• AKT1;
  • AKTIP;
  • bipolar disorder;
  • suicidal behavior;
  • violent suicide attempts


  1. Top of page
  2. Abstract
  3. Introduction
  4. Material and methods
  5. Results
  6. Discussion
  7. References
  8. Acknowledgments

We tested the hypothesis that the presence of AKT1 and AKTIP polymorphisms, target genes that encode key proteins in the signaling of dopaminergic and serotonergic systems, is associated with suicidal behavior in bipolar patients. The subjects were 273 patients diagnosed with bipolar disorder I or II (age = 41.4 ± 12.9). TaqMan single-nucleotide polymorphism genotyping assays (AKT1: rs2494731, rs3803304, rs3730358, rs10149779, rs2494746, rs1130214 and rs249878; AKTIP: rs9302648 and rs7189819) were used. We found that the AKT1 marker showed an association with suicide attempts (rs1130214, P < 0.05) and attempted violent attacks (rs2494746, P < 0.05). One out of the seven tested markers of AKT1 attained significant genotype association with violent attempt (rs2494731; P < 0.05). A significant association was detected in the AKT1 haplotype test. We did not observe an association between suicidal behavior and AKTIP variants and also did not find an interaction between AKTIP and AKT1 polymorphisms. In addition, we found that demographic and clinical data are associated with lifetime history of suicide attempts. Our data suggest that demographic and clinical characteristics and AKT1 single markers and haplotypes, but not AKTIP polymorphisms or interactions between AKT1 and AKTIP, are associated with increased risk for suicidal behavior in bipolar patients.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Material and methods
  5. Results
  6. Discussion
  7. References
  8. Acknowledgments

Suicide is a significant public health issue and a major cause of death throughout the world (Bondy et al. 2006). Although various non-genetic, psychological, sociological and economic factors clearly contribute to the complex etiology of suicidal behavior, evidence for a genetic liability is also convincing (Brent et al. 2002; Wasserman et al. 2008). Evidence suggests that suicidal behavior is a disorder in its own right (Viana et al. 2006), although psychiatric comorbidities often complicate the situation, with about 90% of suicide subjects having at least one psychiatric diagnosis (Mann 2002).

Bipolar disorder (BD) is associated with high suicide rates. Between 25 and 60% of bipolar patients make at least one suicide attempt during the course of their illness. In addition, it is estimated that individuals with BD are 30 times more likely to attempt suicide than those without a psychiatric disorder (Chen & Dilsaver 1996; Kessler et al. 1999; Pompili et al. 2008; Potash et al. 2000). However, not all BD patients attempt or suicide, suggesting that, although the presence of environmental factors may be a risk, other elements may also play a role (Neves et al. 2009; Tsuang et al. 2004). Previously, we have shown that genetic (Neves et al. 2008) and neuropsychological features (Malloy-Diniz et al. 2009) are involved in suicide or attempted suicide in bipolar patients, but our knowledge of the genetic risk factors is still poor.

In recent years, an approach to identify susceptibility variants has been used following the ‘functional candidate’ genes or molecular pathways on the basis of disease etiology. Alterations in intracellular signaling pathways are known to be important in neuropsychiatric disorders (Einat & Manji 2006; Guo 2004). The serine/threonine protein kinase B/Akt1 was shown to play a central role in a number of cellular responses to growth factors and insulin (Beaulieu et al. 2007a; Sale & Sale 2008). Dysregulated Akt1 pathway in BD could have multiple effects that could impair neural plasticity, such as modulation of neuronal architecture, neurogenesis, gene expression and the ability of neurons to respond to stressful, potentially lethal, conditions (Beaulieu et al. 2007b). In suicide victims, the PI3-K/Akt signaling pathway was compromised in the occipital cortex with a significant decrease in Akt1 activity and other upstream effectors (Hsiung et al. 2003). In addition, some authors showed an alteration in the kinase activity of Akt1 and Gsk-3 in depressed subjects independent of suicidal behavior (Karege et al. 2007). The identification of a novel AKT1-binding protein (AKTIP or Ft1) has also pointed to an important new target for investigation, because of its interaction with Akt1, enhancing the phosphorylation of both regulatory sites by promoting its interaction with the upstream kinase PDK1 (Remy & Michnick 2004). Furthermore, the modulation of Akt1 activity by AKTIP has a strong effect on modulating or integrating signaling pathways that act on Akt1 activation.

