The scale of the issue is easy to underestimate. Recent global estimates of human population distribution suggest that a substantial share of humanity lives at moderate or high elevation, with large concentrations in the Andes, the Himalayan plateau and its surroundings, the highlands of East Africa, and the mountainous interior of Mexico and Central America. Cities such as Bogotá, Mexico City, Quito, La Paz, Kathmandu, and Denver all sit at altitudes where barometric pressure, and therefore the partial pressure of inspired oxygen, is significantly reduced compared with conditions at sea level. For the residents of these cities, sleep does not occur in the same physiological environment that most sleep medicine textbooks implicitly assume, and that mismatch has consequences for how breathing during sleep is measured, scored, and interpreted.

The core physics are straightforward but their downstream effects are not. As altitude increases, barometric pressure falls, and with it the partial pressure of oxygen in inspired air. The body responds with a suite of acclimatization mechanisms: ventilation rises, producing a mild respiratory alkalosis as carbon dioxide is washed out; oxygen saturation of hemoglobin drifts downward, particularly during sleep; and the chemoreflexes that govern breathing become more sensitive to changes in carbon dioxide and oxygen. During wakefulness these adjustments are usually well tolerated. During sleep, however, the normal withdrawal of the behavioral and cortical drive to breathe leaves chemical control in charge, and at altitude that chemical control system becomes unstable. The result is a characteristic pattern of periodic breathing, in which ventilation waxes and wanes in regular cycles, sometimes culminating in central apneas, pauses in breathing that originate not in blocked airways but in an over-responsive control loop.

This instability is often described in terms of loop gain, a control-systems measure of how strongly the respiratory system amplifies a small perturbation in blood gases. At altitude, the reduced inspired oxygen raises the gain of the peripheral chemoreflex, so a modest dip in oxygen saturation triggers a larger ventilatory response than it would at sea level. The resulting over-ventilation lowers carbon dioxide below the threshold needed to keep breathing going, breathing pauses follow, and the cycle repeats. Sleep itself amplifies the problem, because the carbon dioxide threshold for maintaining breathing rises during non-REM sleep, narrowing the window between apneic and hyperpneic thresholds. Classic laboratory work has shown that hypocapnic apneas and hypopneas develop readily during NREM sleep under hypoxic conditions, and more recent field studies have documented that these loop-gain effects differ between men and women, with sex-related differences in the propensity for high-altitude sleep-disordered breathing.

The clinical consequence is that a sleep study performed at 2,600 meters may look different from the same study performed at sea level, in the same person, on the same equipment. Oxygen saturation values that would be flagged as abnormal at low altitude may be unremarkable for a healthy resident of a high-altitude city, and thresholds used to score hypopneas or to calibrate oxygen therapy may need local adjustment. Studies of healthy adults sleeping at around 2,240 meters, for example, have documented breathing patterns and saturation levels that depart from low-altitude norms without indicating disease, while work in Bogotá at 2,640 meters has examined how apnea severity and oxygenation measures shift in adults living at that elevation. Conversely, patients with obstructive sleep apnea who live at altitude may show a distinct phenotype: research on high-altitude populations has described a predominance of REM-related obstructive events, a pattern with implications for how severity is graded and how treatment is titrated.

Altitude does not only add central events to the picture; it can also unmask or aggravate obstructive disease. The intermittent desaturation that accompanies upper airway narrowing is amplified when the baseline saturation is already lower, so the same degree of airway collapse produces deeper and longer oxygen dips at elevation. Some investigators have argued that mild chronic high-altitude exposure contributes to the comorbidity burden of obstructive sleep apnea-hypopnea syndrome, potentially worsening cardiovascular and metabolic risk. Comparative data from sleep clinics at different elevations in the mountain west of the United States have shown that altitude-associated central apnea can complicate both the diagnosis and the treatment of obstructive sleep apnea, because positive airway pressure therapy that works well at one elevation may behave differently at another, and residual central events may appear or disappear as patients move between altitudes.

That last point, the effect of moving between elevations, is where the newest evidence becomes particularly striking. A randomized crossover trial published in the same journal examined healthy residents of a moderate-altitude city who descended to low altitude for short periods, and found that even brief descent changed their sleep and breathing physiology. Meanwhile, an earlier study of patients with obstructive sleep apnea who traveled from moderate elevation to sea level documented changes in apnea severity with descent, and clinical experience in mountain regions has long noted that patients referred for sleep testing shortly after travel to a different altitude may yield results that do not reflect their usual condition. In practical terms, a sleep study is a snapshot not only of a patient but of the atmospheric conditions under which it was recorded, and the commentary argues that this context belongs in the interpretation.

