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A single measure of knee laxity ie, measurement of laxity in a single plane of motion is probably inadequate to fully describe how knee joint laxity is associated with anterior cruciate ligament injury. To characterize interparticipant differences in the absolute and relative magnitudes of multiplanar knee laxity ie, sagittal, frontal, and transverse planes and examine physical characteristics that may contribute to these differences.

Using cluster analysis, we grouped participants into distinct multiplanar knee laxity profiles based on the absolute and relative magnitudes of their anterior knee laxity AKL , genu recurvatum GR , and varusvalgus VV and internal-external rotation IER knee laxity. Using multinomial logistic regression, we then examined associations between the different laxity profile clusters and physical characteristics of sex, age, activity level, general joint laxity, body mass index, thigh strength, and 8 measures of lower extremity anatomical alignment.

Once all other physical characteristics were accounted for, the LOW cluster was more likely to be older, with longer femur length. The absolute and relative magnitudes of a person's multiplanar knee laxity are not always uniform across planes of motion and can be influenced by age, body composition, thigh strength, and structural alignment.

Except in HIGH GR , sex was not a significant predictor of cluster membership once other physical characteristics were taken into account. We identified distinct clusters that differed in the absolute and relative magnitudes of their multiplanar knee laxity profiles. A person's physical characteristics ie, age, body composition, strength, lower extremity posture in part predicted the probability of membership in a particular cluster.

The greater magnitudes of knee laxity often observed in females may be the result of innate sex differences in body composition and structure. A growing body of literature reports an association between greater magnitudes of knee joint laxity ie, anterior knee laxity [AKL]; genu recurvatum [GR]; general joint laxity [GJL], which encompasses GR; and internal rotation laxity and a greater risk of anterior cruciate ligament ACL injury.

That is, although females are reported to have greater sagittal plane laxity ie, AKL, GR than males, 7 , 8 , 13—15 they have substantially greater varus-valgus VV and internal-external rotation IER laxities, 16—18 even when matched with males on sagittal-plane knee laxity. Therefore, a single measure of knee laxity is probably inadequate to fully describe how laxity is associated with ACL injury. It is important to consider a more complete or multiplanar knee laxity profile ie, one that considers both the absolute and relative magnitudes of knee laxity across the sagittal, transverse, and frontal planes when examining a person's relative risk of injury.

However, we are unaware of any authors to date who have attempted to characterize these multiplanar knee laxity profiles. Also important to understand are the factors that may contribute to interindividual variations in multiplanar knee laxity profiles, so that we can better understand the underlying factors that contribute to high-risk knee joint laxity profiles in the future.

Physical characteristics that contribute to the greater magnitudes of knee laxity in females than in males may include sex differences in body composition eg, females have less thigh muscle mass and strength surrounding the knee 8 and hormone exposure 19 eg, females are exposed to large variations in sex hormone concentrations that may differentially affect intraarticular and extra-articular ligaments 18 , Other factors that may selectively load capsuloligamentous structures and promote greater knee laxity in a single plane of motion include condylar geometry, 21 , 22 lower extremity alignment,23 types of habitual cutting and running activities, 24 and height.

Previous authors 23 have found associations between AKL and pelvic tilt, hip anteversion, GR, and navicular drop, but we are unaware of any researchers who have examined structural alignment associations with multiplanar knee laxity. Therefore, our purpose was to use cluster analysis to group individuals based on the absolute and relative magnitudes of their AKL, GR, and VV and IER knee laxity values and then determine the physical characteristics that predicted membership in each of these multiplanar knee laxity clusters.

Our hypothesis was that we would identify distinct clusters of multiplanar knee laxity profiles that differed in absolute and relative magnitudes across anatomical planes and that an individual's physical characteristics would, in part, predict the probability of membership in a particular multiplanar laxity cluster. Specifically, we expected that clusters with higher overall magnitudes of knee laxity would be more likely to be younger, less active, female, leaner ie, have a lower body mass index [BMI] , and weaker ie, have less thigh strength and that clusters with disproportionally higher knee laxity in a given plane of motion would be more likely to have structural characteristics that selectively load capsuloligamentous structures in one or more planes eg, greater or lesser tibiofemoral angle and knee valgus or varus laxity.

Women had regular menstrual cycles and were nulligravida, as determined by self-report and a menstrual history questionnaire. All participants were enrolled in a larger study examining the effects of hormone-mediated knee laxity changes on weight-bearing knee joint biomechanics. After enrollment 1 week later for men, 2—3 months later for women , participants were measured for thigh strength and 5 joint laxity measures, with all women being measured during the first 6 days of their menstrual cycle to control for cyclic variations in knee laxity.

The delay between initial enrollment and strength and laxity testing in women allowed us to track and document their knee laxity changes across the menstrual cycle 18 , 37 and identify the days during menses when knee laxity values were expected to be at their baseline nadir.

Details of each measurement protocol follow. To determine the exposure of the knee to different activity-related loads, we used the Activity Rating Scale by Marx et al. We evaluated pelvic angle, hip anteversion, quadriceps angle Q-angle , tibiofemoral angle, tibial torsion, navicular drop, tibia length, and femur length.

All measurement procedures and their validity and reliability have been previously described 14 , 39 , 40 and illustrated14 in detail. Briefly, pelvic angle, Q-angle, tibiofemoral angle, navicular drop, and tibia and femur length were measured with the standing participant barefoot, with feet placed shoulder width apart, arms across the chest, and looking straight ahead.

Tibiofemoral frontal-plane knee angle was defined as the angle formed by the anatomical axis of the femur and the anatomical axis of the tibia. Hip anteversion was defined as the torsion of the femur using the Craig test. Participants were instructed to keep their arms over their chests and to extend the knee quadriceps or flex the knee hamstrings as hard as possible.

Three 5-second maximal voluntary isometric contractions were obtained for each motion, and the mean peak torque across trials was recorded. To control for the effects of exercise, all participants refrained from activity on the day that knee joint laxity values were obtained.

Genu recurvatum was defined as the amount of knee hyperextension in degrees when the participant maximally extended the knee with the distal thigh supported by a 4-in cm bolster. Varus-valgus rotational laxity was defined as the total angular displacement in degrees of the tibia relative to the femur while 10 Nm of torque was applied to the lateral and medial aspects of the distal tibia via a handheld force transducer model SM; Interface, Inc, Scottsdale, AZ Figure.

Internal-external rotational laxity was defined as the total angular displacement in degrees about the long axis of the tibia when internal and external rotation torques of 5 Nm were applied using a T-handle connected to a force transducer model MC3A; Advanced Medical Technology, Inc, Watertown, MA affixed to the foot cradle Figure. Consistent laxity measurements ICC range, 0.

A, Varusvalgus. B, Internal-external. Data were analyzed in 2 steps. Standardized scores for each laxity variable were used so that the magnitude of any one variable did not overwhelm the model. Based on the mean differences in laxity values between clusters, we characterized and named a multiplanar laxity profile for each cluster.

Although GJL is also a measure of joint laxity, we did not include it in the cluster analysis because it is not knee specific. Rather, GJL was used as a predictor of the different laxity profiles see next paragraph. Once the multiplanar knee laxity profile was characterized for each cluster, a backward stepwise multinomial logistic regression analysis examined the extent to which the different physical characteristics predicted cluster membership. For the initial regression analysis, the cluster that characterized the least amount of multiplanar knee laxity compared with all other clusters served as the reference group, and all other clusters were initially compared with this cluster.

Then, we conducted post hoc multiple comparisons between the various multiplanar laxity clusters by changing the reference group eg, using the cluster that characterized the greatest amount of multiplanar knee laxity as the reference group to which all other clusters were compared in order to further distinguish characteristics of the different clusters.

