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Keywords:

  • gene;
  • depression;
  • trajectories;
  • biomarker

Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. ACKNOWLEDGMENTS
  8. REFERENCES

Methylenetetrahydrofolate reductase (MTHFR) genetic variation has been associated with the diagnosis of major depressive disorder (MDD) but no study to date has examined the effect MTHFR variation has on MDD prognosis. We sought to examine the prospective effects of two common MTHFR variants (C677T and A1298C) as well as seven haplotype-tagging single nucleotide polymorphisms (htSNPs) on MDD prognosis over a 5-year (60-month) period. Participants were 147 depressed primary care attendees enrolled in the Diagnosis, Management and Outcomes of Depression in Primary Care (diamond) prospective cohort study. Prognosis of MDD was measured using three methods: (1) DSM-IV criteria, (2) Primary Care Evaluation of Mental Disorders Patient Health Questionnaire-9 (PHQ-9), and (3) Center for Epidemiologic Studies Depression Scale (CESD). DSM-IV criteria for MDD was assessed using the Composite International Diagnostic Interview at baseline and 24, 36, 48, and 60 months post-baseline; whereas, PHQ-9 and CESD measures were employed at baseline and 12, 24, 36, 48, and 60 months post-baseline. Repeated measures analysis of variance showed that PHQ-9 symptom severity trajectories differed by C677T genotype (F = 3.34, df = 2,144, P = 0.038), with 677CC genotype showing the most severe symptom severity course over the 60 months of observation. Neither the A1298C polymorphism nor any of the htSNPs were associated with MDD prognosis regardless of measure used. Our results suggest that the MTHFR C677T polymorphism may serve as a marker for MDD prognosis pending independent replication. © 2013 Wiley Periodicals, Inc.


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. ACKNOWLEDGMENTS
  8. REFERENCES

Much of the personal, social, and economic burden associated with depression can be attributed to the considerable time and effort required and poorly defined guidelines available for promptly identifying and targeting interventions for individuals at-risk for development of persistent and disabling depression. We have documented the limitations of current depression guidelines with their linear approach to management [Hegarty et al., 2009], which fail to assist with individual treatment decisions, as they lack prognostic information about the likelihood of a particular outcome based on individual factors and circumstances [Wiesemann, 1998].

On such individual factor is genetic variation. Historically, genetic research of depression has focused on identifying diagnostic markers of current major depressive disorder (MDD). Largely ignored but equally important and relevant is research aimed at identifying genetic prognostic markers that could assist in identifying patients with a high probability of having an unfavorable (i.e., persistent depression) or favorable (i.e., remission) course. It has been estimated that one-third of depressed patients develop a persistent course while an additional one-third experience short-term remission [Licht-Strunk et al., 2007]. A retrospective audit of 222 participants diagnosed with depression in Dutch primary care found 40% (88/222) had more than one episode of depression in the 10-year follow-up period [van Weel-Baumgarten et al., 2000]. In a large European multicenter trial of depressed adults, 28% (83/301) were depressed 12 months after initial contact [Dowrick et al., 2000]. Importantly, persistently depressed individuals have substantial reductions in quality of life and high rates of suicidal ideation [Young et al., 2008]; yet presently they are “hidden” within a large group of “currently depressed” patients.

Despite this knowledge, prognostic studies for depression are rare, have limited performance when applied to primary care, and have focused on environmental and psychosocial markers of depression [Simon, 2000; van Weel-Baumgarten et al., 2000; Rubenstein et al., 2007; Katon et al., 2010; van Beljouw et al., 2010; Colman et al., 2011; Stegenga et al., 2012]. To our knowledge, no genetic-based prognostic tool is approved for use in primary care, psychiatry, or other medical specialty, albeit there are several examples within medicine of genetic-based tools currently in use that aid in diagnosis and/or determine disease susceptibility (e.g., HTT, Huntingtons disease; BRCA1/BRCA2, breast cancer). This paucity of genetic-based prognostic tools is likely to change in the near future particularly for some types of cancer (i.e., lung and colorectal) for which evidence for genetic prognostic markers has emerged over the last 10 years [Savas and Liu, 2009]. However, depression researchers have only recently commenced efforts to identify prognostic markers, few of which have included genetics. In fact, other than our group, only the Spanish PREDICT-gene study [Cervilla et al., 2006] and Netherlands Study of Depression and Anxiety [Penninx et al., 2008] are known to be in a position to examine and identify genetic markers of depression prognosis in primary care.

