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A postmortem study found no statistically significant difference in morphine concentrations between heroin users with pregabalin detected and a gabapentinoid-negative comparison group. That result did not establish that pregabalin reduced heroin intake, that doses were identical, or that the combination had no toxicological interaction.

Two comparisons addressed different questions

Nahar and colleagues screened femoral-vein blood from 3,750 coronial cases. The methods describe a two-year period, January 2016 through December 2017, although the abstract states a one-year end date. Of 354 people classified as heroin users, 69 were pregabalin-positive and 264 were negative for both pregabalin and gabapentin. These were distinct study groups rather than a complete two-way split of all 354.

The first comparison examined morphine concentrations between those 69 pregabalin-positive cases and 264 controls. A second analysis examined the relationship between pregabalin and morphine within 67 pregabalin-positive heroin cases after excluding two suspected intentional overdoses.

How heroin exposure was classified

Morphine had to be present in blood or urine. In addition, another heroin-related compound had to be detected, or the circumstances had to strongly support heroin use. Examples included 6-acetylmorphine or relevant scene evidence. Morphine detection alone was not treated as universally specific for heroin.

Nine pregabalin-positive heroin cases had no morphine detected in blood but had morphine and heroin-related compounds in urine. This matters when reading a blood concentration range beginning at zero: a zero blood entry did not mean the person failed the study’s exposure criteria.

Quantitative methods and the null findings

Pregabalin was measured by LC–MS/MS following protein precipitation, with a reported linear range of 0.5–50 µg/mL and positivity threshold of 0.5 µg/mL. Morphine-positive specimens underwent duplicate quantification by a validated solid-phase extraction and GC–MS procedure. The morphine assay was linear over 0.01–1.00 µg/mL; detection and quantification limits were both 0.01 µg/mL, and reported intra-day and inter-day imprecision were below 5%.

Reported morphine concentrations in the 69 pregabalin-positive heroin cases ranged from 0.00 to 1.46 µg/mL, with a median of 0.15 µg/mL. In the 264 controls, the range was 0.00–20.00 µg/mL and the median 0.13 µg/mL. The Mann–Whitney comparison gave p = 0.98, providing no significant distribution difference.

Within the restricted 67-case group, the results section reports no significant pregabalin–morphine association, with p = 0.95. The discussion gives a different nonsignificant value, p = 0.69. That inconsistency prevents presenting a single unqualified correlation p value; neither value supports a demonstrated association.

Why this did not measure an individual’s heroin dose

Unknown dosing times, staggered consumption and different elimination rates could separate the two measured concentrations. A postmortem blood value was not an administered-dose record. The study also lacked a uniform postmortem interval and individual paired antemortem results.

The absence of a significant concentration difference did not establish equivalent doses or equal toxicity. These comparisons were not designed as equivalence tests. They also did not directly measure respiratory depression or quantify a pregabalin–opioid interaction.

Co-detections remained relevant

Compared with controls, pregabalin-positive heroin cases more often had antidepressants, benzodiazepines, methadone, other opioids and antipsychotics detected; the reported comparisons were significant at p ≤ 0.001. Those associations complicated single-drug attribution, but did not identify which combination caused an individual death.

The defensible interpretation retains the null morphine findings alongside the broader co-detection pattern. For related exposure-classification limits, see negative autopsy drug screens and medication histories.

Primary source

Nahar LK, Murphy K, Paterson S. Toxicological Relevance of Pregabalin in Heroin Users: A Two-Year Postmortem Population Study. Journal of Analytical Toxicology. 2022;46:471–478. doi:10.1093/jat/bkab070.

Higher postmortem pregabalin concentrations in a heroin-use subgroup were a statistical finding, not a direct measurement of higher doses or a universal fatal concentration. A two-year study found different concentration distributions, but the authors cautioned that the magnitude did not establish substantially greater pregabalin toxicity in that subgroup.

The study tested an entire submitted-blood cohort

Nahar and colleagues studied 3,750 coronial cases with femoral-vein blood from deceased people aged at least 16 years. The methods and results specify January 2016 through December 2017; the abstract instead ends the period in December 2016. The two-year period stated in the methods is the basis for describing the cohort here.

Pregabalin was detected in 229 cases: 69 classified as heroin users and 160 as non-heroin users. The latter category did not establish lifelong abstinence from heroin. Classification depended on recent toxicology findings and supporting circumstances.

Screening included a quantitative method

Pregabalin and a deuterated internal standard were extracted from 100 µL of femoral-vein blood by acetonitrile protein precipitation and analyzed using LC–MS/MS. The reported linear range was 0.5–50.0 µg/mL, and a concentration of at least 0.5 µg/mL counted as positive. Positive samples were quantified in duplicate.

