Accurate staging of non-metastatic colon cancer with CT: the importance of training and practice for experienced radiologists and analysis of incorrectly staged cases

PURPOSE

To investigate whether locoregional staging of colon cancer by experienced radiologists can be improved by training and feedback to minimize the risk of over-staging into the context of patient selection for neoadjuvant therapy and to identify potential pitfalls of CT staging by characterizing pathologic traits of tumors that remain challenging for radiologists.

METHODS

Forty-five cases of stage I-III colon cancer were included in this retrospective study. Five experienced radiologists evaluated the CTs; 5 baseline scans followed by 4 sequential batches of 10 scans. All radiologists were trained after baseline scoring and 2 radiologists received feedback. The learning curve, diagnostic performance, reader confidence, and reading time were evaluated with pathologic staging as reference. Pathology reports and H&E slides of challenging cases were reviewed to identify potential pitfalls.

RESULTS

Diagnostic performance in distinguishing T1-2 vs. T3-4 improved significantly after training and with increasing number of reviewed cases. Inaccurate staging was more frequently related to under-staging rather than over-staging. Risk of over-staging was minimized to 7% in batch 3-4. N-staging remained unreliable with an overall accuracy of 61%. Pathologic review identified two tumor characteristics causing under-staging for T-stage in 5/7 cases: (1) very limited invasive part beyond the muscularis propria and (2) mucinous composition of the invading part.

CONCLUSION

The high accuracy and specificity of T-staging reached in our study indicate that sufficient training and practice of experienced radiologists can ensure high validity for CT staging in colon cancer to safely use neoadjuvant therapy without significant risk of over-treatment, while N-staging remained unreliable.

Genetic parameters for mid-infrared spectroscopy-predicted fertility

Female fertility is a challenging trait to improve genetically because of its low heritability, its unfavorable genetic correlation with milk yield, and its relatively small number of records. The MFERT trait is the probability of conception to first insemination predicted using mid-infrared (MIR) spectroscopy of a milk sample collected during lactation as part of routine milk recording, age at calving, days in milk, and milk production. As such, MFERT could become available on many more cows than traditional fertility traits. Our objectives were (1) to estimate the heritability of MFERT; (2) to estimate genetic correlations between MFERT, traditional fertility traits, and milk production traits; and (3) to assess the potential of MFERT to be used as an indicator trait for fertility in a selection index. The MFERT trait had a heritability of 0.16, which was higher than that (0.05) obtained for traditional fertility traits. Genetic correlations between MFERT and traditional fertility traits were low to moderate. The weakest and strongest correlations (mean ± standard error) were with pregnancy at the end of the mating season (0.13 ± 0.05) and calving to first service (-0.61 ± 0.03), respectively. Based on our estimates, including MFERT in a fertility index will only substantially increase the accuracy of the index when there are many more records available for MFERT than for the traditional fertility traits. This is likely to be the case because the number of milk samples from commercial machines belonging to milk recording companies in Australia that are capable of generating MIR spectra is growing. Hence, the number of records for MFERT is expected to increase substantially in the near future.

Estimating methane coefficients to predict the environmental impact of traits in the Australian dairy breeding program

The dairy industry has been scrutinized for the environmental impact associated with rearing and maintaining cattle for dairy production. There are 3 possible opportunities to reduce emissions through genetic selection: (1) a direct methane trait, (2) a reduction in replacements, and (3) an increase in productivity. Our aim was to estimate the independent effects of traits in the Australian National Breeding Objective on the gross methane production and methane intensity (EI) of the Australian dairy herd of average genetic potential. Based on similar published research, the traits determined to have an effect on emissions include production, fertility, survival, health, and feed efficiency. The independent effect of each trait on the gross emissions produced per animal due to genetic improvement and change in EI due to genetic improvement (intensity value, IV) were estimated and compared. Based on an average Australian dairy herd, the gross emissions emitted per cow per year were 4,297.86 kg of carbon dioxide equivalents (CO₂-eq). The annual product output, expressed in protein equivalents (protein-eq), and EI per cow were 339.39 kg of protein-eq and 12.67 kg of CO₂-eq/kg of protein-eq, respectively. Of the traits included in the National Breeding Objective, genetic progress in survival and feed saved were consistently shown to result in a favorable environmental impact. Conversely, production traits had an unfavorable environmental impact when considering gross emissions, and favorable when considering EI. Fertility had minimal impact as its effects were primarily accounted for through survival. Mastitis resistance only affected IV coefficients and to a very limited extent. These coefficients may be used in selection indexes to apply emphasis on traits based on their environmental impact, as well as applied by governments and stakeholders to track trends in industry emissions. Although initiatives are underway to develop breeding values to reduce methane by combining small methane data sets internationally, alternative options to reduce emissions by utilizing selection indexes should be further explored.