One promising candidate genetic marker for depression prognosis is the methylenetetrahydrofolate reductase (MTHFR) gene. The MTHFR gene encodes for a crucial enzyme involved in folate metabolism, neurotransmitter synthesis, and has been linked to homocysteine levels and S-adenosylmethionine (SAMe) biosynthesis; events shown to be perturbed in depression (see review: [Gilbody et al., 2007]. The MTHFR gene is also located in a linkage “hot spot” for recurrent, early onset depression [Zubenko et al., 2003]. Two common functional genetic variants in MTHFR have been identified, a C [RIGHTWARDS ARROW] T transition at nucleotide 677 in exon 4 (C677T) [Frosst et al., 1995] and an A [RIGHTWARDS ARROW] C transversion at nucleotide 1298 in exon 7 (A1298C) [Lievers et al., 2001]. 677T allele homozygotes have 30% enzyme activity compared to 677C allele homezygotes [Rozen, 1996] and those with the 1298CC genotype have approximately 60% of the enzyme activity of those with the AA genotype [Lievers et al., 2001]. Two recent meta-analyses representing 17 studies with a total of 3,486 MDD cases and 18,286 healthy controls have supported an association between MTHFR 677TT genotype and MDD diagnosis [Lopez-Leon et al., 2008; Peerbooms et al., 2011]; albeit one smaller meta-analysis did not support this association [Gaysina et al., 2008] and another suggests it is limited to individuals of East Asian descent [Zintzaras, 2006]. Two studies have examined the link between A1298C and MDD diagnosis; both suggesting 1298CC genotype carriers were more likely to have a diagnosis of MDD [Reif et al., 2005; Evinova et al., 2012]. However, no study to date has examined whether the C677T, A1298C, or other polymorphisms in the MTHFR gene are associated with MDD prognosis.

The primary aim of the present study was to determine if the MTHFR C677T and A1298C polymorphisms were associated with MDD prognosis in a prospective cohort study of depressed primary care attendees. We hypothesized that carriers of the 677TT and 1298CC genotypes would have greater rates of MDD and greater symptom severity over a 60-month observation period compared to 677C and 1298A allele carriers, respectively. As a secondary aim, we explored seven haplotype-tagging polymorphisms covering the entire MTHFR gene in an effort to identify novel MTHFR polymorphism associations with MDD prognosis.

MATERIALS AND METHODS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. ACKNOWLEDGMENTS
  8. REFERENCES

Study Population

All participants were enrolled in the Diagnosis, Management, and Outcomes of Depression in Primary Care (diamond) study, an ongoing prospective cohort that commenced in 2005 with an aim to document the experiences, health outcomes, treatment and service use of primary care patients identified as having clinically relevant depressed mood at screening [Gunn et al., 2008]. Full details of the study methods have been published elsewhere [Gunn et al., 2008; Potiriadis et al., 2008; Boardman et al., 2011]. Briefly, primary care patients were eligible for the diamond cohort if they were: (a) aged 18–75 years, (b) able to read English, (c) not terminally ill, (d) did not reside in a nursing home and (e) scored 16 or higher on the Center for Epidemiologic Studies Depression Scale [CES-D; [Radloff, 1977]]. For the current study, participants were also required to have met DSM-IV criteria [American Psychiatric Association, 1994] for MDD at baseline. Participants were assessed annually using postal surveys and/or computer-assisted telephone interviews. In 2011 (cohort year 6), participants enrolled in the cohort were invited to provide a saliva sample for DNA extraction and genotyping. All procedures were conducted in accord with principles expressed in the Declaration of Helsinki and obtained approval from the University of Melbourne Human Research Ethics Committee.

