The original study, published in the Journal of Clinical Sleep Medicine, followed a 25-year propensity-matched cohort of veterans with narcolepsy and compared their outcomes against patients treated at general sleep clinics, most of whom carried diagnoses of obstructive sleep apnea. The findings raised eyebrows because the excess mortality appeared concentrated in patients classified under an “other narcolepsy” category rather than in those with narcolepsy type 1, the classic form of the disease characterized by cataplexy and low cerebrospinal fluid levels of the wakefulness neuropeptide hypocretin. This inversion of expectations prompted Fei and colleagues to ask whether the “other narcolepsy” group was simply a diagnostic dumping ground, a mixture of narcolepsy type 2, unspecified narcolepsy, and misclassified hypersomnolence conditions whose apparent mortality excess might be an artifact of sloppy phenotyping rather than a genuine biological signal.

In their reply, Sharafkhaneh and his coauthors, an international roster that includes Yves Dauvilliers of Montpellier, Ahmed BaHammam of King Saud University, Michael Thorpy of Albert Einstein College of Medicine, Fang Han of Peking University, and Turkish sleep specialists Gulcin Benbir Senel and Murat Aksu, do not dispute that heterogeneity exists. Instead, they argue that it is an unavoidable and explicitly acknowledged feature of real-world electronic health record phenotyping. The researchers intentionally defined the other narcolepsy group as a heterogeneous category, and they point out that their design required at least two diagnostic codes recorded 30 to 390 days apart before a patient entered the cohort. That temporal spacing requirement, they explain, was engineered to improve diagnostic specificity by filtering out transient miscoding and diagnostic uncertainty, the kind of single-visit noise that plagues administrative claims data.

The technical crux of the debate revolves around the multiple sleep latency test, or MSLT, the gold-standard physiological instrument for distinguishing narcolepsy type 1 from narcolepsy type 2 and idiopathic hypersomnia. The test measures how quickly patients fall asleep during scheduled daytime naps and whether they enter rapid eye movement sleep abnormally early, a phenomenon known as sleep-onset REM periods. Fei and colleagues highlighted the well-documented poor test-retest reliability of the MSLT in noncataplectic hypersomnia, citing influential work by Lynn Trotti and colleagues showing that patients can flip between diagnostic categories when the test is repeated. The reply authors acknowledge this limitation candidly but note a practical reality: MSLT data were simply not available in a standardized manner across the national Veterans Health Administration dataset, making physiological confirmation impossible for a cohort of this scale.

Rather than abandon physiological rigor altogether, the investigators constructed their cohort using a validated computable phenotyping algorithm analogous to the approach applied by Mayo Clinic researchers in prior narcolepsy cohort studies. This algorithmic strategy, the authors argue, does not eliminate misclassification, but it meaningfully minimizes the single-code phenotyping errors that arise when researchers rely on any one diagnostic entry in isolation. The distinction matters because the entire interpretive weight of the mortality finding depends on what the other narcolepsy group actually contains. If the group is a genuine mixture of undertreated or atypically presenting hypersomnolence patients, the authors contend, one would expect mortality rates equal to or higher than those seen in narcolepsy type 1, precisely the pattern the data revealed. Crucially, they emphasize, their findings highlight this heterogeneity but do not imply that narcolepsy type 2 itself carries an inherently elevated mortality risk.

Fei and colleagues also raised the possibility that positive airway pressure therapy, widely prescribed for obstructive sleep apnea, might have artificially lowered mortality in the general sleep clinic comparator group, citing a recent meta-analysis by Benjafield and colleagues suggesting that PAP use is associated with reduced all-cause and cardiovascular mortality. The reply authors treat this as a plausible but ultimately insufficient explanation. Their data showed obstructive sleep apnea prevalence of 65.4 percent in the general sleep clinic cohort compared with only 33 to 37 percent in the two narcolepsy groups, yet differential PAP exposure cannot explain why mortality differed between the other narcolepsy group and narcolepsy type 1, since neither group received PAP at rates approaching the comparator. The confounding argument, in other words, addresses the wrong contrast.