Because many of these variables differ by sex, we retained sex in the model to control for related confounding factors and to ensure that a given laxity profile was related to the actual physical characteristic, not an individual's sex. All analyses were performed using statistical software packages SAS version 9. The final cluster solution revealed 6 distinct clusters. Descriptive statistics for the laxity values within each cluster and results of the analysis of variance models comparing these values between clusters are shown in Table 1.

Based on these comparisons, multiplanar laxity profiles for each cluster were characterized and named as follows Table 2 : clusters 1, 2, and 3 were named LOW, MOD, and HIGH, respectively, because people in these clusters were consistently low, moderate, or high on all laxity values. These laxity profile names are used through the remainder of this article to more precisely describe each cluster. Means and standard deviations for each predictor entered into the multinomial logistic regression model are presented in Table 3 , stratified by clusters.

The 10 predictors that remained in the model after stepwise removal and that predicted membership in 1 or more of the 6 multiplanar laxity clusters are listed in Table 4. The odds ratio OR for each predictor variable when each cluster was compared with LOW ie, the initial reference group is provided in Table 5. A summary of logistic regressions when each cluster was compared with all other clusters is provided in Table 6.

The following sections summarize the primary distinguishing characteristics of each cluster, first as compared with those having the least amount of laxity LOW and then as compared with all other clusters. Results based on standardized scores for all independent variables except sex.

Results based on the standardized scores for all independent variables except sex. Once sex and all other physical characteristics were accounted for, participants who were older and had longer femur lengths were more likely to be members in LOW Table 1.

That is, for every 1-SD increase in hip anteversion, participants were 8. Our goal was to cluster individuals by their multiplanar knee laxity profiles and determine some of the physical characteristics that predict membership in each cluster. In general, our hypotheses were supported: We were able to identify distinct clusters that differed in the absolute and relative magnitudes of their multiplanar knee laxity profiles, and an individual's physical characteristics in part predicted the probability of membership in a particular cluster.

The following paragraphs address the characterization and implications of the different multiplanar knee laxity profiles defined for each cluster, followed by a discussion of the observed associations between physical characteristics and each multiplanar knee laxity cluster. Six distinct multiplanar knee laxity profiles were identified based on the cluster analysis. The latter 3 laxity profiles suggest that the envelope of laxity about the knee is not uniform in all planes of motion in all people.

Current evidence suggests that higher-risk knee joint biomechanics occur in the same planes of motion in which greater magnitudes of knee laxity are observed 10—12 and that each laxity value may uniquely contribute to high-risk landing biomechanics 11 and ACL injury risk.

It will be important for future authors to account for multiplanar knee joint laxity in order to fully understand the implications of greater magnitudes of knee joint laxity on knee joint biomechanics and injury risk. We then considered the primary physical characteristics that predicted membership in a particular cluster in an effort to elucidate the underlying factors that contribute to interparticipant differences in multiplanar knee laxity.

When LOW the cluster with the least amount of laxity was compared with all other clusters and after all other physical characteristics were accounted for, participants were more likely to be older than those in all other clusters and to have longer femur lengths than did all other clusters except for HIGH GR ie, for 1-SD increases in age and femur length, they were 2.

Participants in this study population were young adults; the age range was 18 to 30 years. Therefore, there is a greater likelihood that all participants in LOW had achieved full skeletal maturity as compared with those in other laxity profiles.

Previous studies 40 , 56 , 57 have demonstrated a reduction in joint laxity as males and females mature up to 19 years of age. Although we are not aware of any investigators who have continued to follow these maturational laxity trends into young adult years ie, beyond the age of 19 years , it is possible that laxity continues to decrease as bone and muscle mass increase up to 30 years of age. We hypothesized that people with greater overall magnitudes of knee laxity were more likely to be younger, less active, female, and weaker having less thigh strength and to have less mass lower BMI.

Body mass index is often used as a surrogate method of estimating body composition. Because lean mass and strength are reported to be positively correlated, 60 these findings suggest that members of MOD and HIGH probably had lower overall mass, as well as lower relative lean mass. Many authors 7 , 8 , 13—18 have reported higher average knee laxity values in females than in males, so sex differences in body composition and, in particular, lean body mass may explain these differences to some extent.

These distinguishing characteristics may in part explain why females, who carry less lean body mass relative to their body weight compared with males after puberty, tend to have disproportionately higher VV and IER laxity than males, even with similar sagittal-plane laxity.

The Q-angle represents a composite measure of pelvic position, hip rotation, tibial rotation, patellar position, and foot position, such that smaller angles are associated with a more neutral pelvis changing the orientation of the acetabulum and externally rotating the femur , less femoral anteversion and knee valgus laterally displacing the patella relative to the anterior-superior iliac spine and tibial tuberosity , and greater internal tibial rotation displacing the tibial tuberosity laterally.

Interesting to note is that the higher or lower Q-angles associated with the odds of being in a specific laxity profile once other physical characteristics are accounted for were somewhat inconsistent with the comparative mean values across the different laxity profiles Table 3 , which we did not observe with the other predictors. It may be that the predictive value of the Q-angle depends largely on its anatomical contributions, which were also entered in the model.

These findings suggest that GJL may represent a laxity phenomenon independent of knee joint laxity and thus a separate but important risk factor for ACL injury. Our hypothesis, that structural characteristics were more likely to predict membership in clusters with disproportionally higher knee laxity in a given plane of motion, was in large part supported.

An association between greater magnitude of navicular drop and AKL is consistent with the previous literature. Greater hip anteversion is commonly associated with an in-toeing gait, 70 , 71 which can lead to compensations in other parts of the lower extremity, including excessive internal rotation of the tibia and overpronation of the subtalar joint during walking. Genu recurvatum can result from capsuloligamentous laxity, structural factors, or the combination of both. Tibiofemoral angle describes the angulation of the knee in the frontal plane, where reduced angulation is associated with a varus knee and increased medial contact forces.

Greater relative varus alignment may lead to greater varus accelerations, which have been associated with posterolateral instability and excessive GR. This was the only cluster in which sex was a consistent predictor of cluster membership. Although it is difficult to explain why females would be more likely to be in HIGH GR than in other clusters especially because they are less likely to have smaller tibiofemoral angles and longer femur lengths than males, the other predictors of membership in HIGH GR , sex may be acting as a surrogate for other sex-dependent physical factors not accounted for in the model eg, hormones, tibial geometry.

More work is needed to understand the underlying cause of this sex-dependent association. In summary, knee joint laxity is not uniform across different directions and planes of motion, and a person's multiplanar knee laxity may in part be explained by age, body composition and strength, and lower extremity posture. Specifically, participants who were younger and had a lower BMI and less thigh muscle strength were typically associated with clusters characterized by greater overall frontal- and transverse-plane laxity profiles regardless of sagittal-plane laxity profile , whereas structural factors were more often associated with clusters characterized by disproportionately greater AKL ie, greater likelihood of having more hip anteversion and navicular drop or GR ie, greater likelihood of having smaller tibiofemoral angles and longer femur lengths.

Except for HIGH GR , these associations did not depend strongly on a person's sex, which suggests that the greater magnitudes of knee laxity more often observed in females may be largely explained by innate sex differences in body composition and structure. More work is needed to elucidate how these interparticipant differences in multiplanar knee laxity affect stability at the knee during weight-bearing activity and, ultimately, ACL injury risk.