What would it mean to take altitude seriously in sleep medicine? The commentary points toward several concrete adjustments. Reference values for oxygen saturation, desaturation indices, and normal breathing patterns during sleep should ideally be established or calibrated for the elevation at which testing occurs, rather than imported wholesale from low-altitude populations. Scoring conventions and severity thresholds might need altitude-specific interpretation, so that a given apnea-hypopnea index or saturation nadir is read in light of the local barometric environment. Clinicians should routinely record the altitude of the sleep laboratory or home testing device and consider it when reporting results, and should ask patients about recent travel between elevations before drawing conclusions from a single night. Researchers designing trials of sleep-disordered breathing, and reviewers comparing studies from different centers, face a parallel obligation: a multicenter study that pools data from Quito and Amsterdam without accounting for a 2,800-meter difference in testing conditions may be averaging away a real biological signal.

There is also a broader equity dimension that the commentary raises implicitly through its own vantage point. Sleep medicine guidelines, reference datasets, and device algorithms have historically been developed and validated largely at low altitude, in North American, European, and East Asian lowland populations. Yet a meaningful fraction of the world’s population lives at elevations where those assumptions are strained, and many of the countries most affected, including Colombia, Bolivia, Peru, Ecuador, Mexico, Nepal, and Ethiopia, have limited access to the specialized sleep centers where local normative data could be generated. Building altitude-aware sleep medicine is therefore not only a matter of physiological precision but of making diagnostics valid for the populations that actually use them. The author, who declares no competing interests and reports no specific funding for the commentary, frames the question as one that the field can no longer defer.

The takeaway for readers is deceptively simple: altitude matters, and it matters in both directions. For the lowlander who travels to the mountains, a few nights of periodic breathing and fragmented sleep are usually a normal acclimatization response, not a new disease, though they can complicate any testing done during the stay. For the high-altitude resident, the thinner air is home, and the sleep study that ignores it risks mislabeling normal physiology as pathology or, more dangerously, underestimating disease that is amplified by hypoxic stress. As home sleep apnea testing spreads to ever more diverse environments, the elevation printed on the report may deserve the same attention as the patient’s age, sex, and body mass index. The commentary’s message to the sleep medicine community is that the atmosphere is not background noise in a sleep study; it is part of the experiment.

Subject of Research: The influence of altitude on sleep-disordered breathing physiology and the interpretation of sleep studies

Article Title: Sleep studies: does the altitude matter?

Article References: Bazurto-Zapata, M. A. (2026). Sleep studies: does the altitude matter?. Journal of Clinical Sleep Medicine, 22(1), Article 142. https://doi.org/10.1007/s44470-026-00151-2

Image Credits: AI Generated

DOI: 10.1007/s44470-026-00151-2

Keywords: sleep medicine, altitude, hypoxia, polysomnography, obstructive sleep apnea, central apnea, periodic breathing, loop gain, oxygen saturation, high-altitude populations, respiratory physiology, REM sleep

The stakes of this seemingly academic quarrel are enormous. Obstructive sleep apnea affects roughly a billion people worldwide by some estimates, and its overlap with obesity is so pervasive that the two conditions are often treated as two faces of the same metabolic syndrome. Yet the causal architecture linking them remains surprisingly murky. If clinicians cannot quantify how much of a given patient’s apnea is adiposity-attributable, they cannot accurately predict how much that patient will benefit from weight loss, whether through lifestyle intervention, pharmacotherapy such as the new generation of incretin-based drugs, or bariatric surgery. That uncertainty ripples directly into treatment decisions, insurance coverage, and the design of clinical trials.

At the heart of the exchange is a concept that sleep researchers call endotypes, the distinct physiological mechanisms that produce the shared clinical picture of repetitive upper airway collapse during sleep. Schmickl, Malhotra and their colleagues had previously argued, in their review “Sleep and obesity: known interactions and open questions,” that obesity acts on sleep apnea through multiple parallel pathways: it narrows the pharyngeal airway through fat deposition around the neck and tongue, it reduces lung volume and thereby diminishes the tracheal tug that stiffens the upper airway, it destabilizes respiratory control by increasing loop gain, and it alters arousal thresholds and fluid shifts that further compromise airway patency during sleep. Weight loss, in this view, does not simply shrink a pipe; it reconfigures an entire physiological system.