The observed case range extended above that stated calibration range. The population paper does not set out the dilution procedure for every high result, so the range should not be described as evidence of an undiluted assay validated to 540 µg/mL. Nor did the study provide a uniform postmortem interval or specimen-storage history for reconstructing individual concentration changes.

Which comparison produced which result?

Across all 229 positive cases, pregabalin ranged from 1 to 540 µg/mL. Among heroin users, the range was 1–540 µg/mL and the median 8.0 µg/mL; among non-heroin users, the range was 1–168 µg/mL and the median 5.5 µg/mL. The full-group Mann–Whitney comparison gave p = 0.012.

Investigators then removed two suspected intentional-overdose cases from the heroin group and eleven from the comparison group, using scene and case information. The remaining 67 and 149 cases had medians of 8 and 5 µg/mL, respectively, and the comparison gave p = 0.002. The 75th percentiles were 16 and 11 µg/mL. The smaller p value describes that restricted analysis, rather than the original 69-versus-160 comparison.

Why concentration was not a recovered dose

The authors interpreted the findings as compatible with greater pregabalin consumption among heroin users. They also identified an alternative explanation: different intervals between ingestion and death could produce different concentrations even without a proportional dose difference. Those intervals generally were unknown.

This was observational postmortem casework, not a controlled administration study. Prescription histories and doses were generally unavailable, preventing confident separation of prescribed treatment from illicit misuse. The paper discussed limited redistribution based on earlier literature; it did not measure each decedent’s antemortem-to-postmortem change.

What the result contributes to a death investigation

A measured concentration can support evaluation of exposure alongside other drugs, scene evidence and pathology. It cannot independently establish the dose, timing, impairment or cause of death. Comparison with therapeutic plasma ranges also requires caution because this study measured postmortem whole blood.

The significant distribution difference and the authors’ caution about clinical importance must travel together. For another example of why group comparisons cannot correct an individual result, see postmortem versus antemortem fentanyl comparisons.

Primary source

Nahar LK, Murphy K, Paterson S. Toxicological Relevance of Pregabalin in Heroin Users: A Two-Year Postmortem Population Study. Journal of Analytical Toxicology. 2022;46:471–478. doi:10.1093/jat/bkab070.

A later bedtime after amphetamine treatment can raise an important research question without answering it: did the medication shift the person’s circadian phase, prolong wakefulness, or coincide with a change in schedule? Those explanations require different evidence. A sleep complaint alone cannot identify which process occurred.

A supplied mini-review by Hanshaw, Shanks and Porcu, published in Frontiers in Pharmacology on August 7, 2026, brings these issues together for young adults with ADHD. Its value is an integrated research framework. It is a narrative review, not a new randomized experiment measuring medication-induced changes in the human body clock.

What did the review actually investigate?

The authors searched PubMed and Google Scholar for literature connecting ADHD, amphetamine-type stimulants, sleep, circadian rhythms and psychiatric outcomes. They preferentially considered work relevant to adolescents and young adults, alongside mechanistic research. This approach assembled evidence from different designs and populations rather than producing one pooled estimate of a circadian phase shift.

Consequently, there is no single administered dose, route, exposure interval or participant denominator that describes the entire review. A finding from prescribed oral treatment, an observation in people with amphetamine dependence and a laboratory result in an animal model do not represent interchangeable exposures. The review should not be read as though every cited study tested the same proposition.

Why are bedtime and circadian phase different measurements?

Bedtime records when someone attempts to sleep. Sleep onset records when sleep begins. Sleep-onset latency measures the interval between those events. Total sleep time addresses duration. A person can go to bed later yet fall asleep promptly, or go to bed at the usual time and remain awake longer. Those patterns can produce different interpretations even when the eventual sleep-onset time is identical.

Circadian phase asks a separate question about the timing of an internal rhythm. The review identifies dim-light melatonin onset, or DLMO, among the objective measures needed in future research. Actigraphy can help characterize sleep and activity timing; a preference for evenings describes chronotype. Neither an evening preference nor a reported late bedtime supplies the same information as serial measurement of a circadian marker under specified conditions.

This distinction matters when describing a result. “Participants reported later sleep” is a narrower claim than “amphetamine delayed their biological clock.” The latter requires evidence about internal timing, along with a design capable of evaluating the medication’s contribution.

What could explain an association with stimulant treatment?

The review expressly acknowledges uncertainty in observational comparisons. People with more severe ADHD symptoms or greater pre-existing circadian difficulties may be more likely to receive medication. Their baseline characteristics can therefore contribute to a difference observed between treated and untreated groups.

Academic demands, nighttime light exposure and irregular schedules also belong in the research question. In an individual timeline, recording medication initiation without recording changes in those factors leaves competing explanations unresolved. For causal analysis, the relevant comparison is more demanding than simply observing that medication and sleep difficulty occurred together.

How should the human and animal evidence be separated?