National Center for Biotechnology Information , U. Journal List J Athl Train v. J Athl Train. Sandra J. William N. Author information Copyright and License information Disclaimer. Address correspondence to Sandra J. Address e-mail to ude. This article has been cited by other articles in PMC. Abstract Context: A single measure of knee laxity ie, measurement of laxity in a single plane of motion is probably inadequate to fully describe how knee joint laxity is associated with anterior cruciate ligament injury.

Objective: To characterize interparticipant differences in the absolute and relative magnitudes of multiplanar knee laxity ie, sagittal, frontal, and transverse planes and examine physical characteristics that may contribute to these differences. Design: Descriptive laboratory study. Setting: University research laboratory. Patients or Other Participants: participants 90 women, 50 men. Main Outcome Measure s : Using cluster analysis, we grouped participants into distinct multiplanar knee laxity profiles based on the absolute and relative magnitudes of their anterior knee laxity AKL , genu recurvatum GR , and varusvalgus VV and internal-external rotation IER knee laxity.

Conclusions: The absolute and relative magnitudes of a person's multiplanar knee laxity are not always uniform across planes of motion and can be influenced by age, body composition, thigh strength, and structural alignment. Keywords: hypermobility, anterior cruciate ligament injury risk factors, body composition, strength, lower extremity alignment, age, sex. Key Points. Activity Level To determine the exposure of the knee to different activity-related loads, we used the Activity Rating Scale by Marx et al.

Lower Extremity Anatomical Characteristics We evaluated pelvic angle, hip anteversion, quadriceps angle Q-angle , tibiofemoral angle, tibial torsion, navicular drop, tibia length, and femur length. Open in a separate window. Statistical Analysis Data were analyzed in 2 steps. Table 1. Multiple comparisons performed with Bonferroni adjustment:.

Table 2. Characteristics of Identified Laxity Profiles. Physical Characteristics Distinguishing Laxity Profiles Means and standard deviations for each predictor entered into the multinomial logistic regression model are presented in Table 3 , stratified by clusters. Because smaller species generally have 24 faster respiration rates relative to body weight than larger species, they are predicted to have a Note.

Range 0. Human blood methanol and formate levels following methanol exposure Human subjects; type of sample collected b'c Adult males and females administered aspartame; peak methanol level and range of formate levels up to 24 hr after dosing Infants administered aspartame; peak exposure level Adult males administered aspartame; range of peak serum methanol levels in all subjects Males; post exposure samples Males and females; post exposure serum levels Males without exercise; post exposure blood methanol and plasma formate Males with exercise; post exposure blood methanol and plasma formate Females; post exposure samples Exposure Oral Oral Oral Inhalation Inhalation Inhalation Inhalation Inhalation Exposure duration or method 1 dose in juice beverage 1 dose in water 75 min 4hr 6hr 6hr 8hr Methanol exposure concentration 0 3.

Monkey blood methanol and formate levels following methanol exposure Strain-sex Monkey; Cynomolgus; female; mean blood methanol and range of plasma formate at 30 min post daily exposure during premating, mating, and pregnancy Monkey; Rhesus male; post exposure blood level Exposure route Inhalation Inhalation Exposure duration 2. Because of the high blood:air partition coefficient for methanol and 3 rapid metabolism in all species studied, the bulk of clearance occurs by metabolism, though 4 exhalation and urinary clearance become more significant when doses or exposures are 5 sufficiently high to saturate metabolism subsequently in this document, "clearance" refers to 6 elimination by all routes, including metabolism, as indicated by the decline in methanol blood 7 concentrations.

Metabolic saturation and the corresponding clearance shift have not been 8 observed in humans and nonhuman primates because doses used were limited to the linear range, 9 but the enzymes involved in primate metabolism are also saturable. In addition to enzymatic metabolism, methanol can react with hydroxyl 17 radicals to spontaneously yield formaldehyde Harris et al.

Mannering et al. In an HEI study by Pollack and Brouwer , the metabolism of methanol was 21 2 times as fast in mice versus rats, with a Vmax for elimination of and Despite the faster elimination rate of methanol in mice versus rats, mice 23 consistently exhibited higher blood methanol levels than rats when inhaling equivalent methanol 24 concentrations See Tables and Because smaller species generally 27 have faster breathing rates than larger species, humans would be expected to absorb methanol via 28 inhalation more slowly than rats or mice inhaling equivalent concentrations.

If humans eliminate 29 methanol at a comparable rate to rats and mice, then humans would also be expected to 30 accumulate less methanol than those smaller species. However, if humans eliminate methanol 31 more slowly than rats and mice, such that the ratio of absorption to elimination stays the same, 32 then humans would be expected to accumulate methanol to the same internal concentration but to 33 take longer to reach that concentration. Methanol metabolism and key metabolic enzymes in primates and rodents.

Source: IPCS Formate can undergo adenosine 3 triphosphate- ATP- dependent addition to tetrahydrofolate THF , which can carry either one or 4 two one-carbon groups. Tetrahydrofolate THF - mediated one carbon metabolism is required for the synthesis of purines, thymidylate, and methionine. Source: Montserrat et al. Black et al. Cook et al. As noted in Table , minute to 6-hour exposures of 6 healthy humans to parts per million ppm methanol vapors, the American Council of 7 Governmental Industrial Hygienists ACGIH threshold limit value TLV for occupational 8 exposure , results in increased levels of blood methanol but not formate.

A limited number 9 of monitoring studies indicate that levels of methanol in outdoor air are orders of magnitude 10 lower than the TLV Table indicates that exposure of monkeys to ppm methanol 11 vapors for 2.

Kerns et al. There are no data on blood methanol and formate levels 21 following methanol exposure of humans with reduced ADH activity or marginal folate tissue 22 levels, a possible concern regarding sensitive populations. As discussed in greater detail in 23 Section 3. In conclusion, 25 limited available data suggest that typical occupational, environmental, and dietary exposures are 26 likely to increase baseline blood methanol but not formate levels in most humans.

Grant et al.. Disposition was also studied in pregnant rats and mice 6 exposed to 1,, ppm methanol vapors for 8 hours. Three to five animals were 7 examined at each dose and exposure condition. A parallel, linear route characteristic of passive- 14 diffusion accounted for an increased fraction of total elimination at higher concentrations. The data as a whole suggested that the 21 distribution of orally and i.

No 4 such differences were observed between NP and GD9 mice. The ratio is slightly less than dashed line in plot and appears to be 12 reduced with increasing methanol concentrations, possibly due to decreased blood flow to the 13 fetal compartment. Nevertheless, this is a very minor departure from linearity, consistent with a 14 substrate such as methanol that penetrates cellular membranes readily and distributes throughout 15 total body water.

They 7 speculated that faster respiration rate and more complete absorption in the nasal cavity of mice 8 may explain the higher methanol accumulation and greater sensitivity to certain developmental 9 toxicity endpoints see Section 4. The study objectives were to assess the effects of 15 repeated methanol exposure on disposition kinetics, determine whether repeated methanol 16 exposures result in formate accumulation, and examine the effects of pregnancy on methanol 17 disposition and metabolism.

Reproductive, developmental and neurological toxicity associated 18 with this study were also examined and are discussed in Sections 4. Six-hour methanol clearance 22 studies were conducted prior to and during pregnancy.

Burbacher, Shen et al. These studies were analyzed using classical PK one-compartment 30 models. The inactive ventilation rate was unchanged by methanol exposure, but the active ventilation showed a statistically significant methanol- concentration-related decline. There was also some decline in the fraction of time spent in the active state, but this too was not statistically significant.

Data 3 from the ppm group fit a linear model, while data from the 1, ppm group fit a 4 Michaelis-Menten model. This change was attributed to enzyme induction from the subchronic exposure. There appears to be an upward trend in elimination half-life and 10 corresponding downward trend in blood methanol clearance between Studies 2, 3, and 4.