The association of AKT1 and AKTIP gene polymorphisms with BD suicide attempters has not yet been investigated. In this study, we characterized clinically BD suicide attempters and tested the hypothesis that the presence/interaction of AKT1 and AKTIP genetic polymorphisms is associated with suicidal behavior in the population with BD.

Material and methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Material and methods
  5. Results
  6. Discussion
  7. References
  8. Acknowledgments

Subjects and diagnostic assessment

The subjects were 273 patients diagnosed with BD (I or II) (age = 41.4 ± 12.9) following Diagnostic and Statistical Manual of Mental Disorders, 4th edition criteria. These patients were randomly selected from the Bipolar Treatment Unit of the Instituto de Previdência do Estado de Minas Gerais during a 2-year period. The local Ethical Committee approved the study, and the subjects provided written informed consent in accordance with the 1975 Helsinki Declaration.

Suicidal behavior was assessed as previously described (Corrêa et al. 2002). A psychiatrist, blind to any experimental results, using a semi-structured interview (MINI-Plus) (Amorim 2000), a review of medical records and a supplementary interview with at least a close relative member, assessed lifetime suicide attempts. A suicide attempt was defined as a conscious intent of the patient to end his/her own life, through means that the patient thought could have resulted in death (Asberg et al. 1976). The suicide attempt methods were classified as non-violent (e.g. drug overdose) or violent (e.g. cutting beyond a superficial scratch, jumping from a height, shooting, hanging) (Coccaro et al. 1989).

Sample collection and single-nucleotide polymorphism genotyping

Peripheral blood was collected in EDTA vacuum tubes from all participating individuals after obtaining their written consent. Genomic DNA extraction was performed from whole blood using the high-salt method of Lahiri and Nurnberger (Lahiri & Nurnberger, 1991). HapMap database as well as previous published literature were used for selecting tagging single-nucleotide polymorphisms (SNPs) covering AKT1 and AKTIP genes; 50 ng of DNA each was used for TaqMan SNP genotyping assays (rs2494731, rs3803304, rs3730358, rs10149779, rs2494746, rs1130214, rs2498784, rs9302648 and rs7189819) according to Applied Biosystems protocol (Foster City, CA, USA) (Table 1). Fluorescence was read after completion of polymerase chain reaction cycles using a Stratagene Mx3005P QPCR System (La Jolla, CA, USA). A number of quality control measures were implemented to ensure the accuracy of the data collected.

Table 1.  Position and HWE of seven AKT1 and two AKTIP SNPs used in this study
SNP nameChromosome positionGeneGene locationAncestral alleleHWpval*
  1. *HWpval: HWE P value. χ2 tests.

rs2494731104308725AKT1Intron 12C0.7599
rs3803304104310191AKT1Intron 11G0.8491
rs3730358104317452AKT1Intron 3C0.9636
rs10149779104322131AKT1Intron 2G0.1982
rs2494746104328764AKT1Intron 2G0.2113
rs1130214104330779AKT1Intron 1G0.0106
rs930264852085282AKTIPIntron 8T0.0929
rs718981952093846AKTIPIntron 1C0.5795

Statistical analysis

Comparison of quantitative variables was performed using Student's t-test. Categorical variables and the genotype frequency were also compared between groups with χ2 tests. spss v.15.0 (Chicago, IL, USA) was used for statistical analysis (Souza et al. 2008). Allele and haplotype association analysis was performed using UNPHASED (v.3.0.13) (Dudbridge 2008). The software implements a retrospective likelihood test (the probability of observing genotypes given phenotypes) using a multinomial logistic model. To evaluate pairwise linkage disequilibrium (LD) matrices between each SNP and to examine the LD block structure and genotype deviation from the Hardy–Weinberg equilibrium (HWE), haploview 4.1 was used (Barrett et al. 2005). We used D′ to describe the magnitude of LD and χ2 values for allelic association to determine if the D′-value was statistically significant (if the null hypothesis of no LD could be rejected). We performed 1000 permutations in each test for estimating the global significance of the results for all haplotype analysis and to validate the expectation-maximization values. All tests were two tailed, and P level for significance was set at 0.05. Gene–gene interactions between AKT1 and AKTIP were analyzed using the mdr software (v.2.0.0) (Moore 2004). In the configuration file, 10-fold cross-validation was defined and the threshold ratio was set at 1.0. The model that maximized cross-validation consistency and minimized prediction error was selected (best model), and the null hypothesis was rejected when the upper-tail Monte Carlo P value derived from the permutation test was ≤0.05.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Material and methods
  5. Results
  6. Discussion
  7. References
  8. Acknowledgments

Demographic and clinical data describing the subjects are listed in Table 2. Statistical differences (P < 0.05) were found between genders and among the diagnostic subgroups for the variables tested.