Phenotypic Variables

Outcome

The main outcome of interest was MDD prognosis. Prognosis was defined using three serial measures of depression: (1) DSM-IV criteria [American Psychiatric Association, 1994], (2) Primary Care Evaluation of Mental Disorders Patient Health Questionnaire-9 (PHQ-9) [Kroenke et al., 2001], and (3) Center for Epidemiologic Studies Depression Scale (CESD) [Radloff, 1977]. DSM-IV criteria for MDD was assessed at baseline and 24, 36, 48, and 60 months post-baseline using the Composite International Diagnostic Interview (CIDI) Auto version 2.1 [WHO, 1997] by a trained research assistant. PHQ-9 and CESD measures were employed at baseline and 12, 24, 36, 48, and 60 months post-baseline.

Potential confounders

At baseline, assessments were made of age, sex, highest level of completed education, smoking status, family history of depression, age of depression onset, health status [Ware et al., 1996], quality of life [World Health Organization, 1998], severe child abuse exposure, and personality [Moran et al., 2003]. Suicide ideation and attempt was assessed using items from the CIDI. Panic and other anxiety syndromes were assessed using the anxiety module of the PHQ [Spitzer et al., 1999]. Alcohol and drug abuse/dependence (i.e., cannabis, opioid, sedative, cocaine, amphetamine, hallucinogens, inhalants) was assessed using the CIDI Auto version 2.1 [WHO, 1997]. The current use of antidepressants, anxiolytics, antipsychotics, and herbal/alternative medications were also assessed.

Polymorphism Selection

Two functional MTHFR polymorphisms [C677T (rs1801133) and A1298C (rs1801131)] as well as seven haplotype-tagging single nucleotide polymorphisms (htSNPs) spanning the MTHFR gene were selected. htSNPs were selected using the International Haplotype Map (HapMap) Project (release 27) and Tagger [de Bakker et al., 2005]. The minimum pairwise linkage disequilibrium (LD) rate and minor allele frequency (MAF) were set at 0.80 and 0.15, respectively.

DNA Extraction and Genotyping

DNA was recovered from stabilized saliva samples using the manual prepIT system according to manufacturer's instructions (Oragene DNA (OG-500); DNA Genotek, Inc., Ontario, Canada). DNA precipitates were allowed to resuspend for a minimum of 48 hr before quantification by fluorimetry (QuantiFluor™ dsDNA System; Promega Corporation (Madison, WI) in conjunction with a Gemini™ Spectramax XPS fluorescence microplate reader (Molecular Devices, LLC; Sunnyvale, CA). DNA stocks were adjusted to a working concentration of between 10 and 50 ng/μl for subsequent genotyping.

All polymorphisms were genotyped with the Sequenom MassARRAY MALDI-TOF genotyping system using Sequenom iPLEX Gold chemistries according to manufacturer's instructions (Sequenom, Inc., San Diego, CA). Data analysis was performed in a semi-automated manner using the Typer 4.0 Analyzer Software (Sequenom, Inc.). All genotype calls not assessed as “conservative” by the analysis program were manually checked by the operator and discarded if a clear call could not be made. DNA extraction and genotyping were performed by the Australian Genome Research Facility, Ltd. (Brisbane, Australia).

Statistical Analysis

The CubeX program [Gaunt et al., 2007] was applied to detect departures from Hardy Weinberg Equilibrium (HWE), determine MAF, and estimate pairwise LD measures r2 and D′. SNPs with HWE below 0.01 or MAF below 0.10 were excluded from the analysis. To estimate the presence of population stratification, MAFs from the three HapMap phase III populations (Northern/Western European, CEU; Han Chinese, CHB; Yoruba in Nigeria, YRI) were extracted for each of the MTHFR SNPs and compared to the observed MAFs using chi-square analysis. Comparison of categorical and continuous baseline characteristics by C677T and A1298C genotypes was done using chi-square and analysis of variance (ANOVA), respectively. Repeated measures ANOVA were used to determine mean differences in MDD rates, PHQ-9 symptom severity and CESD symptom severity over the 60-month follow-up period by each of the selected MTHFR polymorphisms. In cases where a participant was missing a follow-up assessment, the last observation was carried forward. Where applicable Cohen's d effect size calculations were performed [Cohen, 1992]. All analyses were performed using PASW Statistics 18.0.2 (IBM, Armonk, NY).

RESULTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. ACKNOWLEDGMENTS
  8. REFERENCES

Of the 789 participants enrolled in the diamond cohort, 498 were enrolled at the time of DNA collection (cohort year 6) and 342 (69%) participants consented and returned a DNA sample. We excluded 38 individuals that returned their sample after genotyping had been completed and an additional 4 individuals who were missing >80% of genotype data. This resulted in 300 individuals of which 147 met criteria for MDD at baseline and were included in the analysis. Table I provides characteristics of these participants at baseline.

Table I. Participant Baseline Characteristics at Baseline (n = 147)
Baseline variables 
  • PHQ, Patient Health Questionnaire; WHOQOL-BREF, World Health Organization Quality of Life-BREF; SAPAS, Standardized Assessment of Personality—Abbreviated Scale.

  • a

    n = 124.

  • b

    n = 146.

  • c

    n = 143.

  • d

    n = 142 was assessed at 24 months.

Age, mean (SD) years47.4 (11.3)
Sex, % (n) female70.1 (103)
Education, % (n) tertiary27.9 (41)
Family history of depression, % (n)a83.9 (104)
Depression age of onset, mean (SD) years30.8 (16.8)
Suicide behavior
Ideation, % (n)36.7 (54)
1 or more attempts, % (n)2.7 (4)
Co-morbid psychiatric disorders
PHQ panic syndrome, % (n)23.1 (34)
PHQ other anxiety syndrome28.6 (42)
Substance use
Current smoker, % (n)28.6 (42)
Alcohol abuse/dependence, % (n)15.0 (22)
Any drug abuse/dependence, % (n)8.8 (13)
Medication use
Any antidepressant, % (n)57.1 (84)
Any anxiolytic, % (n)16.3 (24)
Any antipsychotic, % (n)8.8 (13)
Any herbal/alternative medication, % (n)b32.9 (48)
St. John's Wort, % (n)12.2 (18)
Quality of life and functioning
WHOQOL BREF physical, mean (SD)53.2 (17.7)
WHOQOL BREF social, mean (SD)44.5 (24.3)
Self-rated health, % (n) good to excellent57.1 (84)
Childhood abuse
Severe physical abuse, % (n)b34.9 (51)
Severe sexual abuse, % (n)c33.6 (48)
Personality
SAPAS total score, mean (SD)d2.96 (1.68)

All examined SNPs were in HWE (P > 0.05) and MAF ≥ 15% (Table II). Pairwise LD (r2 < 0.80 and D′ < 0.80) was not present and as such all SNPs were treated as independent. Comparison of MAFs from the HapMap phase III populations and the current sample showed MAFs for all SNPs examined did not differ (P-values >0.08) from the HapMap CEU population; suggesting the current sample is predominately of Northern/Western European descent (Table II).

Table II. MTHFR Minor Allele Frequencies and Pairwise Linkage Disequilibrium
dbSNP IDMinor alleleaMinor allele frequencybIntermarker distance (bp)LDc123456789 
CEUHCBYRICurrent study 
  • HCB, Han Chinese, Beijing; YRI, Yoruba in Ibadab, Nigeria.

  • a

    Minor allele based on the HapMapCentral European (CEU) population.

  • b

    CEU, HCB, and YRI frequencies obtained from International Haplotype Map (HapMap) Project, release 27.

