Background

Propofol is a widely used intravenous hypnotic. Dosing is based mostly on weight, with great interindividual variation in consumption. Suggested factors affecting propofol requirements include age, sex, ethnicity, anxiety, alcohol consumption, smoking, and concomitant valproate use. Genetic factors have not been widely explored.

Methods

This study considered 1,000 women undergoing breast cancer surgery under propofol and remifentanil anesthesia. Depth of anesthesia was monitored with State Entropy (GE Healthcare, Finland). Propofol requirements during surgery were recorded. DNA from blood was genotyped with a genome-wide array. A multivariable linear regression model was used to assess the relevance of clinical variables and select those to be used as covariates in a genome-wide association study. Imputed genotype data were used to explore selected loci further. In silico functional annotation was used to explore possible consequences of the discovered genetic variants. Additionally, previously reported genetic associations from candidate gene studies were tested.

Results

Body mass index, smoking status, alcohol use, remifentanil dose (ln[mg · kg−1 · min−1]), and average State Entropy during surgery remained statistically significant in the multivariable model. Two loci reached genome-wide significance (P < 5 × 10−8). The most significant associations were for single-nucleotide polymorphisms rs997989 (30 kb from ROBO3), likely affecting expression of another nearby gene, FEZ1, and rs9518419, close to NALCN (sodium leak channel); rs10512538 near KCNJ2 encoding the Kir2.1 potassium channel showed suggestive association (P = 4.7 × 10−7). None of these single-nucleotide polymorphisms are coding variants but possibly affect the regulation of nearby genes. None of the single-nucleotide polymorphisms previously reported as affecting propofol pharmacokinetics or pharmacodynamics showed association in the data.

Conclusions

In this first genome-wide association study exploring propofol requirements, This study discovered novel genetic associations suggesting new biologically relevant pathways for propofol and general anesthesia. The roles of the gene products of ROBO3/FEZ1, NALCN, and KCNJ2 in propofol anesthesia warrant further studies.

Editor’s Perspective
What We Already Know about This Topic
  • Some of the interindividual variability in propofol pharmacokinetics and pharmacodynamics remains unexplained and may be due to genetic variability

  • Selected genetic variants in genes likely involved in propofol pharmacodynamics and pharmacokinetics have been explored in hypothesis-driven candidate gene association studies focused on genes encoding γ-aminobutyric acid type A receptors and UGT1A9, CYP2B6, and CYP2C9 genes, but results have been inconsistent

What This Article Tells Us That Is New
  • In a genome-wide association study exploring propofol requirements, novel genetic associations were discovered suggesting new biologically relevant pathways for propofol and general anesthesia

  • The identified single-nucleotide polymorphisms may affect nearby genes: ROBO3/FEZ1, NALCN (sodium channel), and KCNJ2 (inward rectifier potassium channel)

Propofol (2,6-di-isopropylphenol) is a widely used intravenous hypnotic.1  Its dosing is mostly calculated using patients’ weights. Population pharmacokinetic studies show that propofol clearance is strongly associated with total body weight.2–4  However, this does not explain all observed pharmacokinetic variability nor the interindividual differences in propofol requirements (which also depend on the pharmacodynamic characteristics of propofol). Anxiety, regular alcohol consumption, smoking, sex, age, ethnicity, and concomitant use of valproate and drugs that lower cardiac output are associated with propofol requirements.5–12 

After intravenous administration, propofol rapidly reaches its target molecules, extensively binding to plasma proteins.1  Cardiac output affects both speed of induction and hepatic extraction rate. Propofol has high first-pass metabolism with a blood extraction ratio of 90%. Metabolism occurs mostly by conjugation with glucuronide by uridine 5′-diphosphate glucuronosyltransferase. About 30% of propofol is hydroxylated by cytochrome P450 isoforms, mostly CYP2B6 and CYP2C9.1  Liver is the main site of metabolism, but kidneys, small intestine, and lungs also have metabolic activity. Propofol metabolites are excreted in urine.1 