Although the study can be used to predict effects in adequately 19 nourished individuals, the study may not be relevant to persons who are folate deficient. Each exposure was separated by 7 at least 2 months. Reviews by Agarwal , Burnell et al. There is evidence that some 33 human infants are able to efficiently eliminate methanol at high exposure levels, however, 34 possibly via CAT Tran et al. Individuals who are commonly folate deficient include those who are 8 pregnant or lactating, have gastrointestinal GI disorders, have nutritionally inadequate diets, are 9 alcoholics, smoke, have psychiatric disorders, have pernicious anemia, or are taking folic acid 10 antagonist medications such as some antiepileptic drugs ; Groups which are known 11 to have increased incidence of folate deficiencies include Hispanic and African American 12 women, low-income elderly, and mentally ill elderly Genetic variations in folic acid metabolic enzymes 15 and folate receptor activity are theoretical causes of folate deficiencies.

Rogers, Mole, et al. A human model was developed to extrapolate those internal metrics to inhalation and 21 oral exposure concentrations that would result in the same internal dose in humans human 22 equivalent concentrations [HECs] and human equivalent doses [HEDs].

The procedures used 23 for the development, calibration and use of these models are summarized in this section, with 24 further details provided in Appendix B, "Development, Calibration and Application of a 25 Methanol PBPK Model. MOA and Selection of a Dose Metric 26 Dose metrics closely associated with one or more key events that lead to the selected 27 critical effect are preferred for dose-response analyses compared to metrics not clearly correlated.

Rogers, 2 Mole, et al. This is likely due to the saturable metabolism of methanol. In addition, 3 respiratory and GI absorption may vary between and within species. Mode of action MOA 4 considerations can also influence whether to model the parent compound with or without its 5 metabolites for selection of the most adequate dose metric. Rogers, Barbee, et al. Andrews et al. The report of the New 11 Energy Development Organization NEDO, of Japan, which investigated developmental 12 effects of methanol in rats, indicated that there is a potential that developing rat brain weight is 13 reduced following maternal and neonatal exposures.

These exposures included both in utero and 14 postnatal exposures. The methanol PBPK models developed for this assessment do not explicitly 15 describe these exposure routes. Mathematical modeling efforts have focused on the estimation of 16 human equivalent external exposures that would lead to an increase in internal blood levels of 17 methanol or its metabolites presumed to be associated with developmental effects as reported in 18 rats NEDO, and mice J.

While recent in vitro evidence indicates that 24 formaldehyde is more embryotoxic than methanol and formate Harris et al. Thus, even if formaldehyde is ultimately identified as the 29 proximate teratogen, methanol would likely play a prominent role, at least in terms of transport to 30 the target tissue. Thus, both blood methanol and total metabolism metrics are considered to be 6 important components of the PBPK models.

Dose metric selection and MOA issues are 7 discussed further in Sections 3. Criteria for the Development of Methanol PBPK Models 8 The development of methanol PBPK models that would meet the needs of this 9 assessment was organized around a set of criteria that reflect: 1 the MOA s being considered 10 for methanol; 2 absorption, distribution, metabolism, and elimination characteristics; 3 dose 11 routes necessary for interpreting toxicity studies or estimating HECs; and 4 general parameters 12 needed for the development of predictive PK models.

Blood 15 methanol is the recommended dose metric for developmental effects, but total metabolism 16 may be a useful metric. These routes are important for determining dose metrics in the most 20 sensitive test species under the conditions of the toxicity study and in the relevant exposure 21 routes in humans. A standard variable in inhalation route risk assessments is ventilation rate.

Blood 24 methanol concentrations will depend strongly on ventilation rate, which varies significantly 25 between species. Saturable metabolism has 27 the potential to bring nonlinearities into the exposure: tissue dose relationship. Model should adequately describe the biological mechanisms that determine the 30 internal dose metrics blood methanol and total metabolism to assure that it can be reliably 31 used to predict those metrics in exposure conditions and scenarios where data are lacking.

Pollack and Brouwer 10 determined that methanol distribution in rats and mice following repeated oral and i. Mole, et al.. Further, the available data indicate that the maternal blood:fetal partition 19 coefficient is approximately 1 at dose levels most relevant to this assessment Horton et al. Consequently, fetal methanol concentrations are expected to be roughly equivalent 24 to that in the mother's blood.

Thus, pharmacokinetics and blood dose metrics for NP mice and 25 humans are expected to provide reasonable approximations of pregnancy levels and fetal 26 exposure, particularly during early gestation, that improve upon default estimations from external 27 exposure concentrations. Methanol PBPK Models 28 As has been discussed, methanol is well absorbed by both inhalation and oral routes and 29 is readily metabolized to formaldehyde, which is rapidly converted to formate in both rodents and 30 humans.

As was discussed in Section 3. Several rat, mouse and human PBPK models which attempt 32 to account for these species differences have been published Fisher et al. Ward et al. The model 10 has not been parameterized for humans. The Bouchard et al. The model was developed for inhalation and i. Methanol is primarily eliminated via saturable metabolism. The model adequately 5 simulates blood kinetics in NP rats and humans following inhalation exposure and in NP rats 6 following i.

Because methanol distributes 7 with total body water Horton etal. The model has been parameterized for a required human 14 exposure route, inhalation Table The model meets criteria 1, 2b, 3, 4, and 5 described in 15 Section 3. However, the Bouchard model has specific and significant limitations. The 16 model has neither been parameterized for the mouse, a test species of concern Table , nor for 17 the oral route in humans. As such, the model cannot be used to conduct the necessary 18 interspecies extrapolation.

The model 21 has not been parameterized for humans. The model adequately fits the experimental blood kinetics of methanol in rat and mice and is therefore suitable for simulating blood dosimetry in the relevant test species and routes of exposure oral and i. The Ward et al. The most significant limitation is the absence of parameters for the oral and inhalation routes in the human.

A modified version of this model that includes human parameters and a standard PBPK lung compartment might be suitable for the purposes of this assessment. Routes of exposure optimized in models - optimized against blood concentration data. Route i. Inhalation Oral Ward et al. Gentry et al. The rat and human models described in three papers by Gentry et al.

Although the overall model structure, the description of kinetics for both parent compound and primary metabolite, gestational compartments, lactational transfer, oral and i. In particular, the model structure is more elaborate than necessary; because methanol partition coefficients are near 1 for all tissues except fat, there is no need to individually represent these tissues.

Similarly, a fetal compartment may not be necessary because methanol kinetics in the fetus conceptus is expected to parallel maternal blood concentrations in the rodent. However, even if a fetal model was considered necessary, other than the partition coefficient, there are insufficient data to identify conceptus compartment parameters for methanol.

This model would require the most modification and parameterization to be useful for methanol risk assessment since parameters would have to be estimated for all relevant species at least rat and humans and for several routes of exposure.

Therefore the isopropanol model was not considered further. Selected Modeling Approach 1 As discussed earlier regarding model criteria, fetal methanol concentrations can 2 reasonably be assumed to equal maternal blood concentration. Thus, methanol pharmacokinetics 3 and blood dose metrics for NP laboratory animals and humans are expected to improve upon 4 default extrapolations from external exposures as estimates of fetal exposure during early 5 gestation.

The same level of confidence cannot be placed on the whole-body rate of metabolism, 6 in particular as a surrogate for formaldehyde dose. Because of formaldehyde's reactivity and the 7 limited fetal metabolic ADH activity see Sections 3. But since there is no model that explicitly describes formaldehyde 10 concentration in the adult, let alone the fetus, the metabolism metric is the closest one can come 11 to predicting fetal formaldehyde dose.