The review discusses dopaminergic and noradrenergic arousal pathways and animal research involving clock-related processes. These observations support biological hypotheses. They do not establish the size, direction or duration of a circadian phase change in a particular young adult taking a particular amphetamine formulation.

The authors also identify limited direct comparisons of immediate-release and extended-release amphetamine formulations using circadian phase markers. A formulation’s longer drug-exposure profile cannot substitute for measuring the circadian outcome. Nor does a mechanism proposed from animal work establish an individual human impairment finding.

What would a stronger study need to measure?

A study designed to test a clock-shift claim would need baseline and follow-up circadian measurements, a clearly specified medication exposure, and an appropriate comparison condition. Dose, formulation, administration time and time since dosing would need to remain identifiable. Sleep opportunity, actual sleep, light exposure and concurrent substances would need attention as potential influences on interpretation.

The review’s discussion of psychiatric outcomes adds another distinction: a proposed pathway through sleep or circadian disruption is not proof that this pathway mediated a particular outcome. Demonstrating mediation requires evidence connecting exposure, the proposed intermediate change and the subsequent outcome over time.

For forensic interpretation, this review supports careful separation of exposure history, observed sleep behavior and measured internal timing. It provides no blood or urine concentration threshold for a circadian disturbance, no individual diagnostic test for impairment, and no basis for attributing a person’s behavior to amphetamine solely because the drug was detected.

Source and scope

Hanshaw A, Shanks A, Porcu A. Amphetamine-type stimulants in young adults with ADHD: effects on sleep, circadian rhythms, and neuropsychiatric outcomes. Frontiers in Pharmacology. 2026;17:1891825. doi:10.3389/fphar.2026.1891825. Full text.

This is original commentary on the supplied narrative review. Findings of underlying studies are discussed as summarized by that review; they are not presented as separately validated primary-study results. No source figures or tables are reproduced.

Death certificates, forensic files and integrated case review did not produce identical pharmaceutical-poisoning counts in a Swedish population study. Twelve deaths were classified from certificates, ten from medico-legal files and nine after multidisciplinary review of all available records. Adding those counts would double-count overlapping assessments rather than identify 31 separate deaths.

The population was sampled beyond the autopsy room

Jönsson and colleagues studied deaths during 2001 in Östergötland, Jönköping and Kalmar counties, with a mean population of 974,884. They randomly selected one in every seven of the 11,015 recorded deaths, yielding 1,574 study subjects.

Certificates were obtained for all 1,574. Medical records were available for 1,503, prescription information for 1,553 and forensic autopsy records for 83. Thus, this was not a toxicology-screened series of 1,574 autopsies. Most subjects were assessed through records rather than a uniform postmortem laboratory protocol.

What counted as pharmaceutical poisoning?

The study concerned pharmaceutical agents, not every possible illicit-drug or chemical poisoning. Review focused particularly on drug histories during the 14 days before death. Two pharmacists, two clinical pharmacologists and a forensic pathologist evaluated possible cases.

Classification required consensus and at least a possible relationship between drug toxicity and death, with known toxic potential and reasonable timing. Other contributing causes could coexist. The study’s classification was therefore a structured causality judgment, not a rule equating a positive drug result with poisoning.

The three counts answered different questions

Death certificates identified 12 possible pharmaceutical-poisoning fatalities and medico-legal files identified ten. Integrated review retained nine. All nine were represented on death certificates, which also listed three additional cases. The study did not show that consulting more records necessarily increases the final count; fuller information can also lead reviewers to exclude a suspected case.

Cases with more likely alternative explanations or unresolved inconsistencies were excluded under the authors’ conservative assessment. This approach reduced some classification uncertainty but did not establish an infallible reference standard for the true cause of every death.

Of the nine retained cases, four were intentional, three unintentional and two of unknown intent. Only seven had forensic autopsies. Reviewing every tenth of the 1,565 remaining deaths found no additional pharmaceutical-poisoning cases; that check was not exhaustive re-examination of all negatives.

What toxicology contributed when available

Forensic autopsies included femoral blood, urine and vitreous screening for drugs and ethanol. Drug concentrations were interpreted alongside medical history and circumstances. The report did not provide a uniform collection interval, storage history or numerical analytical-performance dataset covering all subjects.

Table 2 illustrates why analyte identity matters: one nitrazepam-associated result was actually measured as its metabolite 7-aminonitrazepam, reported at 0.7 µg/g in femoral blood. That should not be relabeled as a measured parent-nitrazepam concentration. Some retained poisoning cases lacked a postmortem femoral concentration altogether.

Two different denominators produced two different measures

The nine cases represented 0.57% of sampled deaths, with a 95% confidence interval of 0.20–0.94%. The estimated population incidence was 6.5 per 100,000 person-years, with a 95% interval of 2.3–10.7. A proportion of deaths and an incidence rate in the general population are not interchangeable quantities.