The pharmacologic effect of propofol is mediated mainly through γ-aminobutyric acid type A (GABAA) receptors.13  Propofol binds to postsynaptic receptors and causes an inward current of chloride ions by opening the associated channel. This current causes hyperpolarization of postsynaptic neurons, inhibiting depolarization and new action potentials in a dose-dependent manner: low concentrations enhance the effects of endogenous GABA, but higher concentrations directly activate GABAA receptors.14  Inhaled anesthetics have several recognized targets that modulate the electrical activity of cells. It has been stated that the modulation of GABAA receptors is neither necessary nor sufficient to account for every effect of all general anesthetics.15  Interactions between propofol and N-methyl-d-aspartate and α-amino-3-hydroxy-5-methyl-4-isoxazole proprionate (AMPA) receptors, voltage-gated sodium channels, HCN1 (hyperpolarization-activated cyclic nucleotide-gated potassium channel 1), kinesin, transient receptor potential (TRP) channels TRPA1 and TRPV1, SIRT2 (sirtuin isoform 2), SNARE proteins (soluble N-ethylmaleimide-sensitive factor attachment protein receptors [neurotransmitter release machinery]), and mitochondria have been demonstrated.16–23  They do not all necessarily mediate the hypnotic effect but may altogether have clinical significance.

The source of interindividual variability in propofol dose requirements is not fully known but may be due to pharmacodynamic and/or pharmacokinetic factors. Single-nucleotide polymorphisms in our genomes account for up to 80% of individual features.24  A single-nucleotide polymorphism can alter the function of a gene by affecting its expression or the structure of its coded protein. Relevant genes and some associated single-nucleotide polymorphisms involved in propofol pharmacodynamics and pharmacokinetics have been explored in hypothesis-driven candidate gene association studies focused on genes associated with GABAA receptors,25–27 UGT1A9, CYP2B6, and CYP2C9,28–30  but results have been inconsistent. The aim of this study was to identify genetic factors associated with dose requirements of propofol (mg · kg−1 · min−1) in a pseudo-steady-state of general anesthesia (Entropy 40-60, GE Healthcare, Finland) by performing a genome-wide association study in a cohort of 1,000 women of Finnish origin undergoing breast cancer surgery.

BrePainGen is a prospective study exploring the role of genetics in anesthetic agent requirements, postoperative nausea and vomiting, and persistent postsurgical pain. Its protocol was approved by the coordinating ethics committee (136/E0/2006) and the Ethics Committee of the Department of Surgery (Dnro 148/E6/05) of the Hospital District of Helsinki and Uusimaa. Recruitment and the criteria for exclusion are described in figure 1. The study cohort comprised 1,000 Finnish women undergoing breast cancer surgery at the Helsinki University Hospital between August 1, 2006, and December 31, 2010. After informed consent, patients were interviewed for background factors.

Fig. 1.

Participant recruitment and exclusion process.

Fig. 1.

Participant recruitment and exclusion process.

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Anesthesia Protocol

The patients were premedicated with oral paracetamol (1 g) and diazepam (2.5 to 15 mg). Anesthesia was induced with propofol (2 to 3 mg/kg), remifentanil infusion (0.2 mg · kg−1 · min−1), and rocuronium (0.6 mg/kg). During surgery, anesthesia was maintained with a propofol infusion of 50 to 100 µg · kg−1 · min−1 to keep State Entropy (M-entropy module for S/5TM anesthesia monitor, GE Healthcare) at the level of 50 ± 5. Remifentanil infusion was titrated for rates of 0.05 to 0.25 µg · kg−1 · min−1 to keep systolic blood pressure (SBP) at ±15% of the baseline value minus 20 mmHg. The neuromuscular block was maintained throughout the surgery with rocuronium boluses of 10 mg to keep the train-of-four ratio at 0 to 10%. Mechanical ventilation was adjusted to normocapnia with a 1:1 mixture of oxygen:nitrous oxide. During closure of the skin, remifentanil infusion was stopped, and boluses of fentanyl (1 µg/kg), ondansetron (4 mg), and droperidol (0.01 mg/kg) were administered intravenously. Neuromuscular block was reversed with neostigmine (2.5 mg) and glycopyrrolate (0.5 mg).

During anesthesia, the values for propofol and remifentanil consumption were recorded during surgery and in total. Consumption during surgery was selected for further analysis as being the best standardized variable because it did not include the induction phase or the time spent under anesthesia without surgical stimulation. We standardized the doses of propofol and remifentanil for weight (kg) and duration of surgery (min), recording them as natural logarithms (ln).