This metric is expected to be a better predictor of 12 formaldehyde dose than applied methanol dose or even methanol blood levels, which do not 13 account for species differences in conversion of methanol to formaldehyde. The 17 model of Ward et al. The saturable pathway described in Ward et al. The model of Ward et al. They then explicitly describe two pathways of formaldehyde transformation, one to 27 formate and the other to "other, unobserved formaldehyde byproducts.

All of these metabolic and elimination steps are described as first-order processes, 30 but the explicit descriptions of formaldehyde and formate kinetics significantly distinguish the 31 model of Bouchard et al. The Ward 4 et al. Based primarily on the 5 extensive amount of fitting that has already been demonstrated for this model, it was determined 6 that a modified Ward et al. See Appendix B for a 8 more complete discussion of the selected modeling approach and modeling considerations.

Available PK Data 9 Although limited human data are available, several studies exist that contain PK and 10 metabolic data in mice, rats, and nonhuman primates for model parameterization. Table 11 contains references that were used to verify the model fits as reported in Ward et al. This 14 model is a revision of the model reported by Ward et al. A 20 fat compartment was included because it is the only tissue with a tissue:blood partitioning 21 coefficient appreciably different than 1, and the liver is included because it is the primary site of 22 metabolism.

A bladder compartment was also added for use in simulating human urinary 23 excretion to capture the difference in kinetics between changes in blood methanol concentration 24 and urinary methanol concentration; the difference in model fit to human urinary data with vs. The model code describes inhalation, 26 oral, and i. In humans, inhalation 28 exposure data were available for model calibration and validation but not oral or i.

Monkey data were evaluated for insight into primate 3 kinetics. Data from Batterman et al. The fact that optimized human parameters were similar to those predicted 6 in monkeys was important to the validation process Bouchard et al. Blood levels of methanol have been reported following i. Simulated methanol elimination by these metabolic processes is not linked in the PBPK model to production of formaldehyde or formate.

For the PBPK model, methanol metabolism is simply another route of methanol elimination. Metabolism of formaldehyde to formate is not explicitly simulated by the model, and this model tracks neither formate nor formaldehyde. Both metabolic pathways were used to describe MeOH metabolism in the mouse and SD rat, while a single pathway describes metabolism in the F rat and human.

Thus, the focus of model 4 development was on obtaining predictions of increased body burdens over background following 5 external exposures. To accomplish this, the PBPK models used in this assessment do not account 6 for background levels of methanol, formaldehyde or formate. In addition, background levels 7 were subtracted from the reported data before use in model fitting or validation in many cases 8 the published data already have background subtracted by study authors.

This approach for 9 dealing with endogenous background levels of methanol and its metabolites assumes that: 10 1 endogenous levels do not contribute significantly to the adverse effects of methanol or its 11 metabolites; and 2 the exclusion of endogenous levels does not significantly alter PBPK model 12 predictions.

There is uncertainty associated with these assumptions. Human data are not 13 available to evaluate whether there is a relationship between background levels of methanol or its 14 metabolites. To test the assumption that the exclusion of endogenous background levels does not 15 significantly alter PBPK model predictions, EPA performed the following alternative analysis 16 using models that incorporate background levels of methanol and its metabolites.

Alternative modeling approach - incorporation of background. If background 18 methanol levels are high enough compared to those which induce metabolic saturation, they may 19 have a significant impact on parameter estimation and hence internal dose predictions. To gauge 20 the impact of background levels on PBPK model predictions of exposure-induced changes in 21 internal doses, alternate test versions of the rat and human PBPK models were created which 22 incorporate a zero-order liver infusion term for methanol designed to approximate reported rat 23 and human background levels.

Internal dose estimates for various exposure levels obtained from 24 the PBPK models that exclude background up front could then be compared with those from 25 models for which background levels were modeled, but then subtracted for benchmark dose 26 BMD modeling.

In short the level of effect above 31 background was correlated with the internal dose above background in the animal, then the 32 human background internal dose was added to the POD obtained with that metric to yield an 33 estimate of the dose when humans would have the same level of effect.

Because the more complex PBPK modeling required to 5 include background levels was estimated to have a minimal impact on dose extrapolations, the 6 use of simpler methanol models that do not incorporate background levels is considered adequate 7 for the purposes of this assessment. Model Parameters 8 The EPA methanol model uses a consistent set of physiological parameters obtained 9 predominantly from the open literature Table ; the Ward et al.

The QPC used to fit the human data was obtained from U. EPA b. This QPC was somewhat higher than calculated from Brown et al. K1C is the first-order loss from the blood for human simulations that represents urinary elimination. Allometric scaling for first-order clearance processes was done as previously described Teeguarden et al..

Parameter values used in the calibrated model are given in Table The best model fit to the mouse oral route blood methanol PK data was obtained 6 using a two-compartment GI tract model, as depicted in Figure Because the oral data in rats 7 led to the conclusion that a saturable rate of uptake from the stomach lumen was necessary see 8 section 3. Adjusting the other mouse oral uptake 11 parameters gave an adequate fit to those data. This calibration allows inhalation to oral dose- 12 route extrapolations in the mouse, which can then be extrapolated to identify human oral route 13 exposures equivalent to mouse inhalation exposures if equivalent human exposures exist.

Model fits to data sets from GD6, GD7, and GD10 mice for 6- to 7-hour inhalation exposures to 1,, ppm methanol. Maximum concentrations are from Table 2 in Rogers et al. The dataset for GD7 mice exposed to 10, ppm is from Rogers and Mole and personal communication. Default ventilation rates Table were used to simulate these data. Source: Rogers and Mole : Rogers et al. Data points are measured blood methanol levels and lines represent PBPK model simulations.

Digitizlt Sharlt! Default ventilation rates Table were used to simulate the Dorman data. The alveolar ventilation rate for each data set from Perkins et al. The cardiac output for these simulations was set equal to the alveolar ventilation rate. Source: Dorman et al. The results of this calibration of the methanol PBPK 4 model are described in Appendix B and were generally consistent with both the available 5 inhalation and oral-route data.

Up to 20 hours post exposure, blood methanol kinetics appears 6 similar for NP and pregnant mice. However, some data suggests that clearance in GDIS mice is 7 slower than in NP and earlier in gestation GD10 and less , particularly beyond 20 hours post 8 exposure see the i.

The i. These values appear to be 3 consistent with the plots in their publication but are inconsistent with some of the values in their 4 Table 6 Ward et al. In particular, the initial maternal blood concentration i. The discrepancy between the first two values 13 and the third value suggests either a dose dependence in the Vd or some source of experimental 14 variability.

However, based on these values, the U. EPA 26 has concluded that the apparent dose dependency is probably the result of a dosing error and 27 therefore, that dose-dependent parameter changes e. With that exception, both the single set of parameters used 5 herein and the assumption that maternal blood methanol is a good metric of fetal exposure are 6 well supported by the data.

Two saturable metabolic pathways are thus described by the model 5 and supported by the data. Also, it is thereby demonstrated that a model based on NP mouse 6 physiology adequately describes predicts dosimetry in the pregnant mouse dam through GD Thus the existing model appears to be adequate for predicting 10 internal methanol doses, including fetal exposures, at bioassay conditions.

Sensitivity analyses were conducted for 16 the inhalation and oral routes. The inhalation route analysis was conducted under the exposure 17 conditions of Rogers and Mole and Rogers et al. The sensitivity coefficient 23 for QPC increases during the exposure period as metabolism begins to saturate. Following oral 24 exposure, mouse blood methanol AUC was sensitive to the rate constants for oral uptake. The sensitivity coefficient for VmASC decreased during the 27 first hours after exposure from 1 to less than 0.