These are historical, regional estimates published in 2009 from 2001 data. They do not establish today’s poisoning incidence or an individual’s risk from a prescription. Only nine cases contributed to the final estimate, limiting precision and comparisons among drugs.

What the study supports in case review

Toxicological measurements, diagnoses on certificates and final causal assessments should be kept distinct and reconciled through the underlying records. The study supports integrating those sources while preserving missing evidence and alternative causes. It does not validate a concentration threshold, impairment test or automatic cause-of-death rule.

For another counting distinction, see unconfirmed findings in postmortem surveillance totals.

Primary source

Jönsson AK, Spigset O, Tjäderborn M, Druid H, Hägg S. Fatal drug poisonings in a Swedish general population. BMC Clinical Pharmacology. 2009;9:7. doi:10.1186/1472-6904-9-7.

An ultrarapid CYP2D6 prediction from postmortem DNA did not establish why a particular person died of opioid toxicity. A 2020 veteran study found five predicted ultrarapid metabolizers among 74 usable samples, about 7%, but did not establish that this phenotype was overrepresented in overdose deaths. The relevant toxicology subgroup comparison was also nonsignificant.

How were the decedents selected?

Boyle and Stock identified Utah military veterans who received care at one VA medical center, had a hydrocodone, oxycodone or tramadol prescription in their record, and were identified through overdose death records from January 2009 to January 2016. Banked autopsy blood had to be available.

They obtained 75 samples; one failed genotyping because of poor DNA amplification, leaving 74. Participants were predominantly male, 97%, and Caucasian, 95%, with a mean age of 51 years. These selected records were not a matched case-control study of all opioid users, all veterans or all people who died.

The laboratory predicted function from DNA

Autopsy blood had been placed on FTA filter-paper cards. DNA was extracted from 3 mm punches, then analyzed by an iPLEX genotyping assay and MassARRAY, with copy-number assessment. Each sample was tested twice and the laboratory reported strong concordance, despite variable DNA quantity and quality.

The method tested a defined variant panel. If no targeted variant was detected, the allele was presumed functional *1. That assumption and the panel’s coverage belong with the interpretation; the study did not sequence every possible functional variation.

The authors used historical activity-score categories to predict phenotype: poor at zero, intermediate at 0.5, extensive at 1–2 and ultrarapid above two. This was genotype-to-phenotype translation under the study’s classification scheme, not direct measurement of liver enzyme expression or real-time metabolic activity immediately before death.

The toxicology denominators narrowed further

Toxicology records were available for 68 decedents, of whom 49 had an opioid detected. Twenty of those 49 had hydrocodone, oxycodone or tramadol detected; 29 had other opioids. A prior prescription therefore did not establish that the prescribed drug was present at death.

The paper did not provide one uniform anatomical blood collection site, postmortem interval, quantitative opioid assay range or drug concentration series for this analysis. The genetic results cannot be combined with invented drug concentrations to reconstruct an active-metabolite exposure.

What was statistically significant?

Three of the 20 decedents with the selected prescription opioids detected had an ultrarapid prediction, compared with one of 29 with other opioids: 15% versus 3.4%. Fisher’s exact test gave p = 0.291, which was not statistically significant. The numerical difference should not be presented as a demonstrated association.

The overall five-of-74 finding included an ultrarapid prediction in a person without opioids detected whose death was classified as suicide by asphyxia. There was no directly measured veteran comparison population establishing the expected background frequency. Small numbers precluded a confident overrepresentation claim; a null result did not prove equal risk.

Co-detections and missing evidence limited causation

Twenty-four of the 49 opioid-positive people had non-opioid substances detected, including potentially sedating medications or alcohol. Detection alone did not quantify an interaction, but it complicated attributing death to a single genetic pathway.

The study examined no adherence proxy. Absence of a prescribed opioid could not establish deliberate nonadherence, and the records did not provide a validated dose–genotype–concentration model for individual death reconstruction.

What can the finding contribute?

Postmortem genotyping can add information about inherited drug-metabolism potential. It does not independently prove a toxic active-metabolite concentration, impairment, dosing error or cause of death. Interpretation must retain the exposure evidence, genetic test scope, prediction rules and full case context.

For a separate example of record-based exposure limits, see negative autopsy screens and medication exposure. The genetic study’s conclusions describe its historical data and do not constitute a current prescribing guideline.

Primary source

Boyle J, Stock CJ. CYP2D6 Expression in Veterans Experiencing Opioid Overdose: A Postmortem Review. Pharmacogenomics and Personalized Medicine. 2020;13:289–293. doi:10.2147/PGPM.S261424.

