Short answer: Yes. Gender-affirming hormone treatment can change body composition and the assumptions used in a Widmark-style blood alcohol calculation. A 2022 forensic modeling paper found that choosing different sex-based total-body-water equations could materially change the calculated initial blood alcohol concentration. It did not measure alcohol concentrations in a trial of transgender volunteers, validate an individual correction factor, or establish impairment. The scientifically important issue is whether the report identifies what was measured, which physiological inputs were estimated, and which assumptions drive the final number.
In a forensic case, a numerical calculation is not an independent chemical measurement. Maskell and colleagues used previously measured body-water data and published hormone-treatment body-composition changes to examine an especially important model-input question: how should the analyst represent distribution volume when an individual’s physiology may differ from the population assumed by a standard equation? The complete original paper is Maskell et al., Journal of Forensic Sciences (2022), DOI: 10.1111/1556-4029.15052.
What exactly does a Widmark calculation estimate?
Widmark-style calculations use an assumed ethanol dose and an estimate of the volume into which ethanol distributes. In the formulation analyzed by the authors, the hypothetical initial concentration, designated C0, depends on the amount of ethanol, total body water (TBW), and the fraction of whole-blood volume consisting of water. Other forensic exercises also require defensible assumptions about when drinking occurred, absorption and elimination. Changing an input changes the output even when there has been no change in the actual evidence.
The paper’s C0 is a model output, not a blood specimen obtained at “time zero.” It is not the concentration measured by a laboratory, a direct measurement of brain ethanol, or a prediction of a person’s reaction time, judgment, gait, or driving performance. Calling this modeled quantity a “BAC reading” would conceal the central scientific limitation.
An actual forensic whole-blood ethanol result, when properly obtained, is different evidence: it is an analytical concentration reported for a specified specimen and collection time. An instrument’s breath-alcohol result is another measurement type. A retrospective reconstruction or dose-to-concentration estimate must be labeled as an inference, not substituted for either measurement.
Why can hormone treatment matter to alcohol distribution?
Ethanol distributes substantially through body water. Lean mass generally contains more water than adipose tissue; thus two individuals of similar body weight may have different distribution volumes. Gender-affirming treatment can alter fat-free mass, total fat, and hematocrit. Those physiological changes are relevant to the assumptions behind group-derived TBW equations, but they do not supply a direct measure of one person’s current body water.
The researchers drew on previously published longitudinal treatment data in which average lean mass fell by approximately 3% after 12 months among 179 transgender women and rose by approximately 10% among 162 transgender men. Those are group averages from the cited external study, not experimentally established changes in every person. Dose, duration, baseline composition, age, and other differences may matter; the 2022 model did not individually measure all of those variables in a new drinking experiment.
The authors also examined blood-water fraction indirectly. They used published changes in hematocrit and an established hematocrit-to-whole-blood-water relationship to estimate the blood-water component for their calculations. The modeled values were approximately 0.841 for transgender women and 0.826 for transgender men. Neither value was obtained by directly measuring whole-blood water in a newly recruited transgender cohort. Treating them as personally measured results would add certainty the study does not provide.
What was the study design—and what was actually observed?
This was an analytical modeling study built from existing data, not a randomized ethanol-administration trial in transgender participants. Two historical datasets contained directly measured TBW, using a tritiated-water dilution technique, for 582 cisgender men and 884 cisgender women. Published average body-composition changes associated with hormone treatment were then applied to model TBW after treatment. The authors used those modeled TBW figures, modeled blood-water fractions, and specified ethanol doses to calculate hypothetical C0 values under alternative equation assumptions.
Consequently, the sample sizes of 582 and 884 describe the underlying cisgender body-water datasets. They are not numbers of transgender people prospectively given alcohol, and the outputs are not measured pre/post-treatment BAC values. The key results quantify sensitivity of a mathematical procedure to its selected inputs.
| Component | What it represents | What it cannot establish |
|---|---|---|
| Historical TBW datasets (582 men; 884 women) | Previously measured total body water in cisgender populations | Direct body-water measurement in a transgender person on treatment |
| Published 12-month treatment changes | Average changes in lean mass used to adjust the model | An individual patient’s actual percent change |
| Hematocrit-based blood-water fraction | Estimated water content from an indirect physiological relationship | A new individually measured blood-water fraction or BAC |
| Modeled C0 at 16 g and 80 g doses | Hypothetical initial BAC obtained under alternate equations | A chemical test, observed peak BAC, impairment score, or event-time BAC |
How much could equation choice change the result?