Genotyping

Before the patient woke from anesthesia, a blood specimen was drawn for DNA isolation. DNA was extracted from peripheral blood using Autopure LS Automated DNA purification instrument (Gentra Systems, Inc., USA). Genotype data were produced at the Wellcome Sanger Institute (United Kingdom) on the Human OmniExpress Illumina BeadChip (Illumina, Inc., USA), blinded to phenotypic information.

Sample quality control procedures have been described in detail earlier.31  Single-nucleotide polymorphisms were filtered based on minor allele frequency (greater than 0.01), Hardy–Weinberg equilibrium (P > 1 × 10−6), and success rate (greater than 0.97). The mean genotyping success rate was 0.997. After quality control, genotyping data were available for 926 of the 1,000 participants.

Statistical Analysis

Statistical analyses and data management were conducted with IBM SPSS Software version 28.0 and R version 3.6.2. First, we performed univariate testing with linear regression between clinical variables and the propofol requirements. Explored clinical factors were age, body mass index (BMI), American Society of Anesthesiologists physical status, current smoking (yes/no), use of alcohol (abstinent or weekly use), alcoholism in the family, consumption of remifentanil during anesthesia (ln[mg · kg−1 · min−1]), average State Entropy during surgery, type of surgery in the axilla (sentinel node biopsy and/or evacuation), type of breast surgery (resection or mastectomy), chronic pain condition, and red hair pigment.

After single variable testing, we ran multivariable linear regression analyses based on variables with statistically significant (P < 0.05) associations with propofol requirements. Homoscedasticity was observed by visual inspection of a plot of studentized residuals versus unstandardized predicted values. Multicollinearity was assessed (tolerance values greater than 0.1, and variance inflation factor values less than 2). There were nine studentized residuals greater than ±3 but no leverage values greater than 0.2 or values for Cook’s distance above 1. Outliers, after checking for legitimacy, were kept in the analyses. The assumption of normality was met, as assessed by histogram.

The clinically relevant factors explaining propofol consumption, and the first five dimensions from multidimensional scaling of genotype data (to consider a possible hidden population structure) were used as covariates in the genome-wide association study to control the influence of clinical factors. Additionally, we included the use of beta blockers, calcium channel blockers, and valproate medications as covariates because they might affect propofol requirements.

The genome-wide association study was conducted with an additive linear regression model with PLINK 1.07.32  Associations between propofol dose (ln[mg · kg−1 · min−1]) and 653,034 single-nucleotide polymorphisms were tested. Standard thresholds for genome-wide statistical significance (P < 5 × 10−8) and for suggestive association (P < 1 × 10−5) were applied.33,34  Possible biologic functions of variants with P < 5 × 10−6 were further inspected, using the Genotype-Tissue Expression (GTEx),35–37  Open Targets,38  Ensembl,39  and RegulomeDB40,41  databases.

To further study the genetic signals obtained from the genome-wide association study, we used imputed genetic data to analyze associations with those single-nucleotide polymorphisms not found on the Human OmniExpress Illumina BeadChip used for genotyping our cohort. Genomic data were prephased with Eagle, version 2.4.42  Genotypes were imputed using Beagle 4.1 and the population-specific Sequencing Initiative Suomi panel as imputation references.43,44  Poorly imputed variants were excluded (INFO less than 0.7). Imputed genotype data were processed with PLINK 2.

Demographics and Clinical Variables

Details of the patients and anesthesia are presented in table 1. Additional data about illnesses and medications are reported in supplemental table 1 (https://links.lww.com/ALN/D553).

Table 1.

Characteristics of Participants

Characteristics of Participants
Characteristics of Participants

Multiple linear regression analysis for clinical factors is presented in table 2. Assumptions of linear regression analysis were met. Clinical variables that predicted higher propofol requirements were current smoking, alcohol consumption, high remifentanil dose, and State Entropy. High BMI predicted lower propofol requirements. Age and chronic pain were kept in the model because they increased the model’s ability to predict propofol requirement (R2), but they were not statistically significant.

Table 2.