A postmortem paroxetine concentration of 3.3 mg/L supported poisoning in a reported mixed-drug fatality, but did not diagnose serotonin syndrome. The authors specifically stated that they could neither document nor exclude serotonin syndrome or an arrhythmic mechanism. Their cause-of-death conclusion combined toxicology with the scene, history and examination.

The specimen was external iliac blood

Florou and colleagues described a 22-year-old man found dead after treatment for psychiatric illness. The scene included empty or partly empty medication packages. Death was estimated at 18–24 hours before discovery; transfer to the mortuary occurred eight hours after discovery, and autopsy 12 hours after transfer. These were estimated and documented intervals, not a precisely known ingestion-to-death timeline.

Autopsy specimens were blood from the left external iliac vein, bladder urine and gastric contents. The blood was not femoral blood and not serum. It was collected in a 2 mL tube containing 5 mg sodium fluoride and 4 mg potassium oxalate. Samples remained at 4°C while analyses were completed within two weeks, then were retained at −20°C for possible reanalysis.

Early putrefactive changes were present. Histology was not performed because of the tissue condition. Cerebral and pulmonary edema and lung congestion were described, without a specific structural finding establishing serotonin toxicity.

Screening and quantification were different procedures

The investigation included immunoassay screening, full-scan GC–MS toxicological screening and headspace GC–FID for ethanol. Paroxetine was quantified by LC–MS/MS, with the blood procedure modified for urine and gastric-content work. Analyses were performed in triplicate.

The reported paroxetine concentration was 3.3 mg/L in blood and approximately 1.6 mg/L in urine; the abstract gives the more precise urine value of 1.58 mg/L. Table 1 expresses concentrations in µg/mL, numerically equivalent to mg/L. This unit equivalence does not make blood, urine and serum biologically interchangeable.

The case report did not reproduce numerical detection limits, quantitation limits, calibration ranges or precision data for the modified analyses. Triplicate testing is useful but does not, by itself, establish every aspect of quantitative validation in each matrix.

What else was detected?

Table 1 reports alprazolam at 0.038 µg/mL, alpha-hydroxy-alprazolam at 0.0056 µg/mL and quetiapine at 0.0013 µg/mL in blood. Norclozapine was reported in urine at 0.0165 µg/mL, without a corresponding positive blood entry. Gastric drug findings were qualitative, not a measured recovered dose.

The authors described alprazolam as within a referenced therapeutic range and quetiapine as below it, while considering them possible contributors in combination with paroxetine. Neither a therapeutic-range comparison nor detection alone establishes the magnitude of a drug interaction in one deceased person.

Why mechanism remained unresolved

Serotonin syndrome is not established by identifying an SSRI or by assigning a concentration to it. This report lacked the clinical observations needed to demonstrate that syndrome before death. The authors also could not document QT prolongation or ventricular arrhythmia as the mechanism.

They attributed the death to mixed-drug intoxication with paroxetine as the primary toxicant and described additive or synergistic contributions from other medications. That was an integrated case interpretation, not an experimentally quantified interaction or a statistical comparison between exposed groups.

Postmortem change prevents an exact reconstruction

The authors acknowledged that putrefaction and redistribution could have altered concentrations in either direction. Peripheral collection reduced some concerns but did not establish an unchanged antemortem concentration. There was no paired predeath specimen or serial postmortem study from which to calculate the individual’s change.

The case therefore supplies no universal lethal cutoff, ingested dose, ingestion time or impairment threshold. The appropriate conclusion preserves the measured result and specimen history while separating the authors’ poisoning attribution from the unproven mechanism.

Related interpretation issues appear in clozapine detection and causal attribution and matrix-dependent parent/metabolite ratios.

Primary source

Florou D, et al. Poisoning suicide due to paroxetine overdose toxicity aided by benzodiazepine and antipsychotic drugs. International Journal of Legal Medicine. 2025;139:2713–2719. doi:10.1007/s00414-025-03570-7.

Cocaine on postmortem skin can reflect exposure, surface contamination or a combination. A 2026 study strengthened some exposure assessments by detecting networks of cocaine metabolites in skin extracts, including cases with negative conventional toxicology reports. It did not establish that any single skin metabolite conclusively excludes contamination or determines when cocaine was used.

The specimen and study setting

Wasinger and colleagues collected dermal extracts from 93 people during medicolegal autopsy. Water-wetted paper was pressed against fingers and soles for five minutes. Initial cases included a brief hand-rinsing comparison, while subsequent collections generally used unwashed hands and feet. Samples were frozen at −20°C for up to three months before dilute-and-shoot extraction and untargeted high-resolution LC–MS/MS analysis.

Exact death timing was unavailable for 35 people; known-time autopsies extended to 18 days after death. This was not a controlled cocaine administration experiment or a controlled environmental-contamination challenge. Its results must retain that casework context.