The differences were not merely semantic. Table 5 of the source reports the following group-mean calculated C0 values for the authors’ hypothetical 80-gram ethanol dose. Units are milligrams per 100 milliliters of blood. These figures are illustrative outputs from alternative equations applied to modeled groups; they are not concentrations measured in persons who drank 80 grams in the study.
| Modeled group | Sex-at-birth equation | Affirmed-gender equation | Authors’ adjusted model |
|---|---|---|---|
| Transgender women after modeled treatment | 154 mg/100 mL (cis-male equation) | 188 mg/100 mL (cis-female equation) | 156 mg/100 mL |
| Transgender men after modeled treatment | 208 mg/100 mL (cis-female equation) | 172 mg/100 mL (cis-male equation) | 182 mg/100 mL |
For example, in the modeled transgender-women group, the cis-female equation produced a mean C0 of 188 mg/100 mL versus 156 mg/100 mL with the authors’ adjusted equation. For the modeled transgender-men group, the corresponding cis-female calculation was 208 mg/100 mL versus 182 mg/100 mL after adjustment. The result changes because the assumed distribution terms change, not because the study detected different blood ethanol in the same person.
The authors also modeled a smaller 16-gram ethanol dose. Their overall mean C0 calculations were 31, 38, and 31 mg/100 mL, respectively, for transgender women using the sex-at-birth, affirmed-gender, and adjusted models. For transgender men, the three corresponding model outputs were 42, 34, and 36 mg/100 mL. These are calculation demonstrations; they are not empirically validated dose-response tables for individual clients.
Is using a gender marker the same as measuring body water?
No. A demographic marker identifies a category for selecting a formula; it does not directly measure total body water, hematocrit, or actual blood ethanol. A choice between two population-derived equations can create markedly different outputs while leaving the person’s true physiology unknown. The correct scientific response is to identify that dependency and describe it, not to pretend that one label resolves all interindividual variation.
Maskell and colleagues suggested a transparent reporting assumption for circumstances in which sex assigned at birth is not known or disclosed. Their proposal concerned how to document the equation choice; it was not validation that a gender marker, birth classification, or treatment history supplies a person-specific distribution factor. The paper itself calls for more representative primary measurements and validation. Nor does a methodological recommendation in a 2022 article determine the governing legal rule in any particular jurisdiction.
When treatment history is relevant and appropriately available for a case, an analyst should distinguish documented facts from assumed population averages. A sensitive personal attribute should not be guessed or presented as an analytic measurement. The objective is to describe the evidentiary basis, alternatives, and uncertainty without drawing a conclusion about identity from a numerical model.
Can these percentages be used as a correction to measured BAC?
They should not be applied mechanically to a laboratory result. The percentages concern model-estimated TBW or hypothetical C0 under selected equations. For example, an approximately 17.7% modeled TBW underestimation for one comparison does not authorize adding or subtracting 17.7% from a forensic whole-blood GC result. TBW and BAC are different quantities, and the mathematical response is not universally linear across a changing individual, dose, and time.
Likewise, the 2022 paper did not establish a treatment-specific ethanol elimination rate, drinking-time correction, or impairment threshold. The authors noted that their forensic framework used existing elimination-rate ranges for both sexes. A drinking-history calculation can be sensitive to several assumptions at once: ethanol amount, completeness and timing of absorption, dose history, distribution, elimination, and specimen timing. This study isolates a model-input issue; it does not validate all the others.
Comparing a calculated C0 with a measured blood ethanol concentration also requires care. One may represent an idealized concentration immediately after hypothetical distribution; the other represents ethanol actually assayed at a documented time in a specific specimen. Agreement between numbers would not by itself validate all assumptions, and disagreement does not, by itself, identify which input was wrong.
What uncertainty did the authors acknowledge?
The authors explicitly described their adjusted equations as unvalidated. They modeled average changes following approximately 12 months of hormone treatment, drawing primarily on data from Western populations across a limited adult age range. The historical datasets and treatment cohorts were not a single sample in which actual body water, hormone exposure, and observed ethanol kinetics were prospectively measured together.