Multiple Linear Regression Model with Clinical Variables

Multiple Linear Regression Model with Clinical Variables
Multiple Linear Regression Model with Clinical Variables

Genome-wide Association Study

A total of 926 patients were successfully genotyped and included in the genetic analysis. The results of the genome-wide association study are presented in table 3 and figure 2. LocusZoom plots are in supplemental figure 1 (https://links.lww.com/ALN/D553). The genome-wide association study summary statistics are available in supplemental data 2 (https://links.lww.com/ALN/D554).

Table 3.

Results of Genome-wide Association Study Analysis with Propofol Requirements

Results of Genome-wide Association Study Analysis with Propofol Requirements
Results of Genome-wide Association Study Analysis with Propofol Requirements
Fig. 2.

Manhattan plot of the genome-wide association study results showing all the tested single-nucleotide polymorphisms in their respective chromosomes and −log10 (P values). The horizontal line shows the genome-wide significance level of P < 5 × 10−8. Chromosome 11 contains a locus with a statistically significant association with the studied phenotype (ln[propofol dose mg · kg−1 · min−1]). Chromosomes 13 and 17 contain loci showing near genome-wide associations. The covariates used in genome-wide association study were age, body mass index, remifentanil consumption (ln[mg · kg−1 · min−1]), current smoking, alcohol consumption, State Entropy, chronic pain, the use of beta blockers, calcium channel blockers, or valproate and genetic multidimensional scaling factors to correct for possible population substructure. Below is the quantile–quantile plot that presents the expected (x) −log10(P value) against observed (y) −log10(P value). The quantile–quantile plot of the genome-wide association study shows deviation of the observed P value from the expected P value (null hypothesis). Here, the observed P values depart from the expected P values (reject null hypothesis).

Fig. 2.

Manhattan plot of the genome-wide association study results showing all the tested single-nucleotide polymorphisms in their respective chromosomes and −log10 (P values). The horizontal line shows the genome-wide significance level of P < 5 × 10−8. Chromosome 11 contains a locus with a statistically significant association with the studied phenotype (ln[propofol dose mg · kg−1 · min−1]). Chromosomes 13 and 17 contain loci showing near genome-wide associations. The covariates used in genome-wide association study were age, body mass index, remifentanil consumption (ln[mg · kg−1 · min−1]), current smoking, alcohol consumption, State Entropy, chronic pain, the use of beta blockers, calcium channel blockers, or valproate and genetic multidimensional scaling factors to correct for possible population substructure. Below is the quantile–quantile plot that presents the expected (x) −log10(P value) against observed (y) −log10(P value). The quantile–quantile plot of the genome-wide association study shows deviation of the observed P value from the expected P value (null hypothesis). Here, the observed P values depart from the expected P values (reject null hypothesis).

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In the genome-wide association study, one noncoding transcript exon variant on chromosome 11, rs997989, remained with genome-wide significance (P = 3.5 × 10−8; β, 0.13). The results indicate that 64 variants of 54 genes showed suggestive association (P < 1 × 10−5; supplemental table 2, https://links.lww.com/ALN/D553). Of these, four had P values of < 5 × 10−6 and are listed in table 3. They include variants rs10512538 and rs9891877 on chromosome 17 (linkage disequilibrium r2 = 0.76; D′ = 0.96), rs9300663 on chromosome 13, and rs9395094 on chromosome 6. For all variants except rs9395094, the minor allele increased the propofol requirements (table 3).

We further analyzed these four loci on chromosomes 6, 11, 13, and 17 by conducting another association analysis with imputed genotype data to identify potential functional variants or variants with stronger associations with propofol consumption. Corresponding LocusZoom plots are in supplemental figure 2 (https://links.lww.com/ALN/D553). On chromosome 11, there were two additional variants with genome-wide significant associations: rs997990 and rs56381751, whereas three others had P values of < 5 × 10−6. These three variants in chromosome 11 were in almost complete linkage disequilibrium with each other. On chromosome 13, one of the imputed single-nucleotide polymorphisms, rs9518419, reached genome-wide significance. On chromosomes 6 and 17, none of the tested variants had genome-wide significance, but chromosome 17 had nine variants with P values of < 5 × 10−6.

To evaluate the effect of covariates, another genome-wide association analysis was conducted without them (supplemental fig. 8 and supplemental tables 5 to 7, https://links.lww.com/ALN/D553). No genome-wide significant single-nucleotide polymorphisms associated with propofol consumption were identified.