Perhaps the most striking observation in the exchange concerns comorbidities. The general sleep clinic cohort carried a substantially heavier baseline burden of cardiovascular, metabolic, pulmonary, and psychiatric disease than either narcolepsy group, yet the elevated mortality in narcolepsy persisted despite this seemingly protective asymmetry. If comorbidity profiles alone drove the mortality differences, the general sleep clinic patients should have fared worse. The authors are careful to stress that their study cannot identify causal mechanisms; what it delivers is a significant association that they argue warrants deeper investigation in cohorts enriched with physiologic data and treatment-adherence information, ideally including device-level PAP downloads and pharmacy fill trajectories that would allow researchers to model therapeutic exposure directly.

Treatment exposure emerges as a genuinely promising mechanistic thread in the reply. In the real-world VA data, patients with narcolepsy type 1 received sodium oxybate, stimulants, and wake-promoting agents more frequently than the other narcolepsy group, a pattern the authors attribute to greater diagnostic certainty and the established therapeutic indication for cataplexy rather than to disparities in clinical access. Fei and colleagues noted that low-sodium oxybate formulations are associated with a more favorable cardiovascular profile, referencing a 2025 study in Hypertension comparing the blood pressure effects of high- versus low-sodium oxybate in narcolepsy patients. The reply authors agree that differential pharmacologic exposure deserves further research, and they go further, suggesting that unequal treatment exposure may itself represent an important mechanism contributing to poorer outcomes in the heterogeneously composed other narcolepsy group, a hypothesis with direct implications for how clinicians manage patients whose diagnoses remain uncertain.

The broader literature on narcolepsy mortality remains stubbornly inconsistent, and the reply situates the debate within that larger landscape. A recent Taiwanese cohort study by Hsu and colleagues, published in JAMA Network Open, reported no excess all-cause or cause-specific mortality among narcolepsy patients after adjustment for comorbidities, standing in apparent contradiction to the VA findings of elevated mortality relative to a clinically complex sleep-clinic comparator. Reconciling these divergent results, the authors argue, will require harmonized phenotyping standards, cause-specific death ascertainment, and treatment-exposure modeling across diverse health systems, since a diagnosis of narcolepsy may carry different meanings in a national claims database, a single-payer registry, and an integrated veteran health system. The authors express hope that their work, together with the constructive critique from Fei and colleagues, will catalyze such multi-system efforts.

What makes this exchange more than an academic squabble is what it reveals about the epistemology of modern sleep medicine. As ever-larger electronic health record datasets become the primary engine of epidemiological discovery, researchers must navigate a fundamental tension between scale and precision: cohorts large enough to detect mortality signals over decades are rarely cohorts in which every diagnosis has been physiologically confirmed. The VA team’s reply models one way of handling that tension, defending algorithmic phenotyping while transparently cataloging its limits and inviting the very scrutiny it received. For patients with narcolepsy type 2 and related hypersomnolence disorders, the practical message is nuanced but important: the apparent mortality excess in non-type 1 narcolepsy may reflect diagnostic mixture and undertreatment rather than the disease itself, which means that clearer diagnostic pathways and more aggressive therapeutic engagement for diagnostically ambiguous patients could be among the most consequential interventions sleep medicine can offer.

Subject of Research: Mortality risk and diagnostic heterogeneity in narcolepsy type 2 and other hypersomnolence disorders using electronic health record phenotyping

Article Title: Reply to “Unexpectedly higher mortality in non‑type 1 narcolepsy: diagnostic heterogeneity or unmeasured confounders?”

Article References: Sharafkhaneh, A., Dauvilliers, Y., BaHammam, A. S., Azarian, M., Thorpy, M., Han, F., Senel, G. B., Aksu, M., & Razjouyan, J. (2026). Reply to “Unexpectedly higher mortality in non‑type 1 narcolepsy: diagnostic heterogeneity or unmeasured confounders?”. Journal of Clinical Sleep Medicine, 22(1), Article 124. https://doi.org/10.1007/s44470-026-00120-9

Image Credits: AI Generated

DOI: 10.1007/s44470-026-00120-9

Keywords: narcolepsy, narcolepsy type 1, narcolepsy type 2, mortality, electronic health records, multiple sleep latency test, sodium oxybate, obstructive sleep apnea, positive airway pressure, hypersomnolence, sleep medicine, Veterans Affairs cohort