Daily practice in guideline adherence to adjuvant chemotherapy in stage III colon cancer and predictors of outcome

INTRODUCTION

Although guidelines recommend adjuvant chemotherapy for stage III colon cancer patients, many patients do not receive adjuvant chemotherapy. The aim of this study was to identify reasons for guideline non-adherence and assess the effect on patient outcomes in a multicenter cohort of stage III colon cancer patients who received surgery plus adjuvant chemotherapy or surgery alone.

METHODS

Patients who underwent surgery between 2007 and 2017 were included. Reasons for non-adherence were determined. Propensity score analyses with inverse probability weighting were performed to adjust for confounding factors. Cox proportional hazards regression and risk stratified analyses were performed to assess the association of guideline adherence and other potential predictors with recurrence free survival (RFS).

RESULTS

Data of 575 patients were included of whom 61% received adjuvant chemotherapy. In 87 of 222 patients (39%) who did not receive adjuvant chemotherapy, no reason was documented. Only age was predictive for receiving chemotherapy. Patients who received adjuvant chemotherapy had longer RFS (HR 0.42, 95%CI 0.29-0.62, p < 0.001). High T- and N-stage were associated with poorer RFS HR 2.0 (95%CI 1.58-2.71, p < 0.001) and HR 2.19 (95%CI 1.60-2.99, p < 0.001) respectively. Risk groups were identified with distinct prognosis and treatment effect and a nomogram is presented to visualize individualized RFS differences.

CONCLUSION

This study shows considerable variation in guideline adherence to adjuvant chemotherapy and poor documentation on reasons for non-adherence. Optimizing adherence and gaining insight in reasons for non-adherence is advocated as this can lead to significant RFS benefit, especially in patients with high T-and N-stage tumors.

A method for implementing methane breeding values in Australian dairy cattle

Context There has been a lot of interest in recent years in developing estimated breeding values (EBVs) to reduce methane emissions from the livestock sector. However, while a major limitation is the availability of high-quality methane phenotypes measured on individual animals required to develop these EBVs, it has been recognised that selecting for improved efficiency of milk production, longevity, feed efficiency and fertility may be an effective strategy to genetically reduce methane emissions in dairy cows. Aim Applying carbon dioxide equivalents (CO2-eq) weights to these EBVs, we hypothesise that it is possible to develop a genetic tool to reduce greenhouse-gas emissions (GHG). Methods We calculated the effect of an EBV unit change in each trait in the Balanced Performance Index on CO2-eq emissions per cow per year. The estimated environmental weights were used to calculate a prototype index of CO2-eq emissions. The final set of EBVs selected for inclusion in the GHG subindex were milk volume, fat yield and protein yield, survival and feed saved, as these traits had an independent effect on emissions. Feed saved is the Australian feed efficiency trait. A further modification was to include a direct methane trait in the GHG subindex, which is a more direct genomic evaluation of methane estimated from measured methane data, calculated as the difference between actual and predicted emissions, for example, a residual methane EBV. Key results The accuracy of the GHG subindex (excluding residual methane EBV) is ~0.50, calculated as the correlation between the index and gross methane (using 3-day mean gross methane phenotypes corrected for fixed effects, such as batch and parity and adjusting for the heritability). The addition of the residual methane EBV had a minimal effect with a correlation of 0.99 between the indexes. This was likely to be due to limited availability of methane phenotypes, resulting in residual methane EBVs with low reliabilities. Conclusions We expect that as more methane data becomes available and the accuracy of the residual methane trait increases, the two GHG subindexes will become differentiated. When the GHG subindex estimates are applied to bull EBVs, it can be seen that selecting for bulls that are low emitters of GHG can be achieved with a small compromise in the BPI of ~20 BPI units (standard deviation of BPI = 100). Implications Therefore, selection for more sustainable dairy cattle, both economic and environmental, may be promptly implemented until sufficient data are collected on methane.