Table 2.  Demographic and clinical characteristics in 273 bipolar patients with and without suicidal behavior
VariablesGenderPSuicide attemptsPViolent suicide attemptsPTotal
  1. Results shown as mean (standard deviation), where appropriate. Student's t-tests and χ2 tests were used to compare the quantitative and categorical variables between groups, respectively. A significant association is indicated in bold.

 Male (30.6%)Female (69.4%) YesNo YesNo  
Age43.8 (±13.9)40.5 (±12.3)0.15138.01 (±12.03)44.66 (±12.86)< 0.00134.89 (±11.92)40.50 (±1.70)0.04041.4 (±12.9)
Total education (years)1.9 (±.7)2.0 (±.8)0.7542.05 (±.66)1.99 (±.79)0.6151.92 (±.75)2.16 (±.57)0.1432.0 (±.77)
Cigarettes/day8.8 (±13.8)4.9 (±11.4)0.1657.10 (±12.66)5.49 (±12.03)0.5039.90 (±14.15)4.15 (±10.42)0.1276.0 (±12.2)
Years until treatment7.4 (±10.2)7.2 (±8.1)0.9256.67 (±7.44)7.58 (±9.22)0.6745.60 (±6.72)7.43 (±8.07)0.5157.3 (±8.6)
Age of first psychiatric hospitalization18.6 (±13.9)21.9 (±16.0)0.16123.87 (±12.44)18.64 (±17.24)0.01924.09 (±9.81)23.71 (±14.11)0.90520.9 (±15.4)
Age of first manic episode25.0 (±11.5)27.6 (±11.2)0.16424.53 (±9.24)28.91 (±12.59)0.01020.48 (±6.38)27.83 (±9.97)< 0.00126.9 (±11.4)
Age of first depressive episode21.0 (±12.2)21.0 (±11.5)0.99620.18 (±8.95)21.85 (±13.67)0.34918.59 (±9.22)21.34 (±8.68)0.18921.1 (±11.7)
Number of manic episodes10.2 (±8.6)10.9 (±11.0)0.71913.39 (±11.26)8.46 (±8.88)0.00414.94 (±11.62)12.13 (±10.95)0.27610.7 (±10.2)
Number of depressive episodes6.0 (±5.34)7.7 (±6.4)0.1819.10 (±6.32)5.27 (±5.27)< 0.00110.50 (±6.47)8.11 (±6.10)0.1147.2 (±6.1)
Number of suicide attempts2.3 (±1.6)2.8 (±1.8)0.2553.61 (±1.88)1.98 (±1.24)< 0.0012.7 (±1.7)
History of alcohol abuse/dependence46.625.00.00345.819.6< 0.00158.335.40.03731.7
History of drug abuse/dependence25.512.10.02525.09.10.00427.322.90.65516.2
History of psychotic symptoms86.778.10.48882.685.70.77881.890.90.53444.7
Family history37.560.00.04354.850.00.66545.560.00.43651.7
Suicide attempts36.249.20.09745.2
Violent attempts40.942.90.87442.4