In Silico Functional Annotation

Because none of our top single-nucleotide polymorphisms are coding variants, we hypothesized that they might affect regulation of nearby genes. Functional annotation using GTEx, Open Targets, Ensembl, and RegulomeDB was therefore performed.

Based on the RegulomeDB, variants rs997989 and rs997990 on chromosome 11 lie in a regulatory genomic region and are likely to affect transcription factor binding (score 1f; table 4). The nearest coding gene is ROBO3, whereas the variants are also located within MSANTD2 antisense RNA 1. Evidence retrieved from GTEx suggests that these variants most likely affect the expression of FEZ1. When only neural tissues were examined, a less significant association between these variants and SPA17 expression levels was seen. Variant rs56381751 is located within an enhancer region with evidence of its regulating the expression of FEZ1 (supplemental fig. 3, A to C, https://links.lww.com/ALN/D553).

Table 4.

In Silico Functional Annotation Results for the Variants Listed in Table 3A 

In Silico Functional Annotation Results for the Variants Listed in Table 3A
In Silico Functional Annotation Results for the Variants Listed in Table 3A

Variants rs9300663 and rs9518419 on chromosome 13 are located within introns of NALCN and ITGBL1, respectively. RegulomeDB and GTEx suggest that these variants are located within a transcription factor binding site and are likely to affect the expression of NALCN (score 1f). This gene is relatively highly expressed throughout the brain (supplemental fig. 4, A to E, https://links.lww.com/ALN/D553).

Variant rs9891877 is located near KCNJ2. RegulomeDB provided some support (score 2b) for a functional consequence for rs9891877 but not for the other variant located near KCNJ2, rs10512538 (score 5). GTEx did not include these variants (supplemental fig. 5, https://links.lww.com/ALN/D553). In addition, we found barely any functional data about variant rs9395094 in chromosome 6 (supplemental fig. 6, https://links.lww.com/ALN/D553).

Replication Attempt of Earlier Propofol Association Signals

A literature search for previous studies exploring genetics was performed using PubMed and Ovid Medline. The 21 studies found are listed in supplemental table 3 (https://links.lww.com/ALN/D553). No variant with earlier evidence of association with propofol consumption in GABRA1, GABRA2, 5HTA2A, SCN9A, UGT1A9, and CYP2B6 loci showed any association in our data (supplemental fig. 7 and supplemental table 4, https://links.lww.com/ALN/D553).

Our study, with standardized surgery and anesthesia protocol, shows BMI, current smoking, alcohol use, required remifentanil dose, and average State Entropy to be associated with propofol requirements. Genetic analyses highlighted regions close to NALCN, ROBO3/FEZ1, and KCNJ2 genes associated with propofol requirement, with the first two reaching genome-wide significance. This suggests that interindividual differences in propofol consumption could result from differences at receptor or ion channel level, as well as from biotransformation.

Clinical Factors

First, we investigated clinical factors predicting propofol requirements. Smoking and alcohol use are associated with higher requirements for surgical anesthesia, and higher BMI is associated with lower requirements for surgical anesthesia, as in our cohort.2,4,6,11  Surprisingly, higher remifentanil doses predicted higher propofol requirement, contradicting previous studies that found synergism in anesthesia.45,46  Our protocol set remifentanil infusion rates based on SBP, although remifentanil might also affect State Entropy. One interesting possibility is interindividual variability in synergy: patients homozygous (GG) for the A118G variant in the OPRM1 gene are reported not to show synergy between propofol and remifentanil.47  Possibly, we observed such association because propofol and remifentanil were titrated according to different variables (State Entropy and SBP), the observed connection being a titration paradox effect.

Naturally, as the primary guide to dosing, State Entropy was significantly associated with propofol consumption. Although the goal was to keep State Entropy at 50 ± 5, 51% of our patients had an average State Entropy less than 45, and 4% of patients had an average State Entropy less than 35, suggesting that although State Entropy was the guideline for dosing, clinicians considered other factors when assessing anesthesia. Thus, individuals were considered adequately anesthetized as evaluated by State Entropy or other methods.