The use of milk mid-infrared spectroscopy to improve genomic prediction accuracy of serum biomarkers

Breeding objectives in the dairy industry have shifted from being solely focused on production to including fertility, animal health, and environmental impact. Increased serum concentrations of candidate biomarkers of health and fertility, such as β-hydroxybutyric acid (BHB), fatty acids, and urea are difficult and costly to measure, and thus limit the number of records. Accurate genomic prediction requires a large reference population. The inclusion of milk mid-infrared (MIR) spectroscopic predictions of biomarkers may increase genomic prediction accuracy of these traits. Our objectives were to (1) estimate the heritability of, and genetic correlations between, selected serum biomarkers and their respective MIR predictions, and (2) evaluate genomic prediction accuracies of either only measured serum traits, or serum traits plus MIR-predicted traits. The MIR-predicted traits were either fitted in a single trait model, assuming the measured trait and predicted trait were the same trait, or in a multitrait model, where measured and predicted trait were assumed to be correlated traits. We performed all analyses using relationship matrices constructed from pedigree (A matrix), genotypes (G matrix), or both pedigree and genotypes (H matrix). Our data set comprised up to 2,198 and 9,657 Holstein cows with records for serum biomarkers and MIR-predicted traits, respectively. Heritabilities of measured serum traits ranged from 0.04 to 0.07 for BHB, from 0.13 to 0.21 for fatty acids, and from 0.10 to 0.12 for urea. Heritabilities for MIR-predicted traits were not significantly different from those for the measured traits. Genetic correlations between measured traits and MIR-predicted traits were close to 1 for urea. For BHB and fatty acids, genetic correlations were lower and had large standard errors. The inclusion of MIR predicted urea substantially increased prediction accuracy for urea. For BHB, including MIR-predicted BHB reduced the genomic prediction accuracy, whereas for fatty acids, prediction accuracies were similar with either measured fatty acids, MIR-predicted fatty acids, or both. The high genetic correlation between urea and MIR-predicted urea, in combination with the increased prediction accuracy, demonstrated the potential of using MIR-predicted urea for genomic prediction of urea. For BHB and fatty acids, further studies with larger data sets are required to obtain more accurate estimates of genetic correlations.

Genetic parameters for methane emission traits in Australian dairy cows

Methane is a greenhouse gas of high interest to the dairy industry, with 57% of Australia's dairy emissions attributed to enteric methane. Enteric methane emissions also constitute a loss of approximately 6.5% of ingested energy. Genetic selection offers a unique mitigation strategy to decrease the methane emissions of dairy cattle, while simultaneously improving their energy efficiency. Breeding objectives should focus on improving the overall sustainability of dairy cattle by reducing methane emissions without negatively affecting important economic traits. Common definitions for methane production, methane yield, and methane intensity are widely accepted, but there is not yet consensus for the most appropriate method to calculate residual methane production, as the different methods have not been compared. In this study, we examined 9 definitions of residual methane production. Records of individual cow methane, dry matter intake (DMI), and energy corrected milk (ECM) were obtained from 379 animals and measured over a 5-d period from 12 batches across 5 yr using the SF₆ tracer method and an electronic feed recording system, respectively. The 9 methods of calculating residual methane involved genetic and phenotypic regression of methane production on a combination of DMI and ECM corrected for days in milk, parity, and experimental batch using phenotypes or direct genomic values. As direct genomic values (DGV) for DMI are not routinely evaluated in Australia at this time, DGV for FeedSaved, which is derived from DGV for residual feed intake and estimated breeding value for bodyweight, were used. Heritability estimates were calculated using univariate models, and correlations were estimated using bivariate models corrected for the fixed effects of year-batch, days in milk, and lactation number, and fitted using a genomic relationship matrix. Residual methane production candidate traits had low to moderate heritability (0.10 ± 0.09 to 0.21 ± 0.10), with residual methane production corrected for ECM being the highest. All definitions of residual methane were highly correlated phenotypically (>0.87) and genetically (>0.79) with one another and moderately to highly with other methane candidate traits (>0.59), with high standard errors. The results suggest that direct selection for a residual methane production trait would result in indirect, favorable improvement in all other methane traits. The high standard errors highlight the importance of expanding data sets by measuring more animals for their methane emissions and DMI, or through exploration of proxy traits and combining data via international collaboration.

Improving clinical management of colon cancer through CONNECTION, a nation-wide colon cancer registry and stratification effort (CONNECTION II trial): rationale and protocol of a single arm intervention study

BACKGROUND

It is estimated that around 15-30% of patients with early stage colon cancer benefit from adjuvant chemotherapy. We are currently not capable of upfront selection of patients who benefit from chemotherapy, which indicates the need for additional predictive markers for response to chemotherapy. It has been shown that the consensus molecular subtypes (CMSs), defined by RNA-profiling, have prognostic and/or predictive value. Due to postoperative timing of chemotherapy in current guidelines, tumor response to chemotherapy per CMS is not known, which makes the differentiation between the prognostic and predictive value impossible. Therefore, we propose to assess the tumor response per CMS in the neoadjuvant chemotherapy setting. This will provide us with clear data on the predictive value for chemotherapy response of the CMSs.