Why cocaine alone was not the decisive finding

Dermal analysis reported cocaine in 51 people, compared with six conventional toxicology reports. Benzoylecgonine was reported in 37 dermal assessments versus nine conventional reports. Those differences did not establish 45 missed cocaine users or 28 proven missed benzoylecgonine exposures.

The authors recognized that both cocaine and benzoylecgonine can occur through surface contamination. They therefore examined additional metabolites: ecgonine, hydroxybenzoylecgonine, norbenzoylecgonine and norcocaine, alongside ecgonine methyl ester and other available findings.

What the metabolite-network assessment added

In nine cases identified as cocaine consumption through conventional toxicology, dermal extracts contained at least four additional cocaine metabolites. Applying the expanded assessment to other dermal-positive cases identified eight further people with networks of four or more metabolites, resulting in 17 suspected cocaine consumers.

The eight additional people had died from other drug intoxications in six cases and polytrauma in two. Their other toxicology findings made cocaine exposure plausible, but those circumstances were corroborating context rather than independent proof of cocaine use.

Ecgonine methyl ester was consistently detected across sampled sites in all 17 suspected consumers. Hydroxybenzoylecgonine and norcocaine were robustly detected across matched sites in 15 of 17. Multiple chemically related findings and more than one anatomical sampling location gave a stronger basis for interpretation than an isolated parent-drug signal.

Alcohol-related markers had a narrower role

Twelve suspected cocaine consumers had ethanol reported in blood or urine by conventional testing. Eleven of those had both cocaethylene and ethyl glucuronide identified in dermal extracts. This supported the researchers’ co-exposure interpretation; it did not quantify the earlier blood ethanol concentration or establish a contribution to death.

The dermal LC–MS/MS method did not directly determine ethanol because of its mass-range constraints. A positive ethyl-glucuronide finding should not be rewritten as a measured ethanol concentration.

Why the study did not settle contamination in every case

The dermal results were qualitative. Candidate identification used exact mass, isotope patterns and MS/MS fragmentation, with authentic-standard verification for a subset of compounds. The study did not establish a validated environmental cutoff, metabolite-count threshold with known sensitivity and specificity, or a human dose-to-skin calibration.

The authors proposed that drugs may remain in skin longer than in conventional matrices. That explanation was plausible but not a controlled detection-window measurement. They also acknowledged possible effects of postmortem redistribution and decomposition and called for more data to distinguish exposure, contamination and postmortem change.

What a defensible interpretation preserves

Report the parent and metabolite identities, sampling sites, identification certainty and comparator findings. Describe the network as strengthening an exposure assessment rather than converting every discordance into a proven missed exposure. None of these qualitative findings measures impairment or establishes cocaine as a cause of death.

For another problem involving the source of a metabolite, see shared nitazene metabolites and parent-drug attribution.

Primary source

Wasinger D, Stolz K, Wolf M, et al. Analysis of postmortem dermal extracts is an efficient illicit drug screening method supporting forensic medicine. Scientific Reports. 2026;16:14183. doi:10.1038/s41598-026-45056-5.

A postmortem skin-extract study reported approximately 95% average positive identification overlap with conventional toxicology for 19 selected drugs and metabolites. That is promising agreement, but it is not independently validated 95% sensitivity for all drugs, all deceased people or every collection condition. The investigators expressly lacked a definitive ground truth for calculating sensitivity and selectivity.

Who was studied and how was skin sampled?

Wasinger and colleagues studied 93 people undergoing medicolegal autopsy in Vienna over one year. They included 37 females and 56 males, aged 17–94 years. Exact time of death was unknown in 35 cases. In cases with known timing, the methods report a mean death-to-autopsy interval of 8.7 days and maximum of 18 days; the discussion uses a different average of 8.3 days, so the report does not support assigning one uniform interval to all cases.

Water-wetted paper was held against the fingers or sole for five minutes. The first 23 people had three collection sites, including a rinsed right hand and an unwashed left hand. Later cases generally had an unwashed hand and foot sampled. Samples were stored at −20°C for up to three months before extraction and analysis.

This was extraction from the postmortem skin surface, not active sweat production from a living person. It also was not a validated quantitative measurement of the amount of drug in the entire skin.

What was the comparator?

Routine forensic toxicology used a validated screening procedure covering more than 400 compounds in available blood, urine, gastric contents and tissues. Peripheral blood was femoral arterial blood. Other collections included venous sinus blood and brain; advanced autolysis sometimes required sampling mixed brain material rather than defined regions.

The research dermal method used untargeted high-resolution LC–MS/MS. Candidates were evaluated through exact mass, isotope patterns and fragmentation. Nine substances had additional authentic-standard retention-time verification. Identification evidence was therefore not identical for every reported compound.

The percentages depended on compound and comparison

Conventional toxicology identified 123 different compounds across the cohort; 82 were identified in dermal extracts. That 82/123 compound coverage is a separate measure from average positive overlap for the selected 19-drug comparison.