  • c

    Calculated using CubeX [Gaunt et al., 2007].

rs13306561C0.170.100.240.1501 1.001.001.001.001.000.490.960.35 
rs9651118C0.210.310.270.223,59020.05 1.001.001.001.001.001.001.00 
rs7533315T0.310.100.270.231,53130.050.09 0.981.000.970.480.480.56 
rs17421511A0.190.110.010.182,89540.040.090.60 1.001.000.820.920.96 
rs1801133 (C677T)T0.310.510.090.341,41050.090.150.180.11 1.001.001.001.00D′
rs6541003G0.450.200.570.4051160.270.180.470.310.36 1.001.001.00 
rs12121543A0.260.170.100.231,19670.100.090.220.440.170.47 1.000.94 
rs1801131 (A1298C)C0.340.200.120.3219580.350.120.170.390.240.670.70 0.93 
rs1476413A0.310.200.120.262,17690.070.100.290.530.190.520.760.68  
            r2     

MDD rates and CES-D symptom severity trajectories over the 60-month follow-up period did not differ by C677T or A1298C genotype. However, PHQ-9 symptom severity trajectories were more severe for 677CC genotype carriers compared to TT genotype carriers (F = 3.34, df = 2,144, P = 0.038; Fig. 1). Post hoc examination of each of the PHQ-9 follow-up assessments independently showed that 677CC genotype carriers had greater PHQ-9 scores at 24 (Cohen's d = 0.79, P = 0.024), 36 (d = 0.91, P = 0.008), 48 (d = 0.90, P = 0.014), and 60 (d = 0.66, P = 0.027) months post-baseline compared to 677TT genotype carriers. Examination of baseline demographic and clinical characteristics by C677T genotype showed no significant differences (Table III). PHQ-9 symptom severity trajectories did not differ by A1298C genotype.

image

Figure 1. Measures of depression over 60 months of follow-up by MTHFR C677T and A1298C polymorphisms. Error bars represent standard error of the mean. PHQ-9, Primary Care Evaluation of Mental Disorders Patient Health Questionnaire-9; CESD, Center for Epidemiologic Studies Depression Scale.

Download figure to PowerPoint

Table III. Participant Characteristics by Genotype
Baseline Variablesrs1801133 (C677T)Prs1801131 (A1298C)P
CC (n = 63)CT (n = 68)TT (n = 16)AA (n = 69)CA (n = 63)CC (n = 15)
  • PHQ, Patient Health Questionnaire; WHOQOL-BREF, World Health Organization Quality of Life-BREF; SAPAS, Standardized Assessment of Personality—Abbreviated Scale.

  • a

    n = 124.

  • b

    n = 146.

  • c

    n = 143.

  • d

    n = 142 was assessed at 24 months.