Genetic Factors

Our genome-wide association study highlighted several regions with potentially relevant target genes. Because the lead single-nucleotide polymorphisms were not coding variants, we used in silico approaches and publicly available expression and regulation databases to identify the most likely target genes and biologic mechanisms behind these associations. Greater than 90% of genome-wide association study trait-associated single-nucleotide polymorphisms fall in noncoding regions. Thus, genome-wide association studies often report variants with possible regulatory functions, indicating the importance of more subtle regulatory mechanisms behind genetic association findings.38 

The strongest association was seen for a gene-rich chromosome 11 locus, near ROBO3: gene-expression data sets provide different results depending on tissue examined, so it is impossible to unambiguously decide which gene drives the association. RegulomeDB and GTEx suggest that variants rs997989, rs997990, and rs56381751 affect expression of FEZ1 (encoding a protein necessary for normal axonal bundling and elongation within axon bundles).48  In adult rats and mice, fez1 (mouse and rat ortholog) is expressed in the central nervous system, mainly in GABAergic inhibitory neurons.49  In Caenorhabditis elegans, with unc-76 mutants (FEZ1 ortholog), GABA-containing neurons fail to bundle.50  A rat study shows expression of fez1 to gradually increase during brain development, decreasing in the adult brain, preferentially expressed in dentate gyrus granule cells, a cell type undergoing neurogenesis in the adult rat brain.51  In the developing human fetal brain (postconception weeks 8 to 21), FEZ1 is highly expressed in mature glutamatergic cells and GABAergic neurons, although some GABAergic neurons showed reduced expression.52  Authors also reported that FEZ1 expression was closely coupled to expression of SNARE complex proteins. Interestingly, clinically relevant propofol concentrations inhibit presynaptic neurotransmitter release. There is some evidence of propofol impairing the formation of SNARE complex required for neuronal exocytosis by interacting with syntaxin 1A, a key member of the presynaptic release machinery.23 

For this locus, expression and transcript data also pointed to three genes: ROBO3 (nearest coding gene), a location within MSANTD2 antisense RNA 1, and association with expression of SPA17. ROBO1-3 proteins are immunoglobulin transmembrane receptors, targeted by Slit proteins 1 to 3, chemorepellents controlling axon midline crossing. Loss-of-function mutations in ROBO3 can result in failure of corticospinal and somatosensory axon tracts to cross the medulla midline.53,54  ROBOs are cell adhesion molecules able to discriminate GABAergic neurons.55  The related MSANTD2 gene seems relevant in neural development, being associated with autism spectrum disorders and schizophrenia.56–58 SPA17 promotes cell–cell adhesion.59 

Another statistically significant association was for rs9518419 on chromosome 13. RegulomeDB and GTEx data indicate this to be located within a transcription factor binding site regulating expression of voltage-independent transmembrane channel gene NALCN, expressed widely in the brain. This is vital: mice lacking nalcn (mouse ortholog) have severely disrupted respiratory rhythm, with newborns dying within 24 h.60  In humans, NALCN mutations can cause CLIFAHDD syndrome.61  NALCN regulates resting membrane potential and neuron excitability, generating spontaneous action potentials (“pacemaker activity”).62  Increased sensitivity of mutated NALCN orthologs (and auxiliary units unc-79 and unc-80) to anesthetics and ethanol has been reported.63–65  Interestingly, authors concluded that neurons expressing this channel were not the targets for anesthetics but were affecting their sensitivity.63  Also, increased sensitivity to sedative effects and respiratory depression has been reported in a child with a NALCN missense mutation showing increased sensitivity to sevoflurane sedation at subtherapeutic doses.66  rs9518419 is also located near ITGBL1, coding for a β-integrin–related protein. Integrins have been linked to synaptic plasticity and regulation of GABAergic synapses.67 

The third promising genome-wide association study finding was a locus on chromosome 17, near KCNJ2, showing suggestive association with propofol consumption. KCNJ2 encodes a transmembrane potassium channel Kir2.1.68  Kirs have an important role in baseline (“leakage”) potassium currents, especially when membrane potential is near the equilibrium for potassium.69  They participate in producing slow inhibitory postsynaptic potentials, thus regulating intrinsic excitability and synaptic transmission in the brain;70  their clinical importance has been thoroughly established. Kir2.1 is expressed in several brain areas.71,72  A case report describes a patient with Andersen–Tawil syndrome (caused by a KCNJ2 mutation) with persistent ventricular ectopy resolving during propofol infusion.73  Furthermore, Kir3.2 and 3.3 subtypes of GIRK (G protein–gated inwardly rectifying potassium channels) mediate inhibitory effects of GABAB and opioids (µ-opioid receptors on locus ceruleus neurons), promoting sedation.74–77  Interestingly, Kir2.1 channels are coexpressed with GABAB receptors in cerebellar granule cells, whereas dentate gyrus granule cells were the site of expression of fez1 in adult rats.51,78  Last, two studies reported that thiopental, an intravenous anesthetic targeting GABAA receptors, like propofol, inhibits Kir currents.79,80 