Average positive overlap was approximately 95%, while average negative overlap was approximately 87%. Results varied materially: fentanyl positive overlap was 69.2%, norfentanyl 50%, morphine 91.3% and ketamine 91.7%. Several other targets had 100% positive overlap, sometimes based on only one or two comparator-positive cases.

Equal marginal counts did not guarantee matching people. Fentanyl was reported in 13 people by each approach, yet positive overlap was only 69.2%. The two sets of 13 were not identical. Nor can one target’s 100% overlap in two cases establish high precision for its future performance.

Why disagreement did not identify a universal winner

Some dermal-positive findings lacked a conventional-positive counterpart. Possible explanations included contamination, different biological detection windows and differences in analytical scope. Conventional toxicology was useful as a comparator but did not independently establish the true exposure status for every discordant finding.

The authors acknowledged that the untargeted method had limited sensitivity and required further targeted-method development. Its favorable aggregate overlap should not be presented as complete confirmation or an established replacement for conventional forensic testing.

No blood concentration could be recovered from a skin signal

The dermal evaluation was qualitative. Collection lacked a standard specimen volume or weight for normalization, and quantitative signals varied substantially. The paper did not establish analyte-specific dermal calibration, validated concentration cutoffs for exposure or a conversion to blood concentration.

The method may help identify cases needing further investigation. It does not independently establish dose, impairment or cause of death. For related distinctions, see the scope of a negative autopsy drug screen and why accurate mass alone does not settle identity.

Primary source

Wasinger D, Stolz K, Wolf M, et al. Analysis of postmortem dermal extracts is an efficient illicit drug screening method supporting forensic medicine. Scientific Reports. 2026;16:14183. doi:10.1038/s41598-026-45056-5.

Feeling connected to a person during a conversation and feeling greater trust toward society are different outcomes. A supplied paper by Martinez and colleagues illustrates why those outcomes should remain separate: a small methamphetamine experiment found no significant improvement in broader subjective or social well-being after a structured conversation.

What did the methamphetamine experiment involve?

Study 2 included 20 healthy adults in a double-blind, counterbalanced comparison of 20 mg oral methamphetamine and placebo. Participants attended separate drug and placebo sessions and spoke for 45 minutes with a same-sex stranger who was a trained confederate. The conversation partner did not receive the drug and was unaware of the participant’s assigned condition.

The reported schedule placed capsule administration at 9:30 a.m., conversation onset at 10:40 a.m., and the later questionnaire assessment at 1:30 p.m. The paper describes its main broader well-being endpoint as approximately 90 minutes after the conversation. That timing should not be confused with a measurement taken immediately after swallowing the capsule or with a drug-free assessment days later.

The outcome was a questionnaire, not a behavior or concentration

The investigators used nine subscales from the Comprehensive Inventory of Thriving: trust, self-worth, life satisfaction, optimism, meaning, belonging, support, respect and engagement. Each subscale combined three self-report items rated on a five-point scale. The trust items addressed the broader social world, rather than only the immediate conversation partner.

The study therefore measured reported perceptions under a defined laboratory procedure. It did not measure driving, truthfulness, susceptibility to coercion, or the ability to give consent. No blood or oral-fluid concentration threshold for any of those capacities was established.

What did the null methamphetamine result look like?

The investigators found no significant methamphetamine–placebo differences in these broader well-being outcomes, whether comparing post-session ratings or changes from orientation. For trust gain scores, Table 3 reported means of −0.08 after methamphetamine and −0.12 after placebo, with standard deviations of 0.58 and 0.55, respectively; the paired analysis had p = .716.

That is evidence that this small experiment did not demonstrate an increase in global trust under its conditions. It is not an equivalence finding proving that the drug has precisely zero effect. A study of 20 participants leaves uncertainty about small effects, individual variation, different doses and other settings.

Why does the MDMA comparison need restraint?

Study 1 separately compared 100 mg oral MDMA with placebo in 15 analyzed participants. It reported higher global trust after MDMA, with p = .022, while its self-worth finding had p = .065 and did not meet the conventional .05 threshold. The paper presented several well-being outcomes; a positive result for one cannot be expanded into demonstrated improvement across all of them.

Crucially, the two drugs were tested in separate participant samples. Prior MDMA experience was required for the MDMA sample, whereas the recruitment criteria differed for the methamphetamine sample. The authors explicitly identified the need for a design permitting direct drug comparisons. A significant result in one sample and a nonsignificant result in another do not, by themselves, establish a statistically significant difference between the drugs.

How can sociability coexist with a global-trust null finding?

The authors explain that earlier analyses from the same experiments addressed connection and enjoyment during the conversations. Those immediate partner-focused outcomes and the later society-focused questionnaire concern different constructs. Treating publications from the same experiments as independent replications would also overstate the evidence base.