Blood methanol AUC 28 was also modestly sensitive to first-order uptake from the intestine KAI , and first-order transfer 29 between stomach and intestine KSI , the rate constants for uptake from the intestine and transfer 30 rates between compartments, respectively. Rat Model Calibration 32 The rat model was calibrated to fit data from i. Holding other parameters constant, the rat PBPK model was 2 initially calibrated against the entire set of i. However when the resulting parameters were then 8 used to simulate the F inhalation uptake data of Horton et al.

More careful examination of the i. It was concluded that the 14 combined data set indicated a true strain difference in metabolic parameters. The metabolic 15 parameters for SD rats were then obtained by fitting only the Ward et al. For this data set, however, the optimization either converged with 20 the metabolic Vmax for the high affinity low Km pathway at zero, or with that Km value 21 increasing to be statistically indistinguishable from the high Km value.

Therefore the Vmax for 22 the high affinity pathway was allowed to be zero, the Km for that pathway was not estimated, and 23 only a single Vmax and low affinity high Km were fit to those data, with a simultaneous 24 identification of FRACIN. The optimized parameters for both strains of rats are 26 given in Table This lower fractional absorption is consistent with values presented by 30 Perkins et al. Data points represent measured blood concentrations and lines represent PBPK model simulations.

Source: Ward et al. Model fits to data sets from inhalation exposures to triangles , 1, diamonds , or 2, squares ppm methanol in male F rats. The model was calibrated against all three sets of concentration data, though it converged to parameter values that only fit the lower two data sets well. Symbols are concentrations obtained from Horton et al.

Lines represent PBPK model fits. Since the ppm data peak occurred at 7 hour, a 7-hour simulated exposure is also shown for comparison. Source: Horton et al. It was concluded that the rate of absorption must at least partly saturate at the higher 13 dose, and hence that Michaelis-Menten kinetics should be used.

For the purpose of scaling across individuals, strains, and species, the Km for 23 absorption from the stomach KMAS was assumed to scale in proportion to the stomach 24 lumen volume; i. Symbols are concentration data obtained from the command file. Note that the total amount eliminated by this route 7 depends only on Kl, while KBL affects the rate at which the material cleared from the blood then 8 appears in the urine.

Inhalation-route urinary methanol kinetic data described by Sedivec et al. Urinary methanol elimination concentration upper panels and cumulative amount lower panel following inhalation exposures to methanol in human volunteers. Middle panel shows that without a bladder compartment the shape of the urine time-course is quite discrepant from the data.

Data points in lower panel represent estimated total urinary methanol elimination from humans exposed to 78 diamonds , triangles , and circles ppm methanol for 8 hours, and lines represent PBPK model simulations. Source: Sedivec et al. The S. Source: Perkins et al. Parameter estimate results obtained using acslXtreme to fit all human data using either saturable or first-order metabolism Parameters Michaelis-Menten optimized Km vmaxc First order KLLC Optimized value Therefore a true S.

The metabolic first-order or saturable and urinary elimination constants 4 were numerically fit to the human datasets, while holding the value for FRACIN at 0. Other human-specific physiological parameters were 8 used, as reported in Table Batterman et al. Data showing the visual quality of the fit using optimized first- order or Michaelis-Menten kinetics to describe the metabolism of methanol in humans.

Rate constants used for each simulation are given in Table Source: Batterman et al. Use of a first-order rate has the advantage of resulting in a simpler one fewer variable model, while providing an adequate fit to the data; however, the saturable model clearly fits some of the data better. Since the parameters are optimized in the model using the maximum log likelihood 4 function LLF , the resultant LLF is used for the statistical comparison of the models.

Forcing the model to use the 13 Km calculated by Perkins et al. While the correlation coefficients Table indicate that 15 VmaxC, and Km are highly correlated, that is not unexpected, and the S. If the data were indistinguishable from a linear system, Km in 17 particular would not be so bounded from above since the Michaels-Menten model becomes 18 indistinguishable from a linear model as VmaxC and Km tend to infinity.

Therefore, the 22 Michaelis-Menten metabolism rate equation appears to be sufficiently supported by the existing 23 data with values in a concentration range in which the nonlinearity has an impact. Models were optimized for all human datasets under non working conditions.

Under these conditions, the likelihood ratio can be used to compare the relative ability of the two models to describe the data, as described in "Reference Guide for Simusolv" Steiner etal.. Extrapolation to higher concentrations is 4 potentially misleading since the nonlinearity in the exposure-internal-dose relationship for 5 humans is uncertain above this point. However, the use of a BMDL should place the exposure 6 concentrations well within the linear range of the model.

Historical measures 9 of QPC The results 13 are remarkably good, given the lack of parameter adjustment to data collected in a different 14 laboratory and using different human subjects than those to which the model was calibrated. Ernstgard et al. Inhalation exposures to methanol in human volunteers. Data points represent measured blood methanol concentrations from humans 4 males and 4 females exposed to ppm open symbols or ppm filled symbols for 2 hours during light physical activity.

Solid lines represent PBPK model simulations with no fitting of model parameters. For the first 2 hours, a QPC of Source: Ernstgard et al. Oral Route 1 There were no methanol human data available for calibration or validation of the oral 2 route for the human model. In the absence of methanol data to estimate rate constants for oral 3 uptake, human oral absorption parameters reported values for ethanol Sultatos et al.

Also, while 8 Sultatos et al. Changes in the absorption constants simply 15 cause the amount of methanol in each GI compartment at steady state to change until the net rate 16 of absorption from the stomach and intestine equals the rate of infusion. Thus the human 17 absorption constants were set to what is considered a reasonable estimate, given the lack of 18 human oral PK data, but the simulations are conducted in a way that makes the result insensitive 19 to their values; having human values set does allow for simulations of non-constant infusion, 20 should such be desired.

Since the AUC was computed for a continuous oral exposure, its value is 21 just 24 hours times the steady-state blood concentration at a given oral uptake rate. Individual blood 26 concentration measurements prior to and following exposure are shown in scatter plots in 27 Appendix B of Burbacher, Shen et al.

More specifically, the monkeys in the study were 28 exposed for 2. Blood 31 samples were taken and analyzed for methanol concentration at 30 minutes, 1, 2, 3, 4, and 6 32 hours after removal from the chamber or 1, 1. These data were analyzed to compare the PK in NP versus pregnant animals, 2 and fitted with a simple PK model to estimate hour blood AUC values for each exposure 3 level. Burbacher graciously provided the original data, which were used in this analysis. The data from the 6 scatter plots of Burbacher, Shen et al.

Since the pregnancy time points were from animals that 9 had been previously exposed for 87 days plus the duration of pregnancy to that time point, the 10 pre-exposed NP animals were used for comparison, rather than naive animals, with the 11 expectation that effects due to changes in enzyme expression i. Note that each exposure group included a pre- 13 exposure baseline or background measurement, also shown. To aid in distinguishing the data 14 visually, the NP data are plotted at times 5 minutes prior to the actual blood draws and the 3rd 15 trimester at 5 minutes after each blood draw.

The solid lines are model simulations calibrated to only the 2nd trimester data 18 details below , but they just as adequately represent average concentrations for the NP and 3rd 19 trimester data. Likewise, a PK model calibrated to the NP PK data adequately predicted the 20 maternal methanol concentrations in the pregnant monkeys results not shown. Since any 21 maternal:fetal methanol differences are expected to be similar in experimental animals and 22 humans with the maternal:fetal ratio being close to one due to methanol's high aqueous 23 solubility and relatively limited metabolism by the fetus , the predicted levels for the 2nd 24 trimester maternal blood are used in place of measured or predicted fetal concentrations.