METHODS

In this prospective, single arm, multicenter intervention study, 262 patients with resectable microsatellite stable cT3-4NxM0 colon cancer will be treated with two courses of neoadjuvant and two courses of adjuvant capecitabine and oxaliplatin. The primary endpoint is the pathological tumor response to neoadjuvant chemotherapy per CMS. Secondary endpoints are radiological tumor response, the prognostic value of these responses for recurrence free survival and overall survival and the differences in CMS classification of the same tumor before and after neoadjuvant chemotherapy. The study is scheduled to be performed in 8-10 Dutch hospitals. The first patient was included in February 2020.

DISCUSSION

Patient selection for adjuvant chemotherapy in early stage colon cancer is far from optimal. The CMS classification is a promising new biomarker, but a solid chemotherapy response assessment per subtype is lacking. In this study we will investigate whether CMS classification can be of added value in clinical decision making by analyzing the predictive value for chemotherapy response. This study can provide the results necessary to proceed to future studies in which (neo) adjuvant chemotherapy may be withhold in patients with a specific CMS subtype, who show no benefit from chemotherapy and for whom possible new treatments can be investigated.

TRIAL REGISTRATION

This study has been registered in the Netherlands Trial Register (NL8177) at 11-26-2019, https://www.trialregister.nl/trial/8177 . The study has been approved by the medical ethics committee Utrecht (MEC18/712).

Low Socioeconomic Status Is Associated with Worse Outcomes After Curative Surgery for Colorectal Cancer: Results from a Large, Multicenter Study

BACKGROUND

Socioeconomic status (SES) has been associated with early mortality in cancer patients. However, the association between SES and outcome in colorectal cancer patients is largely unknown. The aim of this study was to investigate whether SES is associated with short- and long-term outcome in patients undergoing curative surgery for colorectal cancer.

METHODS

Patients who underwent curative surgery in the region of Rotterdam for stage I-III colorectal cancer between January 2007 and July 2014 were included. Gross household income and survival status were obtained from a national registry provided by Statistics Netherlands Centraal Bureau voor de Statistiek. Patients were assigned percentiles according to the national income distribution. Logistic regression and Cox proportional hazard regression were performed to assess the association of SES with 30-day postoperative complications, overall survival and cancer-specific survival, adjusted for known prognosticators.

RESULTS

For 965 of the 975 eligible patients (99%), gross household income could be retrieved. Patients with a lower SES more often had diabetes, more often underwent an open surgical procedure, and had more comorbidities. In addition, patients with a lower SES were less likely to receive (neo) adjuvant treatment. Lower SES was independently associated with an increased risk of postoperative complications (Odds ratio per percent increase 0.99, 95%CI 0.99-0.998, p = 0.004) and lower cancer-specific mortality (Hazard ratio per percent increase 0.99, 95%CI 0.98-0.99, p = 0.009).

CONCLUSION

This study shows that lower SES is associated with increased risk of postoperative complications, and poor cancer-specific survival in patients undergoing surgery for stage I-III colorectal cancer after correcting for known prognosticators.

Overlap between eQTL and QTL associated with production traits and fertility in dairy cattle

Background

Identifying causative mutations or genes through which quantitative trait loci (QTL) act has proven very difficult. Using information such as gene expression may help to identify genes and mutations underlying QTL. Our objective was to identify regions associated both with production traits or fertility and with gene expression, in dairy cattle. We used three different approaches to discover QTL that are also expression QTL (eQTL): 1) estimate the correlation between local genomic estimated breeding values (GEBV) and gene expression, 2) investigate whether the 300 intervals explaining most genetic variance for a trait contain more eQTL than 300 randomly selected intervals, and 3) a colocalisation analysis. Phenotypes and genotypes up to sequence level of 35,775 dairy bulls and cows were used for QTL mapping, and gene expression and genotypes of 131 cows were used to identify eQTL.