The Maizura commentary, in turn, pressed the field to go further and define what fraction of apnea severity is genuinely attributable to adiposity rather than merely coexisting with it. The distinction matters because correlation between body mass index and apnea severity, while real, is notoriously imperfect. Many patients with severe obesity never develop sleep apnea, and many lean patients do. Craniofacial anatomy, upper airway muscle responsiveness, genetic predisposition, and age all modulate the relationship. The commentators argued that without a formal definition of adiposity-attributable disease, the field risks overestimating the benefits of weight-targeted therapies and underdiagnosing the apnea that persists after the pounds come off.

Schmickl and Malhotra’s reply, accepted by the journal in September 2026, engages this critique directly. While the published reply is brief, its positioning within the citation trail reveals its argumentative center of gravity: the authors point to recent work by Beatty and colleagues, published in Chest, that measured how weight loss reshapes the physiological endotypes of obstructive sleep apnea. That study provides some of the most direct evidence to date that reducing adiposity produces measurable, mechanistically specific changes in airway collapsibility, loop gain, and arousal threshold, rather than a diffuse or nonspecific improvement. In other words, the causal pathways that obesity exploits are identifiable, quantifiable, and reversible, which is precisely the kind of evidence needed to move from coexistence to causation.

The reply also draws on a foundational observation from Peppard, Ward and Morrell, who showed more than a decade and a half ago that obesity amplifies oxygen desaturation during sleep-disordered breathing independent of its effect on airway collapse itself. Fat tissue, particularly around the abdomen and chest wall, mechanically loads the respiratory system, reducing the oxygen reserves available when breathing pauses occur. Two patients with identical degrees of airway obstruction can therefore experience dramatically different falls in blood oxygen depending on their body habitus. This finding complicates any attempt to define adiposity-attributable apnea using the apnea-hypopnea index alone, because the index counts events but says nothing about their physiological consequences.

That limitation has driven a broader movement in the field toward measures such as the hypoxic burden, championed by Azarbarzin, Sands and colleagues in analyses of the Osteoporotic Fractures in Men Study and the Sleep Heart Health Study. Their work demonstrated that the total burden of nocturnal oxygen deprivation predicts cardiovascular mortality better than the conventional event index, suggesting that the downstream harm of sleep apnea flows substantially through intermittent hypoxia. If adiposity disproportionately worsens hypoxic burden, then the fraction of apnea-related cardiovascular risk that is adiposity-attributable may exceed the fraction of apnea events that weight loss eliminates. Schmickl and Malhotra’s reply implicitly invokes this distinction, arguing that any definition of adiposity-attributable disease must grapple with severity measures that capture physiological consequence, not just event frequency.

The exchange also touches on a practical question that clinicians confront daily: what happens to sleep apnea after weight loss? Bariatric surgery cohorts and pharmacological trials consistently show substantial reductions in apnea severity, but remission is far from universal, and severity often recurs even when weight loss is maintained. The Beatty endotype study helps explain why. Weight loss preferentially improves certain mechanisms, such as airway collapsibility and lung volume effects, while leaving others, such as inherently narrow craniofacial anatomy or high loop gain driven by ventilatory control instability, largely untouched. A patient whose apnea was mostly anatomical may remain apneic after losing significant weight, while a patient whose apnea was mostly weight-driven may remit completely. Defining adiposity-attributable apnea, in this light, is less a semantic exercise than a prerequisite for personalized therapy.

Neither side of the dispute disputes the fundamental biology. Obesity is the single strongest modifiable risk factor for obstructive sleep apnea, and the epidemic of adiposity has been a principal engine of the global rise in sleep-disordered breathing. The disagreement is about epistemics: how confidently the field can assign causal fractions to a multifactorial disease when the contributing mechanisms interact, overlap, and compensate for one another. Schmickl and Malhotra, whose review emphasized the bidirectional nature of the relationship, note that the arrow also points the other way. Sleep apnea fragments sleep, promotes daytime sleepiness that reduces physical activity, and dysregulates hormones such as leptin and ghrelin that govern appetite, thereby promoting weight gain in a self-reinforcing loop. Any causal accounting that treats obesity as the sole upstream actor risks missing this feedback structure entirely.