Of the 64 single-nucleotide polymorphisms showing suggestive association (supplemental table 2, https://links.lww.com/ALN/D553), several are located close to potentially relevant genes: rs4242187 in CPLX2 (encodes part of the SNARE complex, functioning in synaptic vesicle exocytosis); rs4240213 in KCNS3 (voltage-gated potassium channel modifier subfamily S member 3); rs16839051 in CHRM3 (muscarinic acetylcholine receptor M3); rs589416 and rs10471588 near HTR1A (5-hydroxytryptamine receptor 1A; serotonin); rs4780514 in SHISA9 (Shisa family member 9; likely involved in regulating AMPA-receptor activity and short-term neuronal synaptic plasticity, predicted part of AMPA glutamate receptor complex and active in glutamatergic synapses); rs279447 in SULT2B1 (sulfotransferase family 2B member 1); and rs1547893 in NTM (neurotrimin, a cell-adhesion molecule promoting neurite outgrowth). Several other genes were predicted to be involved with neural development, axon outgrowth, and synapses.

Mechanisms of general anesthesia at molecular, cellular, and neuronal network levels remain incompletely understood. Our study did not show interindividual differences to arise from variability in GABAA receptor genes, nor were any genes encoding enzymes known to metabolize propofol highlighted in this genome-wide association study, apart from the suggestive association of one SULT2B1 variant. Interestingly, evidence suggests that SULT2B1 sulfonates DHEA (dehydroepiandrosterone), and in the brain, both sulfated and unsulfated DHEA modulate actions of GABAA receptor and N-methyl-d-aspartate receptor.81–84  In C. elegans, cytosolic sulfotransferase ssu-1 has been shown to interact with unc-79 and to modify sensitivity to volatile anesthetics.85  It has been suggested that general anesthetics interact with multiple molecular targets but nevertheless share a mechanism.86  This genome-wide association study provided a hypothesis-free approach to explore processes involved in individual propofol requirements. Recent studies report that thalamic activity is especially crucial for the waking state and that anesthetics above all disrupt the connection between thalamus and cortex.87–89  The thalamocortical switch theory suggests that anesthetic-induced unconsciousness is based on the hyperpolarization block of thalamocortical neurons.90  Another possible interpretation of our results is that the genome-wide association study signals arise from systems important in general anesthesia but not necessarily direct propofol targets. Both NALCN and Kir2.1 channels are important in regulating cell membrane potential and could mediate sensitivity to sedative drugs by affecting the resistance (threshold) driving changes of membrane potential. ROBO3/FEZ1, MSANTD2, SPA17, and ITGBL1 seem to affect neuron adhesion, morphology, and synapses.

Complementarily, we explored whether previously studied genetic variants showed association in our study. Earlier findings came from a candidate gene approach with small samples, with most focusing on propofol’s pharmacokinetic parameters not propofol requirements during anesthesia; their endpoint was not the level of anesthesia. We were unable to replicate earlier findings.

The downside of focusing on an understudied phenotype is the lack of suitable replication cohorts. We included all patients in the discovery cohort to maximize detection power for genetic association signals rather than splitting into subcohorts. Because all patients were of Finnish origin, results may not be generalizable. However, studying genetic variation in complex phenotypes in founder populations like the Finns has advantages.91 

Our results provide intriguing hints of potential new biologic mechanisms of propofol and general anesthesia. Further knowledge of propofol targets in the central nervous system and of general anesthesia mechanisms is needed, and further research is encouraged.