Results

With all three approaches, we identified some overlap between eQTL and QTL, though the majority of QTL in our dataset did not seem to be eQTL. The most significant associations between QTL and eQTL were found for intervals on chromosome 18, where local GEBV for all traits showed a strong association with the expression of the FUK and DDX19B. Intervals whose local GEBV for a trait correlated highly significantly with the expression of a nearby gene explained only a very small part of the genetic variance for that trait. It is likely that part of these correlations were due to linkage disequilibrium (LD) in the interval. While the 300 intervals explaining most genetic variance explained most of the GEBV variance, they contained only slightly more eQTL than 300 randomly selected intervals that explained a minimal portion of the GEBV variance. Furthermore, some variants showed a high colocalisation probability, but this was only the case for few variants.

Conclusions

Several reasons may have contributed to the low level of overlap between QTL and eQTL detected in our study, including a lack of power in the eQTL study and long-range LD making it difficult to separate QTL and eQTL. Furthermore, it may be that eQTL explain only a small fraction of QTL.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5656-7) contains supplementary material, which is available to authorized users.

Predicting the effect of reference population on the accuracy of within, across, and multibreed genomic prediction

Genomic prediction is widely used to select candidates for breeding. Size and composition of the reference population are important factors influencing prediction accuracy. In Holstein dairy cattle, large reference populations are used, but this is difficult to achieve in numerically small breeds and for traits that are not routinely recorded. The prediction accuracy is usually estimated using cross-validation, requiring the full data set. It would be useful to have a method to predict the benefit of multibreed reference populations that does not require the availability of the full data set. Our objective was to study the effect of the size and breed composition of the reference population on the accuracy of genomic prediction using genomic BLUP and Bayes R. We also examined the effect of trait heritability and validation breed on prediction accuracy. Using these empirical results, we investigated the use of a formula to predict the effect of the size and composition of the reference population on the accuracy of genomic prediction. Phenotypes were simulated in a data set containing real genotypes of imputed sequence variants for 22,752 dairy bulls and cows, including Holstein, Jersey, Red Holstein, and Australian Red cattle. Different reference populations were constructed, varying in size and composition, to study within-breed, multibreed, and across-breed prediction. Phenotypes were simulated varying in heritability, number of chromosomes, and number of quantitative trait loci. Genomic prediction was carried out using genomic BLUP and Bayes R. We used either the genomic relationship matrix (GRM) to estimate the number of independent chromosomal segments and subsequently to predict accuracy, or the accuracies obtained from single-breed reference populations to predict the accuracies of larger or multibreed reference populations. Using the GRM overestimated the accuracy; this overestimation was likely due to close relationships among some of the reference animals. Consequently, the GRM could not be used to predict the accuracy of genomic prediction reliably. However, a method using the prediction accuracies obtained by cross-validation using a small, single-breed reference population predicted the accuracy using a multibreed reference population well and slightly overestimated the accuracy for a larger reference population of the same breed, but gave a reasonably close estimate of the accuracy for a multibreed reference population. This method could be useful for making decisions regarding the size and composition of the reference population.

FISH analysis of 15 chromosomes in human day 4 and 5 preimplantation embryos: the added value of extended aneuploidy detection

OBJECTIVE

Screening for an increased number of chromosomes may improve the detection of abnormal embryos and thus contribute to the capability of preimplantation genetic screening (PGS) to detect the embryo(s) for transfer in IVF with the best chance for a healthy child. Good-quality day 4 and 5 embryos were analyzed after cryopreservation for the nine chromosomes mostly recommended for screening (13, 14, 15, 16, 18, 21, 22, X and Y), next to six additional chromosomes which are less well studied in this context (1, 2, 7, 6, 10 and 17).

METHOD

The copy numbers of 15 chromosomes were investigated by fluorescence in situ hybridization (FISH) in three consecutive rounds. The proportion of aneuploid and mosaic embryos was determined and compared in retrospect to results in case only the recommended probe set had been analyzed.

RESULTS

A total of 52 embryos from 29 infertile women were analyzed. Screening the embryos for six additional chromosomes increased the proportion of abnormal embryos from 67 to 81% (P = 0.03), owing to an increase in mosaic embryos.

CONCLUSION

All but one of the meiotic aneuploidies found in this study would have been detected by the probe set most frequently used in PGS clinics. However, aneuploid cell lines originating from mitotic errors could be detected for almost all chromosomes, so screening of six additional chromosomes mainly increased the proportion of mosaic embryos. The added value of screening for six additional chromosomes in PGS for clinical practice will remain undetermined as long as the fate of mosaic embryos after transfer is unclear.