Differences between individuals—including baseline body composition, height, weight, treatment duration, and the response of fat-free mass—cannot be represented fully by one average percentage. The hematocrit-to-blood-water relation adds another inferential step. That uncertainty should be characterized, not hidden behind excessive decimal precision or the appearance of a personalized formula.
Importantly, the paper did not provide a validated prediction interval for one person’s post-treatment TBW or an external validation cohort for the proposed equations. Its numerical examples show potential modeling discrepancies, but they cannot determine that a particular person’s true C0 lies at the adjusted point estimate rather than elsewhere.
Does this paper establish alcohol impairment or crash causation?
No. The investigators did not give transgender participants ethanol and then measure driving, divided attention, field sobriety performance, reaction time, aggression, or crash outcomes. No field-valid diagnostic classifier was trained or tested. Even a well-validated blood ethanol measurement is distinct from a specific conclusion about an individual’s functional performance or legal responsibility at an earlier event time.
The study can support a critique of a model whose input categories were chosen without explaining their physiological assumptions. It cannot identify the alcohol concentration at the time of a disputed incident, establish that a person actually drank the posited number of grams, or connect an estimated number to a claimed act or injury. Those questions require independent case facts and appropriate scientific methods.
How should a lawyer or toxicologist review such an estimate?
Begin by identifying the precise proposition being advanced. Is the witness offering an actual blood test at a known draw time, a breath measurement, a modeled initial distribution concentration, or a retrospective estimate of an earlier concentration? The documentation should not shift between those categories without explanation.
Then identify the equation, inputs, units, body-water method, population assumptions, and uncertainty analysis. If a demographic category selected the equation, ask what physiology the category was meant to approximate and what evidence supports applying it to this individual. When an adjusted TBW estimate is invoked, determine whether it was directly measured or projected from published averages and whether the particular application was validated.
Finally, examine the dose and timing premises independently. Even a well-reasoned treatment of distribution volume cannot rescue an unsupported drinking quantity, an unknown absorption state, or an assumed elimination course. The two-blood-sample elimination research brief explains a separate timing limitation. The Attorney Scientists research library organizes additional analytical and interpretive reading. The site’s editorial and source policy explains how educational summaries differ from adopted expert opinions.
Frequently asked questions
Did the researchers measure blood alcohol in transgender volunteers?
No. They analyzed earlier body-water and treatment-composition datasets and computed hypothetical BAC outputs. This was not a new alcohol dosing trial in transgender volunteers.
Does hormone treatment always produce the study’s average body-water change?
No. The −3% and +10% average changes used in the model are group estimates, not measurements for every treated individual. Duration, baseline characteristics, and individual responses can differ.
Can the modeled 188 mg/100 mL result be described as a measured BAC?
No. It is one group-mean C0 under a selected hypothetical dose and equation. No blood specimen in this study yielded that number.
Does the paper validate a new Widmark formula for every person receiving treatment?
No. The authors specifically acknowledge that their proposed anthropometric adjustments have not been validated against a representative set of direct body-water measurements in that population.
Should an analyst simply add a fixed percentage to a forensic blood result?
No. The modeled distribution-volume discrepancies are not demonstrated correction factors for already-measured blood ethanol, breath ethanol, or an event-time reconstruction.
Does sex assigned at birth alone determine the person’s total body water?
No. A sex-based equation estimates a population tendency. It does not directly measure the individual’s body composition. The scientific question is what evidence supports a particular assumption and how uncertainty is disclosed.
Does a calculated C0 establish DUI impairment or responsibility for an event?
No. This paper has no functional impairment or crash-causation endpoint. A dose-to-concentration model cannot substitute for properly interpreted toxicology measurements and separate behavioral or event evidence.
Primary scientific source and interpretive boundary
Maskell PD, Sang KJC, Heymsfield SB, Shapses S, Dekorompay A. Forensic alcohol calculations in transgender individuals undergoing gender-affirming hormonal treatment. Journal of Forensic Sciences. 2022;67(4):1624–1631. DOI: 10.1111/1556-4029.15052. This appraisal addresses the published study’s model assumptions and aggregate calculations. It is educational scientific context, not a validated individual BAC correction, medical advice, or an opinion about a particular case.