Age, mean (SD) years47.0 (11.2)47.9 (11.1)47.0 (13.5)0.89448.5 (11.0)46.4 (12.2)46.6 (9.4)0.570
Sex, % (n) female71.4 (45)69.1 (47)68.8 (11)0.95271.0 (49)68.3 (43)73.3 (11)0.902
Education, % (n) tertiary27.0 (17)29.4 (20)25.0 (4)0.91830.4 (21)25.4 (16)26.7 (4)0.807
Family history of depression, % (n)a86.5 (45)83.6 (51)72.7 (8)0.52686.2 (50)79.2 (42)92.3 (12)0.416
Depression age of onset, mean (SD) years31.4 (18.3)31.9 (16.1)24.1 (12.0)0.23231.9 (17.7)29.8 (15.8)30.1 (17.2)0.764
Suicide behavior52.4 (33)44.1 (30)50.0 (8)0.68253.6 (37)41.3 (26)53.3 (8)0.380
Ideation, % (n)41.3 (26)33.8 (23)31.3 (5)0.60339.1 (27)33.3 (21)40.0 (6)0.758
1 or more attempts, % (n)1.6 (1)2.9 (2)6.3 (1)0.6292.9 (2)3.2 (2)0.0 (0)0.878
Co-morbid psychiatric disorders
PHQ panic Syndrome, % (n)20.6 (63)25.0 (17)25.0 (4)0.82527.5 (19)17.5 (11)26.7 (4)0.368
PHQ other anxiety syndrome25.4 (16)30.8 (21)31.2 (5)0.70037.7 (26)33.3 (14)13.3 (2)0.042
Substance use
Current smoker, % (n)31.7 (20)26.5 (18)25.0 (4)0.75624.6 (17)28.6 (18)46.7 (7)0.231
Alcohol abuse/dependence, % (n)14.3 (9)11.8 (8)31.2 (5)0.14213.0 (9)12.7 (8)33.3 (5)0.109
Drug abuse/dependence, % (n)7.9 (5)8.8 (6)12.5 (2)0.8488.7 (6)6.3 (4)20.0 (3)0.246
Medication use
Any antidepressant, % (n)55.6 (35)63.2 (43)37.5 (6)0.16450.7 (35)61.9 (39)66.7 (10)0.317
Any anxiolytic, % (n)17.5 (11)147 (10)18.8 (3)0.87914.5 (10)14.3 (9)33.3 (5)0.171
Any antipsychotic, % (n)9.5 (6)10.3 (7)0.0 (0)0.41410.1 (7)7.9 (5)6.7 (1)0.862
Any herbal/alternative medication, % (n)b34.9 (22)34.3 (23)18.8 (3)0.44336.8 (25)27.0 (17)40.0 (6)0.406
St. John's Wort, % (n)11.1 (7)16.2 (11)0.0 (0)0.19313.0 (9)12.7 (8)6.7 (1)0.784
Quality of life and functioning
WHOQOL BREF physical, mean (SD)54.7 (18.7)53.3 (16.8)47.1 (17.3)0.31150.5 (18.8)54.9 (16.9)58.6 (14.7)0.172
WHOQOL BREF social, mean (SD)43.0 (25.0)45.7 (24.4)45.3 (22.6)0.80942.7 (23.5)46.2 (24.8)45.6 (27.1)0.715
Self-rated health, % (n) good to excellent58.7 (37)57.4 (39)50.0 (8)0.81953.6 (37)58.7 (37)66.7 (10)0.616
Childhood abuse
Severe physical abuse, % (n)b38.1 (24)32.4 (22)33.3 (5)0.78136.8 (25)28.6 (18)53.3 (8)0.178
Severe sexual abuse, % (n)c33.9 (21)31.3 (21)42.9 (6)0.70739.4 (26)25.8 (16)40.0 (6)0.228
Personality
SAPAS total score, mean (SD)d3.2 (1.9)2.8 (1.6)2.9 (1.4)0.4883.0 (1.7)2.9 (1.7)3.3 (2.0)0.611

Examination of the six MTHFR htSNPs showed no associations with MDD rates, PHQ-9 symptom severity, or CES-D symptom severity over the 60-month follow-up period (Table IV).

Table IV. Effects of MTHFR Haplotype-Tagging SNPs on MDD Prevalence, PHQ-9 Symptom Severity and CES-D Symptom Severity Over 60-Months of Follow-Up
dbSNP IDNMDDPHQ-9CES-D
FPFPFP
rs133065611470.310.7361.290.2761.290.279
rs96511181470.610.2791.030.3601.150.318
rs75333151472.530.0832.390.0952.370.097
rs174215111471.140.3050.760.4670.760.470
rs65410031471.950.1450.170.8430.070.930
rs121215431471.190.3090.160.8550.040.965
rs14764131472.480.0870.980.3790.730.484

DISCUSSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. ACKNOWLEDGMENTS
  8. REFERENCES

Our results suggest, contrary to our hypothesis, that carrying the 677CC genotype may increase an individual's probability of remaining depressed in the long term. This was noted most clearly when examining PHQ-9 symptom severity scores over the 60-month follow-up period in which 677CC genotype carriers had significantly greater severity scores at 24, 36, 48, and 60 months post-baseline compared to 677TT genotype carriers. In fact, 677CC genotype carriers had mean scores of 11 on the PHQ-9 (moderate depression) at the 24, 36, 48, and 60-month follow-ups; whereas, 677TT genotype carriers at the same follow-ups had mean scores below 7 (e.g., mild depression). We did not observe an association between the A1298C variant and MDD prognosis regardless of depression measure examined. In addition, our secondary analysis of six htSNPs covering the entire MTHFR gene revealed no associations with MDD prognosis.