The reply, published as volume 22, article 174 of the Journal of Clinical Sleep Medicine, is unlikely to settle the debate on its own, but it clarifies what a resolution would require: longitudinal studies that measure endotype-specific responses to defined amounts of weight loss, severity metrics that capture hypoxic and cardiovascular consequence, and analytical frameworks that can partition variance across interacting mechanisms. As incretin-based weight-loss therapies reshape the treatment landscape and millions of patients begin losing weight while still wearing their CPAP machines, the question of how much apnea the fat was actually causing will move from the pages of academic journals into the examination room. The Schmickl-Malhotra reply signals that the field’s leading sleep physiologists intend to answer that question with mechanistic precision rather than assumption, and the exchange it concludes may well be remembered as an early milestone in the effort to make sleep medicine genuinely causal in its reasoning.

Subject of Research: The causal relationship between adiposity and obstructive sleep apnea and its physiological endotypes

Article Title: Reply to “From coexistence to causation: defining adiposity-attributable obstructive sleep apnea”

Article References: Reply to “From coexistence to causation: defining adiposity-attributable obstructive sleep apnea”. (n.d.). https://doi.org/10.1007/s44470-026-00197-2

Image Credits: AI Generated

DOI: 10.1007/s44470-026-00197-2

Keywords: obstructive sleep apnea, obesity, adiposity, endotypes, weight loss, hypoxic burden, loop gain, airway collapsibility, bariatric surgery, sleep medicine, CPAP, causation

The technical problem at the heart of the debate concerns the hypopnea, defined as a partial reduction in airflow during sleep, in contrast to a complete apnea. Standard polysomnography records airflow through nasal pressure cannulas and thermal sensors, alongside respiratory effort belts and oximetry that tracks blood oxygen. Obstructive events are those in which effort continues while the airway collapses; central events are those in which the brain simply stops issuing the neural commands to breathe. Hypopneas, because they are partial, sit awkwardly in this binary scheme. A shallow breath can arise from a partly closed airway, from a weak central drive, or from a combination of the two, and the signal differences on a routine recording can be subtle enough that scorers disagree.

This ambiguity matters because the two categories point to different physiology and, potentially, different treatments. Obstructive events respond to continuous positive airway pressure, oral appliances, and airway-focused interventions. Central events, which reflect instability in the feedback loop that controls ventilation, may respond poorly or even paradoxically to standard pressure therapy, and are managed with different tools entirely, from adaptive servo-ventilation to medications that adjust chemosensitivity. If a hypopnea that is truly central in origin is counted as obstructive, a patient may receive a diagnosis whose treatment pathway is mismatched with the underlying mechanism.

The letter builds on a recent position paper in the same journal in which Ahn, Azarbarzin, Badr, Berry, and colleagues argued that classifying central hypopneas is important enough to warrant coordinated action by the sleep medicine community. Kumari’s contribution extends that argument by asking a more fundamental question: what is the point of identifying an event if knowing its type does not change what happens to the patient next? Drawing on a framework for evaluating diagnostic tests developed by di Ruffano, Hyde, McCaffery, Bossuyt, and Deeks in the British Medical Journal, she frames central hypopnea classification not as a scoring exercise but as a test whose value must be demonstrated in a chain that runs from detection, to differential diagnosis, to treatment selection, to measurable improvement in health.

That framework, originally developed to help researchers design trials of diagnostic technologies, imposes a discipline that sleep scoring has largely escaped. A diagnostic test earns its place in clinical practice by showing that its results lead to better decisions and better outcomes, not merely that it produces numbers. Applied to hypopnea classification, the question becomes whether knowing that a given hypopnea is central rather than obstructive actually alters management in ways that benefit the patient. The letter suggests that the field cannot answer this question with confidence, because the necessary evidence linking event phenotype to treatment response has never been systematically assembled.

The second pillar of the argument comes from a consensus statement led by Malhotra, Ayappa, Ayas, Collop, Kirsch, and McArdle, published in the journal Sleep, on metrics of sleep apnea severity beyond the apnea-hypopnea index. That statement catalogued the shortcomings of the AHI, the single number that has dominated sleep medicine since its inception. The AHI counts all apneas and hypopneas per hour of sleep regardless of type, position, or physiological consequence. Two patients with identical AHI values can carry very different burdens of disease: one may have long, severely desaturating events concentrated in REM sleep, while the other has short, benign events scattered across the night. Collapsing this heterogeneity into one figure discards exactly the information that might guide treatment.