Preimplantation genetic screening reveals a high incidence of aneuploidy and mosaicism in embryos from young women undergoing IVF

BACKGROUND

In order to assess the frequency of aneuploidy and mosaicism in embryos obtained from IVF patients aged <38 years, preimplantation genetic screening (PGS) was performed after biopsy of two blastomeres. Furthermore, the reliability of this diagnosis was assessed by performing reanalysis of the embryo on day 5.

METHOD

The copy numbers of 10 chromosomes (1, 7, 13, 15, 16, 18, 21, 22, X and Y) were investigated by fluorescence in situ hybridization (FISH) analysis. Embryos that were found to be abnormal or of insufficient morphological quality were cultured until day 5 and reanalysed. Results obtained were compared to the day 3 blastomere analysis.

RESULTS

After analysis of 196 embryos (one cell in 38% and two cells in 62%), only 36% of the embryos were found to be normal on day 3. After analysis of two blastomeres, 50% showed chromosomal mosaicism. Comparison of the FISH results from day 3 blastomeres and day 5 embryos yielded an overall cytogenetic confirmation rate of 54%.

CONCLUSIONS

The rates of mosaicism and aneuploidy in these embryos from young IVF patients are similar to those published for older women. We found the best confirmation rate after a diagnosis based on two cells, where both blastomeres showed the same chromosomal abnormality. In contrast, after a mosaic diagnosis the confirmation rate was low. The present study provides the first detailed reanalysis data of embryos analysed by PGS and clearly demonstrates the impact of mosaicism on the reliability of the PGS diagnosis.

External quality assurance programme for enzymatic analysis of lysosomal storage diseases: a pilot study

Inborn errors of metabolism are rare and laboratories performing diagnostic tests in this field must participate in external quality assurance (EQA) schemes to demonstrate their competence and also to maintain sufficient experience with patient material. EQA schemes for metabolite analyses are available (ERNDIM), but corresponding EQA schemes for enzyme analyses are nonexistent. In this paper we describe a pilot study on lysosomal enzyme testing by four centres in The Netherlands. Quantitative aspects of EQA were studied by interlaboratory comparison of activities of six lysosomal enzymes in a series of buffy coat samples. Interlaboratory variance was enormous. To reduce variance caused by methodological differences, participants reported enzyme activities relative to mean normal values. Beta-D-Galactosidase activities compared well between the participating laboratories (average interlaboratory CV 13%), but for other enzymes large differences were observed, e.g. sphingomyelinase (average CV 38%). Diagnostic proficiency was tested with cultured fibroblasts. In 45 out of a total of 48 tests (12 cell lines, 4 participants) the correct diagnosis was accomplished on the basis of merely biochemical investigations, i.e. without clinical data of the patients. In a survey using blood of a late-onset Pompe disease patient, less conclusive results were obtained. A stable enzyme source was developed for easy distribution. Most lysosomal enzymes were stable upon lyophilization of leukocyte homogenates and during subsequent storage of the freeze-dried material at room temperature, in particular when cryolyoprotectant was added. Shipment of such lyophilized samples is simple and cheap and ideal for an EQA scheme. Our study shows that an EQA programme for enzymatic testing of lysosomal storage diseases is necessary to accomplish reliable diagnostic procedures for lysosomal storage diseases. We recommend that EQA for lysosomal enzymes be implemented through ERNDIM.

Genetic polymorphism of glutamate-pyruvate transaminase (alanine aminotransaminase): influence on erythrocyte activity as a marker of vitamin B-6 nutritional status

A new, sensitive, two-step method free from interference by hemoglobin that measures erythrocyte glutamate-pyruvate transaminase (E-GPT) activity is described. Several aspects of E-GPT activity as an index of vitamin B-6 nutritional status were investigated with this method. 1) GPT shows a structural genetic polymorphism with two common alleles resulting in three phenotypes. In a population study (n = 92) E-GPT activity differed significantly (p less than 0.001) among the three phenotypic groups. Plasma pyridoxal-5'-phosphate concentrations in the three groups did not differ significantly. Therefore, E-GPT activity can only be used to assess vitamin B-6 nutritional status if GPT phenotype is accounted for. 2) Pyridoxine supplementation (10 mg/d) significantly (p less than 0.0001) increased E-GPT activity and decreased (p less than 0.0001) the percentage stimulation by pyridoxal-5'-phosphate in vitro although the absolute amount of in vitro stimulation by pyridoxal-5'-phosphate changed only marginally. 3) Inorganic phosphate inhibits in vitro activation of E-GPT by pyridoxal-5'-phosphate.