No study to date has examined the link between MTHFR genetic variation and MDD prognosis. However, studies that have associated MTHFR genotypic variation with MDD diagnosis have suggested that 677TT genotype carriers have greater odds of receiving a diagnosis of MDD. Interestingly, previous meta-analytic research has suggested that the association between the 667TT genotype and MDD diagnosis may be ancestry dependent in that among Asians but not Caucasians the association is present [Zintzaras, 2006]. In fact, among the seven studies conducted in Caucasian samples, five [Almeida et al., 2005; Reif et al., 2005; Lewis et al., 2006; Almeida et al., 2008; Gaysina et al., 2008; Hong et al., 2009] have reported odds ratios (ORs) for MDD diagnosis below or equal to 1.0 (i.e., protective or no effect) for the 677T allele, albeit none reach statistical significance. The remaining two studies [Kelly et al., 2004; Lewis et al., 2006] reported ORs above 1.0 (i.e., risk effect) but only the Lewis et al. [2006] findings were statistically significant (OR = 1.35, 95% CI = 1.01–1.80). Thus, within a sample predominately of European descent, such as our sample, the C677T polymorphism appears to have modest affects on risk for MDD diagnosis. As such, our findings, albeit modest, merit replication efforts to determine if the C677T polymorphism is a suitable marker for MDD prognosis.

Our findings are strengthened in that at baseline; depression ratings (PHQ-9 and CES-D) and factors known to be associated with depression (e.g., age, sex, family history of depression, comorbid anxiety, substance use, medication use) did not differ by C677T genotype. In fact, post hoc examination of antidepressant use at each of the follow-ups showed no differences in reported use between 677CC and TT genotypes (P's > 0.589; data not shown). In addition, frequencies of the 677TT genotype in our sample (10.8%) did not differ to that of the collective 677TT frequency among MDD cases of Caucasian/European ancestry (n = 2,542) reported in seven previous studies (9.3%, P = 0.30) [Almeida et al., 2005; Reif et al., 2005; Lewis et al., 2006; Almeida et al., 2008; Gaysina et al., 2008; Hong et al., 2009], suggesting that recruitment bias is unlikely and that our sample is genetically similar to previously reported study samples.

Despites these strengths two key caveats should be acknowledged. First, our study sample size was small, statistical power to detect differences in MDD prognosis was sub-optimal, and none of our results survived correction for multiple comparisons (Bonferroni adjusted P = 0.008). Our negative findings for the A1298C variant and the seven htSNPs may also be an artifact of our small sample size and represent Type II errors. Although we are the first to report on the effect of A1298C on MDD prognosis, two previous studies have reported that the A1298C variant is associated with MDD diagnosis [Clemente et al., 2003; Reif et al., 2005]. Reif et al. [2005] reported in a sample of 46 German affective psychosis inpatients and Evinova et al. [2012] reported in a sample of 134 Slovak acute inpatients that MDD diagnosis was associated with a 2.63 (95% CI = 1.02–6.77) and 2.38 (95% CI = 1.07–5.32) greater odds of carrying the 1298CC genotype compared to healthy controls. Interestingly, 10.2% of participants in our MDD primary care sample carried the 1298CC genotype compared to 14.9% (z = 1.18, P = 0.12) in the Evinova et al. [2012] acute inpatient study and 19.6% (z = 1.46, P = 0.07) in the Reif et al. [2005] affective psychosis inpatient study. Although statistically these genotype frequencies are similar, there appears to be a monotonic trend in which 1298CC frequency aligns with the intensity of treatment. Thus, our negative findings for A1298C may be less related to sample size and more likely a result of treatment modality from which our participants were recruited. Alternatively, our results and those previously reported suggest that the A1298C variant may be associated with risk for MDD diagnosis but not with MDD prognosis. Large prospective studies that recruit MDD participants from different treatment modalities are needed to test these potential explanations. In addition, for the C677T variant we observed trend level differences in MDD rates and CES-D symptom severity over the 60-month follow-up period (MDD rates: P = 0.052; CES-D: P = 0.072). Post hoc examination of MDD rates and CES-D follow-up assessments independently showed that 677CC genotype carriers had clinically and/or statistically higher rates of MDD at 24 (d = 0.48, P = 0.241) and 48 (MDD: d = 0.59, P = 0.002) months post-baseline and greater CES-D symptom severity at 24 (d = 0.84, P = 0.044), 36 (d = 0.98, P = 0.009), and 48 (d = 0.74, P = 0.038) months compared to 677TT genotype carriers. Thus, the lack of an association between C677T genotype and MDD prognosis using MDD rates and CES-D symptom severity could signify the presence of Type II errors (false negatives). It is also could reflect inherent differences between the three measures of depression we used. Post hoc examination of the nine individual items of the PHQ suggested that two items (6 and 9) are influencing (P < 0.05) the observed association. Item 6 assess feelings of worthlessness and item 9 assess suicidal thoughts. Feelings of worthlessness are assessed by both the CES-D and CIDI, whereas suicidal thoughts are assessed by the CIDI but not the CES-D; suggesting our results are likely not driven by items that are specific to the PHQ-9.

