independent variable in our regression analysis, a trend with triglycerides was found that did not meet multiple testing cutoff (whole model
Association between HOMA-IR and
Within our study, we found that HOMA-IR was associated with the haplotype of two investigated
HapMap CEU data has shown that the
A nonsignificant trend after a multiple testing adjustment (
Several limitations of our study need to be addressed. First, this is a cross-sectional study and causal associations cannot be drawn from the data; prospective, randomized studies are needed in populations treated with AAPs to draw further conclusions. Second, our study had differences in demographics between the bipolar disorder and the schizophrenia subjects. While these are important differences to consider, we used psychiatric diagnosis as a confounder in our main hypothesis testing; therefore, these differences are a natural reflection of the diagnosis and recruitment area. Third, our subject population was largely exposed to AAPs with known metabolic side effects which makes translation of our results to other disease populations or populations exposed to other medications challenging. Fourth, we used a surrogate measure of insulin resistance, which, although not a direct measure of insulin resistance, has been highly correlated to more invasive measures such as the glucose clamps and the oral glucose tolerance test [
Despite these limitations, our study is the first to identify a relationship between a
HOMA-IR was associated with a
The authors have no conflict of interests to disclose in regard to this paper.
The following sources were utilized for this publication: NIMH (R01 MH082784, K08 MH64158), NIH-NCCR, GCRC Program (UL1RR024986), the Chemistry Core of the Michigan Diabetes Research and Training Center (NIH5P60 DK 20572) (all Bethesda, Maryland) and the Washtenaw Community Health Organization (WCHO, Ann Arbor, Michigan), The Brain and Behavior Research Foundation (formerly NARSAD, Great Neck, New York), and the Prechter Longitudinal Study of Bipolar Disorder (Ann Arbor, Michigan).