The whole sample was in HWE for nine SNPs (Table 1), as well as separately in suicide attempters and in non-suicide attempters (data not shown), except for rs1130214 in all comparison groups. Allele and genotype frequency is shown in Table 3. One AKT1 marker showed association with suicide attempts [rs1130214, P < 0.05; odds ratio (OR ) = 0.621 (0.386–0.990)] and violent attempts [rs2494746, P < 0.05; OR = 0.413 (0.172–0.994)]. One out of the seven tested markers of AKT1 attained significant genotype association with violent suicide attempts (rs2494731; P < 0.05). After adjustment by the permutation procedure, we found significant association only of the rs2494731 marker (P < 0.05) with violent suicide attempts group (adjusted rs1130214 and rs2494746 P value > 0.05 were not significant). The allelic or genotype distribution of all other AKT1 SNPs showed no differences between groups (Table 3). In order to decrease the risk of negative associations, we used haplotype analysis that is more powerful and can provide more information in an association study. In individual haplotype tests, examination of the distribution differences for each haplotype showed that distinct haplotypes differed significantly between suicide attempters and non-suicide attempters. The most common haplotypes of up to a window of four SNPs are shown in Table 4. The most frequent haplotype in suicide attempters was haplotype ‘E’ [P < 0.05; OR = 1.749 (1.190–2.569)], whereas the most frequent in non-suicide attempters was haplotype ‘B’ [P < 0.01; OR = 0.5415 (0.324–0.903; Table 4)]. After correction among haplotype combinations, just the two-marker window (B) remained significant (Table 4). Similarly, AKT1 haplotypes were also strongly associated with the type of suicide attempt (Table 4). AKTIP markers (rs9302648 and rs7189819) were not associated with non-violent or violent suicide attempts both as single markers, single genotypes (Table 3) or haplotype (data not shown). Statistical analysis showed no genotypic or allelic association with gender (data not shown).

Table 3.  Genotype and allele frequency of AKT1 and AKTIP polymorphisms in BD patients with and without suicidal behavior
SNP nameSuicide attemptsType of suicide attemptsTotal (%)
  1. *Genotype and allelic P value for each marker, respectively (χ2 tests). A significant association is indicated in bold.

CG0.4080.465 0.2350.532 0.448
C0.6110.596 0.6470.586 0.598
G0.3890.404 0.3530.414 0.402
CG0.3700.361 0.3240.404 0.373
C0.2470.263 0.2210.266 0.246
G0.7530.747 0.7790.734 0.754
CT0.2440.305 0.1560.305 0.294
C0.8270.836 0.8590.805 0.829
T0.1730.164 0.1410.195 0.171
GA0.3120.323 0.2120.383 0.347
G0.7180.646 0.7430.703 0.663
A0.2820.354 0.2570.297 0.337
CG0.2660.232 0.1820.326 0.247
C0.8040.789 0.8780.750 0.806
G0.1960.211 0.1220.250 0.194
GT0.3380.385 0.2940.391 0.402
G0.7440.651 0.7360.739 0.668
T0.2560.349 0.2640.261 0.332
CT0.1730.186 0.1760.170 0.174
C0.9010.866 0.9110.893 0.899
T0.0990.134 0.0820.107 0.111
TG0.5950.484 0.5000.689 0.543
T0.6120.596 0.6320.588 0.596
G0.3880.404 0.3680.412 0.404
TC0.4220.436 0.3890.468 0.462
T0.4160.376 0.4440.383 0.408
C0.5840.624 0.5560.617 0.592
Table 4.  Haplotype frequency in bipolar patients with suicidal behavior
Haplotype IDrs2498784rs1130214rs2494746rs2494731rs3730358Suicide attemptersNon-suicide attemptersPP*
  1. *P: adjusted P value from 1000 permutation tests. Only two- to four-marker haplotype analyses that showed a significant association (indicated in bold) with suicidal behavior (except haplotype A); χ2 tests.

      Violent attemptNon-violent attemptPP*

Pairwise LD is shown in Fig. 1. This analysis showed one LD block in the AKT1 gene comprising two SNPs. The remaining five SNPs were not included in any block. A significant LD in the whole sample and in subgroups was observed for rs1130214 and rs2494746 (D′ = 1.0). However, there are no significant differences in the rs1130214–rs2494746 (Table 4) haplotype frequency between suicide attempters and non-suicide attempters (global permutation P value > 0.05) or related to the type of attempt in either male or female subjects (data not shown). Similarly, although there was a moderate LD in AKTIP rs9302648 and rs7189819 polymorphisms (D′ = 0.828), there were no significant differences in the haplotype frequency.