Second, we controlled for a range of potential confounders, a notable strength compared to previous MDD studies of MTHFR that rarely control for confounders other than age, sex, and ancestry. However, as in any observational study, we cannot exclude the possibility of unmeasured confounding. For example, we did not measure blood levels of homocysteine or folate, key components of the one-carbon metabolism pathway that interact with MTHFR [Lewis et al., 2006]. The 677TT genotype has been linked to hyperhomocysteinemia [Frosst et al., 1995; Brattstrom et al., 1998] and folate levels [Botto and Yang, 2000], which in turn have been associated with depression [Alpert et al., 2000; Bjelland et al., 2003; Papakostas et al., 2012] albeit findings have been mixed [Moorthy et al., 2012]. Additionally, folic acid supplementation has been shown to confer antidepressant effects [Taylor et al., 2004; Morris et al., 2008] and other genetic and nutritional (e.g., B-12, B-6 intake) factors as well as various drugs (e.g., antibiotics, oral contraceptives, some anticancer agents) may have an effect on homocysteine and folate metabolism [Alpert et al., 2000; Geisel et al., 2003; Kluijtmans et al., 2003; Sunder-Plassmann and Fodinger, 2003] and modify depression course. Thus, it is plausible that the effect we observed is confounded by these unmeasured factors and further research is required, particularly using prospective studies, to untangle the relationships between MTHFR genotype, homocysteine, folate, and their effects on MDD prognosis.

In summary, the present study found that individuals with MDD at baseline who carried the 677CC genotype were more likely to have greater symptom severity over a 60-month follow-up compared to TT genotype carriers. Our results suggest that MTHFR genetic variation may serve as a marker for depression prognosis pending independent replication. If replicated, MTHFR genetic variation, along with promising environmental factors [Gunn et al., 2013], could contribute to the development of a prognostic tool that in the future may assist primary care and psychiatric clinicians in the early identification of depressed individuals at risk for a unfavorable clinical course and signal the need for targeted interventions.

ACKNOWLEDGMENTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. ACKNOWLEDGMENTS
  8. REFERENCES

The diamond study is funded by the National Health and Medical Research Council (IDs 299869, 454463, 566511 and 1002908) and the Victorian Centre for Excellence in Depression and Related Disorders, an initiative between beyondblue and the Victorian Government. The collection of DNA and genotyping was funded by the LEW Carty Chartable Fund (ID 7284). No funding body had a role in the study design; the collection, analysis, and interpretation of data; or the writing of the manuscript for publication. We acknowledge the 30 dedicated GPs, their patients and practice staff for making this research possible. We thank the diamond project team, including associate investigators and researchers involved in the diamond study: Ms. Aves Middleton, Ms. Konstancja Densley, Professor Helen Herrman, Professor Christopher Dowrick, Dr. Gursharan Chana and casual research staff.

REFERENCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. ACKNOWLEDGMENTS
  8. REFERENCES
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