Blood methanol concentration data from NP and pregnant monkeys. NP and 3rd trimester data are plotted, respectively, at 5 minutes before and after actual collection times to facilitate comparison. Solid line is from simple PK model, fit to 2nd trimester data only.

Source: Burbacher, Shen et al. The data 2 in Figure Burbacher, Grant, et al. The use of a single-compartment model for the chamber allows this dynamic to 5 be captured, so that the full concentration-time course is used in simulating the monkey internal 6 concentration rather than an approximate step function i.

Chamber concentration profiles for monkey methanol exposures. Lines are model simulations. Indicated concentrations are target concentrations; measured concentrations differed slightly see Table Source: Buibacher, Shen et al. While the discussion 12 above and data show little difference between the NP and two pregnancy groups, the 2nd 13 trimester group was presumed to be most representative of the average internal dosimetry over 14 the entire pregnancy.

Further, the results of Mooney and Miller show that developmental 15 effects on the monkey brain stem following ethanol exposure are essentially identical for 16 monkeys exposed only during early pregnancy versus full-term, indicating that early pregnancy is 17 a primary window of vulnerability. The model 19 provides a good fit to the monkey blood and chamber air concentration data.

The model does an adequate job of fitting 3 the data for all exposure groups without group-specific parameters. In particular, the data for all 4 exposure levels can be adequately fit using a single value for the volume of distribution Vmk as 5 well as each of the metabolic parameters.

While one may be able to show statistically distinct 6 parameters for different groups or exposure levels by fitting the model separately to each , as 7 was done by Burbacher, Shen et al. Thus, the single set of parameters listed with the parameter 10 descriptions above will be used to estimate internal blood concentrations for the dose-response 11 analysis. The chamber concentrations for "pregnancy" exposures recorded by Burbacher, Shen et 12 al.

Monkey group exposure characteristics Exposure concentration ppm a 1, Group average BW kg b 3. Calculated using the two-compartment PK model as described above. Summary and Conclusions 14 Mouse, rat, and human versions of a methanol PBPK model have been developed and 15 calibrated to data available in the open literature.

The model simplifies the structure used by 16 Ward et al. Likewise, 8 maternal blood kinetics in monkeys differs little from those in NP animals see Section 3. Further, in both mice and monkeys, to the extent that late-pregnancy blood levels differ 10 from NP for a given exposure, they are higher; i. These data support the assumption that the ratio of 12 actual target-tissue methanol concentration to predicted NP maternal blood concentrations will 13 be about the same across species, and hence, that using NP maternal blood levels in place of fetal 14 concentrations will not lead to a systematic error when extrapolating risks.

However, the critical gestational window for the reduced brain weight 18 effect observed in the NEDO rat study is broader than for the mouse cervical rib effect. In 19 addition, NEDO rats were exposed not only to methanol gestationally but also 20 lactationally and via inhalation after parturition. The additional routes of exposure presented to 21 the pups in this study present uncertainties see additional discussion in Section 5. Therefore blood or target-tissue levels in the breast-feeding infant 26 or pup are likely to differ more from maternal levels than do fetal levels.

In addition, the health- 27 effects data indicate that most of the effects of concern are due to fetal exposure, with a relatively 28 small influence due to post birth exposures. Finally, one would still expect the target-tissue concentrations in the offspring to 33 be closely related to maternal blood levels which depend on ambient exposure and determine the 34 amount delivered through breast milk , with the relationship between maternal levels and those 35 in the offspring being similar across species.

Therefore, it is assumed that 3 the potential differences between pup and dam blood methanol levels do not have a significant 4 impact on this risk assessment and the estimation of HECs. In particular, the existing human data allow for predictions of maternal 9 blood levels, which depend strongly on the rate of maternal methanol clearance. Since bottle-fed 10 infants do not receive methanol from their mothers, they are expected to have lower or, at most, 11 similar overall exposures for a given ambient concentration than the breast-fed infant, so that use 12 of maternal blood levels for risk estimation should also be adequately protective for that group.

In particular, the oral 16 mouse model consistently underpredicts the amount of blood methanol reported in two studies 17 Wardetal.. Additionally, lower partition 21 coefficients for placenta 1. The current refined model adequately fits the oral PK data using a single set 23 of parameters that is not varied by dose or source of data. Low-dose exposures were emphasized in model optimization due to their 26 greater relevance to risk assessment.

In Section 5, the models and these results are used to 34 estimate chronic human exposure concentrations from internal dose metrics. As many of the case reports demonstrate, the 4 association of Parkinson-like symptoms with methanol poisoning is related to the observation 5 that lesions in the putamen are a common feature both in Parkinson's disease and methanol 6 overexposure. Other areas of the brain e. The reader also is 12 referred to Kraut and Kurtz and Barceloux et al. A brief discussion of the terms 14 cited in case report literature follows.

The connectivity within the basal ganglia involves both excitatory and inhibitory 19 neurotransmitters such as dopamine associated with Parkinson's disease when production is 20 deficient. Dystonia or involuntary muscle contraction can result from lesions in the 24 putamina; if there are concomitant lesions in the globus pallidus, Parkinsonism can result Bhatia Note.

Bhatia and Marsden have discussed the various behavioral and motor 2 consequences of focal lesions of the basal ganglia from case-study reports. Lesions in the 3 subcortical white matter adjacent to the basal ganglia often occur as well Airas et al. In the case reports of Patankar et al. In all, 41 people 10 died. Visual impairment was mostly characterized 13 by blurred or indistinct vision; some who were not acidotic experienced transient visual 14 disturbances. The cardiovascular parameters were unremarkable.

The importance of acidosis to 15 outcome is shown in Table Among the key pathological features were cerebral edema, lung 16 congestion, gastritis, pancreatic necrosis, fatty liver, epicardial hemorrhages, and congestion of 17 abdominal viscera. The patient had difficulty walking and 2 could only make right turns with difficulty. There was no memory loss. She 5 displayed profound acidosis; her vital signs, once she was treated for acidosis, were normal by 36 6 hours after hospital admission.

A regimen of levadopa treatment greatly improved her ability to function 9 normally. In this case report respiratory support was needed; the woman was in a coma. Computerized Axial Tomography scan 14 findings highlighted the central nervous system CNS as an important site for methanol 15 poisoning. CT scans at days following ingestion were 19 normal. However, MRI scans at day 4 revealed lesions in the putamen and peripheral white 20 matter of the cerebral and cerebellar hemispheres.

Bilateral cerebellar cortical lesions had been 21 reported in an earlier case of methanol poisoning by Chen et al. The first subject 29 was a year-old woman who complained of blurred vision, diplopia, and weakness 24 hours 30 after ingesting mL of a methanolic antifreeze solution.

Upon hospital admission she was 31 comatose and in severe metabolic acidosis. An MRI scan at 9 days indicated abnormal 32 hyperintense foci in the putamina decreased in size by day 23 and subtle lesions no change by 33 day 23 in the white matter.

Upon her discharge, bilateral deficits in visual acuity and color 34 discrimination persisted. An MRI administered 24 hours after hospital admission revealed 4 abnormal hyperintense foci in the putamina, with less intense lesions in the white matter. Like 5 the first subject, a subsequent MRI indicated the foci decreased in size over time, but visual 6 impairments persisted. An MRI revealed lesions in the putamina and occipital subcortical white matter. A 10 follow-up CT scan was performed after 1 year and showed regression of the putaminal lesions 11 but no change in the occipital lesions.