The practical lesson is to identify the exact outcome before making a claim about “social effects.” A person can describe a pleasant interaction without reporting a change in trust toward a neighborhood or society.

Limits on generalization

Participants were screened healthy volunteers, with exclusions for several psychiatric conditions and substance dependence. The dose was a single oral 20 mg administration. The findings cannot be transferred directly to smoked or injected exposure, repeated dosing, prolonged wakefulness, or clinical methamphetamine use disorder.

The later questionnaire could still reflect acute drug effects. The study did not demonstrate durable benefit after drug clearance. Its small samples, multiple outcomes, self-report measures and differences between the two experiments support cautious, outcome-specific interpretation.

Source

Martinez RL, Radošić N, Molla H, de Wit H, Lyubomirsky S. The empathogen 3,4-methylenedioxymethamphetamine, but not methamphetamine, increases feelings of global trust. Journal of Psychopharmacology. 2026;40(9):1361–1367; supplied article carries a 2025 copyright. doi:10.1177/02698811251370999. Two separate placebo-controlled human experiments; broader social outcomes from previously studied laboratory sessions.

A person who reports having used methamphetamine and sildenafil at some point in life has not necessarily taken them together. That distinction matters when reading Fisher and colleagues’ study of men recruited through HIV prevention and testing programs in Long Beach, California. The study identified associations worth investigating, but it did not experimentally administer a drug combination or establish a pharmacological interaction.

What population did the researchers actually study?

The investigators collected information from 1,839 men between May 2001 and July 2007. Participants were recruited through three prevention or testing programs, rather than sampled randomly from all residents. Their ages ranged from 15 to 88 years. The article appeared online in 2009 and in the 2011 volume of Archives of Sexual Behavior. These are historical observations, not estimates of present-day prevalence.

Missing information reduced the multinomial analysis to 1,794 complete cases. Within that analytic sample, 762 reported neither drug, 715 methamphetamine only, 118 sildenafil only, and 199 both. The last category represented 11.1% of complete cases. It should not be presented as an 11.1% prevalence of simultaneous methamphetamine–sildenafil ingestion in the general population.

Why does the exposure definition matter?

The interview instruments asked about lifetime drug use, as well as more recent frequency and sexual behavior. The four-group classification was based on reported use histories. Membership in the “both” category alone cannot establish the date, dose, route, sequence, or temporal overlap of the two drugs. It also cannot establish the identity or concentration of a drug in a specimen from a particular encounter.

The paper separately examined reported methamphetamine use immediately before or during sex. It reported 24% in the methamphetamine-only group and 33% in the group reporting both drugs, with an analysis denominator of 542 and p = .0502. That result sits just above the conventional .05 threshold; describing it as an unequivocally significant difference would erase a relevant qualification. It also does not transform every lifetime report into documented same-event co-use.

The infection comparisons had different denominators

Table 1 reported laboratory results for subsets of the sample. The hepatitis B comparison included 631 tested participants and the HIV comparison 1,021. The two syphilis-test comparisons included 580 and 582. Those denominators differ from both the 1,839 recruited participants and the 1,794 complete cases used in the regression.

For example, the table reported HIV positivity of 9.5% in the group reporting both drugs and 3.8% in the neither-drug group. These are proportions among tested members of those groups. They are not prospective probabilities of infection after a dose, and the table does not justify dividing all infection counts by the full recruitment sample.

What did the statistical model predict?

The multinomial model compared drug-use categories with the neither-drug reference category. In the both-drugs comparison, lifetime GHB/GHL use had an odds ratio of 10.8, with a 95% confidence interval of 4.30–28.80. The direction of that model is important: the outcome was membership in a reported drug-use group. This number is not a 10.8-fold increase in HIV risk caused by GHB, nor a measurement of the toxicity of a three-drug combination.

The overlapping histories of cocaine, ecstasy and other drug use also make it difficult to attribute an observed behavioral or health difference to methamphetamine alone.

What can this evidence establish for an individual?

It supports an association between particular reported exposure histories and behavioral or infection markers in a selected service population. Self-report, recruitment through prevention programs, incomplete testing, and the absence of established temporal sequence constrain causal interpretation. Infection could precede a reported drug-use pattern; shared social or behavioral factors could contribute to both.

For a forensic evaluation, the study supplies background for asking precise questions about exposure and chronology. It does not establish an individual’s intoxication, sexual conduct, consent, source of infection, or impairment. Those conclusions require evidence about the actual person and event.

Source

Fisher DG, Reynolds GL, Ware MR, Napper LE. Methamphetamine and Viagra Use: Relationship to Sexual Risk Behaviors. Archives of Sexual Behavior. 2011;40:273–279. Published online March 28, 2009. doi:10.1007/s10508-009-9495-5. Historical observational survey; no controlled drug administration.