Figure 1. LD block structure across AKT1 gene. The upper panel shows the location of seven polymorphisms in AKT1 gene and lower panel shows the output of haploview (v.4.1). LD plot, each square (with D′ values written within the box), represents a pairwise LD relationship between the two SNPs. Red squares indicate statistically significant LD between the pair of SNPs as measured by the D′ statistic. Darker colors of red indicate higher values of D′, up to a maximum of 1, and white squares indicate pairwise D′ values of < 1 with no statistically significant evidence of LD. The block generated (block 1) under confidence interval algorithm of haploview is marked. This block is constituted by two SNPs (rs2494746 and rs1130214) in introns 1 and 2, respectively

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We examined the possible epistasis of AKT1 and AKTIP SNPs in conferring susceptibility to suicide behavior. The best model of AKT1 and AKTIP interaction obtained from mdr analysis among the selected models to increase the risk of suicide attempt was rs1130214–rs9302648; however, this did not reach statistical significance (cross-validation consistency: 3/10; training accuracy: 0.589; testing accuracy: 0.456 and P > 0.05).


  1. Top of page
  2. Abstract
  3. Introduction
  4. Material and methods
  5. Results
  6. Discussion
  7. References
  8. Acknowledgments

To our knowledge, this is the first report of an association analysis between SNPs in AKT1 and AKTIP genes and suicide attempts and represents the first study to investigate AKTIP polymorphisms in a human population. A significant association was detected in AKT1 single markers, genotype and haplotype tests in 273 bipolar patients. In addition, the demographic and clinical data suggest that in bipolar patients the number of manic and depressive episodes, alcoholism, drug abuse, age, age of first psychiatric hospitalization and manic episode, among others, may be risk factors for suicide attempt (Table 2).

In the single SNP analysis, we found a statistically significant association of SNP rs1130214 (P < 0.05) with suicide attempt and rs2494746 (P < 0.05) with violent attempt, and genotype analysis of rs2494731 (P < 0.05) showed association with violent attempt even after 1000 permutation tests (adjusted P value < 0.05). The studied SNPs are intronic (Tag SNPs), thus not affecting amino acid coding and probably do not directly affect protein function; the observed associations between suicide behavior risk and AKT1 SNPs should be interpreted as the presence of LD between these SNPs and other SNPs in exons (resulting in functional polymorphism) or in regulatory regions (affecting the expression of these genes). All SNPs studied showed a minor allele frequency of more than 5%, which makes them comparable with common disease susceptibility polymorphisms (Dunning et al. 2000) with power to detect LD (Terwilliger et al. 1998). A strong LD between SNPs rs2494746 and rs1130214 was showed, but there was no significant difference in haplotype frequency between suicide attempters and non-suicide attempters.

As shown in Table 4, there were significant differences between the two- to four-marker haplotype analysis among the compared groups. Four haplotypes differed significantly in their distribution between suicide attempters and non-attempters. The most frequent haplotype (C) was more often observed in the suicide attempters group (0.5438 vs. 0.3217) and a second haplotype (E) was only present in this group with a frequency of 0.0540. The difference in distribution of these two haplotypes was no longer significant after correction with permutation. Alternatively, a further haplotype (B) was most frequent in non-suicide attempters (0.3311 vs. 0.2288) and the significance of this observation remained after permutation tests (P < 0.05) (Table 4). These results imply that haplotypes C, D and E carry one or more genetic variants predisposing bipolar patients to suicide attempt. However, it is also possible that the other haplotype (B) contain variants that are protective against the suicide behavior.

The results of our study suggest that polymorphisms in the AKT1 gene and distinct haplotypes of four SNPs represent risk factors in the occurrence of suicide behavior in bipolar patients. However, the possibility that a susceptibility locus lies in others parts of the AKT1 gene needs to be examined. It could also be probable that the observed relation between AKT1 gene polymorphisms and non-violent suicide attempt, as well as violent suicide attempt, represents a partially common pathway in the etiology of these disorders, but with final independent effects on the development of the behavior. Serotonin neurotransmission abnormalities have been related to suicide, but results with the dopaminergic system are more limited. An in vivo investigation of homovanillic acid (HVA), the main dopamine (DA) metabolite, in the cerebrospinal fluid of depressed suicide attempters showed reduced HVA levels, but not in depressed non-attempters (Sher et al. 2006), suggesting a relation between DA and suicide but not depression. In this study, we used target genes that encode key proteins for signaling in both systems (dopaminergic and serotonergic), which support the hypothesis that genetic variations in AKT1 and AKTIP are associated with suicide behavior. It has also to be mentioned that possible interactions between AKT1 and other genes should be considered in disorders related to disturbances of these systems.