Acknowledgments

The authors thank Leslie Hearn, M.Sc. (London, United Kingdom), for proofreading the article and Eija R. Ruoppa, R.N., and Minna Kaiponen, R.N. (Department of Anesthesiology, Intensive Care and Pain Medicine, Helsinki University Hospital, Helsinki, Finland), for assistance throughout the project.

Research Support

Supported by Academy of Finland (Helsinki, Finland) grant Nos. 110489 and 217028; Governmental Research Funds for University Level Health Research (Helsinki, Finland) grant Nos. TYH2008225 and TYH2010210 (to Dr. Kalso); and a stipend from The Finnish Society of Anesthesiologists (Finland), Helsinki University Hospital Research Funds and Finnish Medical Foundation grant No. 4840 (to Dr. Ahlström).

Competing Interests

Dr. Kalso has participated in advisory boards of Orion Pharma (Espoo, Finland) and Pfizer (New York, New York) and has received lecture fees from Orion Pharma, GSK (Brentford, United Kingdom), and Merck (Rahway, New Jersey), unrelated to the current work. Part of Dr. Kaunisto’s salary is covered by a large Finnish biobank study FinnGen, which is funded by 13 international pharmaceutical companies (Abbvie [Chicago, Illinois], AstraZeneca [Cambridge, Massachusetts], Biogen [Baar, Switzerland], Boehringer Ingelheim [Ingelheim, Germany], Celgene [Summit, New Jersey], Genentech [San Francisco, California; a member of the Roche Group], GSK, Janssen [Raritan,New Jersey], Maze Therapeutics [San Francisco, California], Merck/MSD, Novartis [Basel, Switzerland], Pfizer, and Sanofi [Paris, France]), unrelated to the current work. The other authors declare no competing interests.

Supplemental Digital Content

Supplemental Data 1. https://links.lww.com/ALN/D553

Supplemental Table 1. Illnesses and types of medications patients had reported. Pain medications are reported separately.

Supplemental Table 2. Suggestive associations with propofol consumption in our genome-wide association study. Full names of genes and short descriptions of predicted functions or biologic roles are listed alphabetically.

Supplemental Table 3. Literature search from PubMed and Ovid Medline. The target was to find studies that have explored how genes affect propofol requirements during general anesthesia.

Supplemental Table 4. Replication attempts of previously reported associations.

Supplemental Table 5. Significance of the single-nucleotide polymorphisms reported in the article in the genome-wide association analysis without the covariates.

Supplemental Table 6. Single-nucleotide polymorphisms with suggestive association with propofol requirements (P < 5 × 10−6) in the analysis without covariates.

Supplemental Table 7. Single-nucleotide polymorphisms detected in the genome-wide association study without covariates in the main genome-wide association analysis.

Supplemental Figure 1. LocusZoom plots of the genomic area around single-nucleotide polymorphisms highlighted in genome-wide association study.

Supplemental Figure 2. LocusZoom plots of the imputed data from ROBO3 (rs997989), NALCN (rs9518419), KCNJ2 (rs10512538), and TFAP2A (rs9395094) loci.

Supplemental Figure 3. Exploring the expression of FEZ1. (A) GTEx multitissue eQTL comparisons for the impact of rs997989 on FEZ1 expression. (B) FEZ1 expression profile in different tissues. (C) Violin plots representing expression data for different rs997989 genotypes in different neural tissues.

Supplemental Figure 4. Exploring the expression of NALCN. (A) GTEx multitissue eQTL comparisons for the impact of rs9518419 on NALCN expression. (B) Violin plots representing expression data for different rs9518419 genotypes in different neural tissues. (C) NALCN expression levels in different tissues. (D) GTEx multitissue eQTL comparisons for the impact of rs9300663 on NALCN expression. (E) Violin plots representing expression data for different rs9300663 genotypes in different neural tissues.

Supplemental Figure 5. KCNJ2 and KCNJ2-AS1 expression profile.

Supplemental Figure 6. TFAP2A bulk tissue expression profile.

Supplemental Figure 7. Replication attempts (Supplemental Table 2) for previously reported single-nucleotide polymorphisms with association with propofol requirements: Visual inspection of our genome-wide association study data with LocusZoom.

Supplemental Figure 8. Genome-wide association study results of the analysis done without covariates.

Supplemental Data 2. Genome-wide association study summary results, https://links.lww.com/ALN/D554, .txt result file from PLINK.

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