Upon his discharge, severe visual impairment remained 12 but no extrapyramidal signs were observed. An MRI revealed 15 lesions in the putamina; at 3 weeks these lesions were observed to have decreased in size. Upon 16 his discharge, the neurological signs had improved but optic neuropathy in visual evoked 17 potential was observed. No distress to the fetus was observed upon gynecologic 22 examination. Six days after therapy was initiated methanol was not present in blood , she gave 23 birth.

No further complications with either the mother or newborn were noted. The report 26 by Wu et al. However, this 28 infant exhibited no toxic signs and survived without any apparent permanent problems. De 29 Brabander et al. Hantson et al. Approximately 5 10 hours after MRI examination, he developed blurred vision and motor dysfunction. After 6 5 months, visual deficits persisted along with extrapyramidal symptoms. Persistent visual 7 dysfunction was also reported in another methanol poisoning case Arora et al.

Diffusion-weighted MRI provides an image contrast distinct from standard 11 imaging in that contrast is dependent on the molecular motion of water Schaefer et al. A review of a head CT scan performed before the individual went into respiratory arrest 20 revealed bilateral globus pallidus ischemia.

Standard treatments corrected the acidosis pH 24 6. The follow-up MRI showed persistent putaminal lesions 26 with cortical involvement. The initial CT scan revealed mild cerebral edema. A repeat CT scan 48 hours after presentation showed 31 hypodensities in the putamen and peripheral white matter. One month after discharge, cognitive 32 function improved, and the patient experienced only a mild lower-extremity tremor.

The 3 woman, a chronic alcoholic, was in a vegetative state when found and did not improved over the 4 course of a year. In addition, there was extensive 8 subcortical necrosis and bilateral necrosis of the pontine tegmentum and optic nerve. The patient 9 died several hours after the scans were performed. In two of the deceased individuals, CT scans and autopsy 13 revealed putaminal hemorrhagic necrosis. A CT scan on one individual revealed bilateral putaminal and cerebral lesions.

This individual, despite standard treatments, never 20 regained consciousness. The second individual, upon MRI, showed scattered hemorrhage at the 21 grey-white interface of the cerebral hemispheres. Use of a methanol-containing emollient by a woman with 24 chronic pain led to vision loss, hyperventilation and finally, coma Adanir et al.

Dutkiewicz et al. In the case report of Aufderheide et al. This latter individual was treated with fomepizole. No 2 patient had an abnormal ophthalmologic examination. All seven stabilized quickly and acidosis 3 was normalized in 4 hours. In 10 general, serum methanol concentrations were highest among those most severely affected.

The 11 poor outcome was closely correlated with the degree of metabolic acidosis. However, others with detectable levels of ethanol along with 15 severe metabolic acidosis two of whom died presumably had subtherapeutic levels of ethanol in 16 their system.

They concluded that the osmolal gap could be 25 taken as a priori indication of methanol poisoning and be used to guide initiation and duration of 26 dialysis. As they indicated, many hours of dialysis could be safely dispensed with. The osmolal 27 gap pertains to the effect that methanol and other alcohols has on the depression of the freezing 28 point of blood in the presence of normal solutes.

Braden et al. A more detailed discussion 32 of the anion and osmolal gap has been provided by Henderson and Brubacher They examined 25 patients, 12 of whom died; 3 of 35 the survivors were rendered blind. It 2 was concluded that poor prognosis was associated with pH hours delay from intake to admission. These investigators cited differences in sampling time, ingestion of 6 ethanol, and levels of toxic e.

As an 7 illustration, the case report by Prabhakaran et al. Patient 1 was in 11 metabolic acidosis and had an unstable conscious state even after treatment. Upon discharge at 12 day 6, there were no apparent sequelae. Patient 2 had severe metabolic acidosis, fixed and 13 dilated pupils, and no brain stem reflexes. This patient died at day 3 even though therapeutic 14 measures had been administered. These brain levels were much higher than blood levels postmortem.

In their study of 26 chronic users of methylated spirits, Meyer 22 et al. Liu et al. In the case reports cited 17 above, the onset of symptom sets as well as their severity varies depending upon how much 18 methanol was ingested, whether or not and when appropriate treatment was administered, and 19 individual variability. A longer time between exposure and treatment, with few exceptions, 20 results in more severe outcomes e.

In one case report Rubinstein et al. Bilateral hemorrhagic 8 and nonhemorrhagic necrosis of the putamen is considered by many radiologists as the most 9 well-known sequelae of methanol overexposure. Occupational Studies 10 Occupational health studies have been carried out to investigate the potential effects of 11 chronic exposure to lower levels of methanol than those seen in acute poisoning cases such as 12 those described above.

For example, Frederick et al. A group of 84 teacher's 16 aides were selected for study, 66 of whom responded with a completed medical questionnaire. A 17 group of teachers who were not exposed to methanol vapors to the same extent as the 18 teacher's aides completed questionnaires as a control group. Upon comparison of the self- 23 described symptoms of the 66 teacher's aides with those of 66 age-matched teachers chosen from 24 the who responded, the number of symptoms potentially related to methanol were 25 significantly higher in the teacher's aides.

These included blurred vision By 27 contrast, symptoms that are not usually associated with methanol exposure painful urination, 28 diarrhea, poor appetite, and jaundice were similar in incidence among the groups. Odds ratios for cleft palate only and cleft lip with or without cleft palate 2 were calculated for 96 chemicals. There seemed to be no consistent pattern of association for any 3 chemical or group of chemicals with these impairments, and possible exposure to methanol was 4 negative for both outcomes.

In a pilot study by Cook et al. The 11 majority of results in a battery of neurobehavioral endpoints were negative. However, statistical 12 significance was obtained for results in the P and N1-P2 component of event-related 13 potentials brain wave patterns following light flashes and sounds , the Sternberg memory task, 14 and subjective evaluations of concentration and fatigue. As noted by the Cook et al. Exposure resulted in elevated blood and urine methanol levels up to peak levels of 23 6.

The majority of study 24 results were negative. Twelve 7 healthy men average age The 20 ppm Following each single exposure, subclinical inflammation was 12 assessed by measuring concentrations of interleukins IL-8, IL-lp, and IL-6 and prostaglandin 13 E2 in nasal secretions. Mucociliary clearance was evaluated by conducting a saccharin transport 14 time test and measuring ciliary beat frequency.

Interleukin and prostaglandin data were evaluated 15 by a 1-tailed Wilcoxon test, and ciliary function data were assessed by a 2-tailed Wilcoxon test. There were no significant effects on IL-6 and prostaglandin E2 concentration, 19 ciliary function, or on the self-reported incidence of subjective symptoms of irritation.

These exposure levels were associated with peak methanol blood levels of 26 6. Data for subchronic, chronic or in utero 33 human exposures are very limited and inconclusive. Most are via the inhalation route. Presented below are summaries of the 3 noncancer effects reported in these bioassays.

Carcinogenic effects are not described or 4 discussed in this assessment. Acute Toxicity 5 Although there are few studies that have examined the short-term toxic effects of 6 methanol via the oral route, a number of median lethal dose LD50 values have been published 7 for the compound. This study generated data on 13 weekly body weights and food consumption, clinical signs of toxicity, ophthalmologic 14 evaluations, mortality, blood and urine chemistry from a comprehensive set of hematology, 15 serum chemistry, and urinalysis tests , and gross and microscopic evaluations for all test animals.

Histopathologic examinations of livers, hearts, and kidneys and all gross lesions 18 seen at necropsy were done on low-dose and mid-dose rats.

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