Long-term treatment with lithium provides more consistent and compelling evidence of reduced suicidal risk than any other treatment. A comprehensive meta-analysis indicates that lithium has major beneficial effects in reducing both suicides and suicide attempts in patients diagnosed with BD (Baldessarini et al. 2006). This finding may involve Akt1 signaling. As shown earlier (Beaulieu et al. 2008), lithium exerts at least some of its behavioral effects through the disruption of a signaling complex composed of Akt1, β-arrestin 2 and protein phosphatase 2A in animal models. These observations provide a mechanism by which lithium can modulate Akt1 signaling and associated behaviors. The Akt1 pathway plays an important role in neuronal survival, plasticity, synaptic activity and is a common signal-transduction mechanism for growth factors (Sale & Sale 2008). Thus, disrupted Akt1 signaling caused by genetic alterations may mediate the balance between neurotrofic and degenerative processes in BD patients and also a possible impact on clinical improvement with mood stabilizers.

Several studies have pointed to the view that genes act in concert, rather than in isolation (Hill et al. 2008; Motsinger & Ritchie 2006). Therefore, we evaluated whether gene-gene interaction between AKT1 and AKTIP, which encoded proteins with biological interactions, are associated with suicide attempt. We did not observe an association between violent or non-violent suicide attempts and AKTIP polymorphisms, in the same way we did not find an interaction between AKTIP and AKT1 polymorphisms.

Three caveats must be considered when interpreting our results. First, we did not perform a systematic mutation search using large Brazilian BD samples. Second, other candidate genes of the AKT1 signaling cascade should be studied in considering locus heterogeneity. Third, our positive results might be derived from sample bias because of population stratification (especially in Brazil, there are no reliable criteria for ethnic segregation) and non-age-matched samples.

Understanding the interactive relationship between risk and protective factors in suicidal behavior is a challenge for suicide prevention. Unfortunately, most of the genetic studies relating to suicidal behavior are poor in terms of their clinical characterization, which limits the interpretation of results. We also found that several clinical and demographic variables were associated with greater likelihood of suicidal behavior in bipolar patients. Our results are consistent with the view that patients with BD are highly vulnerable to suicidal behavior (45.2% in our sample). However, women were not more likely to attempt suicide than men (Fiedorowicz et al. 2008; Grunebaum et al. 2006; Kawa et al. 2005) as in non-bipolar patients (Isometsa & Lonnqvist 1998) and bipolar patients (Engstrom et al. 2004). BD non-suicide attempters were first hospitalized at a mean age of 18, whereas for suicide attempters hospitalization accessed 5 years later. Patients with a lifetime history of suicide attempts are younger than non-suicide attempts and also exhibited a greater number of manic and depressive episodes, and greater history of abuse/dependence on alcohol and other drugs. These data suggest that treatment adherence is poor in suicide attempters, possibly because of comorbidities. Substance abuse is associated with suicide attempts (Comtois et al. 2004), perhaps because of impulsive traits or increased impulsivity because of intoxication. Suicide attempters also show a greater number of depressive and manic episodes (Grunebaum et al. 2006). This may be in part because of a risk effect of mood elevation, although some authors did not find evidence that grandiose delusions are associated with suicide attempts in BD patients (Grunebaum et al. 2001).

In summary, our data suggest that demographic and clinical characteristics and AKT1 single markers and haplotypes, but not AKTIP polymorphisms or interactions between AKT1 and AKTIP, are associated with increased risk for suicidal behavior in bipolar patients in a Brazilian sample. Further studies in different psychiatric disorders should be carried out in order to verify if these AKT1 polymorphisms are associated with suicide in other clinical contexts.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Material and methods
  5. Results
  6. Discussion
  7. References
  8. Acknowledgments
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  1. Top of page
  2. Abstract
  3. Introduction
  4. Material and methods
  5. Results
  6. Discussion
  7. References
  8. Acknowledgments

Financial support was provided by CNPq, FAPEMIG and John Simon Guggenheim Memorial Foundation. M.A.R.-S. and L.A.De M. are CNPq research fellows. All authors declare that they have no conflicts of interest. The sponsor had no further role in study design, in the collection, analysis and interpretation of data, in the writing of the report, and the decision to submit the article.