Central hypopneas sit at the sharp edge of this metric problem. Because scoring rules allow hypopneas to be identified through airflow reduction with or without associated desaturation or arousal, depending on the rule set in use, the same recording can yield different AHI values under different guidelines. When central events are lumped together with obstructive ones, the resulting index reflects neither the mechanical burden of airway collapse nor the control-system instability of central apnea. For conditions in which central events predominate, such as heart failure-associated central sleep apnea or opioid-induced respiratory depression, an AHI that blends event types may actively mislead the clinician about both severity and prognosis.

The physiological stakes are considerable. Central respiratory events arise from instability in the loop gain of the ventilatory control system, the sensitivity with which the brain responds to fluctuations in carbon dioxide and oxygen. High loop gain produces overshoot and undershoot in ventilation, creating cyclical patterns such as Cheyne-Stokes breathing. This instability is not a mechanical problem that a splinted airway can fix; it is a control problem with its own pharmacology and its own device solutions. Identifying central hypopneas is therefore a step toward measuring loop gain and control instability in ordinary clinical recordings, which could eventually allow clinicians to select patients for servo-ventilation or other control-targeted therapies on a rational basis rather than by trial and error.

The letter also highlights the human cost of the status quo. Patients whose symptomatic breathing disturbances are scored as mild or equivocal may be denied insurance coverage for therapy, told their sleep study was normal, or left to cycle through ineffective treatments. Conversely, patients whose events are counted but whose event type is misclassified may undergo positive airway pressure trials that fail, reinforcing a cycle of non-adherence and clinical frustration. Kumari argues that rigorous, clinically validated classification of central hypopneas would sharpen the diagnostic pathway at both ends, directing the right patients to the right interventions and sparing others inappropriate treatment.

The path forward, as the letter sketches it, follows the logic of the diagnostic-test framework: define the clinical decision the classification is meant to inform, gather evidence that the distinction changes that decision, and then test whether patients whose management is guided by event type fare better than those managed on AHI alone. That program requires agreement on scoring criteria for central hypopneas, prospectively collected data linking event phenotype to treatment response, and trial designs that treat classification as an intervention in its own right. None of this is easy, and the letter does not pretend otherwise. Its central claim is simpler and harder to dismiss: a measurement that has never been shown to change a clinical decision is a measurement awaiting justification, and for central hypopneas the justification has not yet been built. As sleep medicine moves toward richer phenotyping of sleep-disordered breathing, closing the gap between event identification and clinical utility has become the test that the field’s most familiar number must finally pass.

Subject of Research: Clinical classification of central hypopneas in sleep-disordered breathing diagnosis and the validation of diagnostic utility

Article Title: Central hypopnea classification: bridging the gap between event identification and clinical utility

Article References: Kumari, N. (2026). Central hypopnea classification: bridging the gap between event identification and clinical utility. Journal of Clinical Sleep Medicine, 22(1), Article 172. https://doi.org/10.1007/s44470-026-00202-8

Image Credits: AI Generated

DOI: 10.1007/s44470-026-00202-8

Keywords: central hypopnea, sleep apnea, polysomnography, apnea-hypopnea index, sleep medicine, respiratory events, diagnostic testing, loop gain, positive airway pressure, sleep-disordered breathing, Journal of Clinical Sleep Medicine, Central

The distinction is not academic hair-splitting; it reflects two fundamentally different failures of the respiratory system. In obstructive sleep apnea, the sleeper’s airway physically collapses even as the brain keeps driving the breathing muscles—the effort continues, but airflow falters against a blocked conduit. In central sleep apnea, the problem lies upstream: neural output from the brainstem’s respiratory centers wanes, and airflow falls because the drive to breathe itself has been withdrawn. Central events arise from instability in the ventilatory control system, often quantified by an engineering concept known as loop gain. A system with high loop gain overreacts to fluctuations in carbon dioxide, overshooting corrections and oscillating into periodic breathing, whereas low loop gain indicates a stable control system resistant to such instability. Both obstructive and central apnea patients can exhibit elevated loop gain, and the degree of abnormality may predict how a given patient responds to different treatments.

While scoring rules for frank apneas—complete pauses in breathing—are well agreed upon, the AASM scoring manual treats classification of hypopneas as optional, and most sleep centers simply do not perform it. The panel argues this default has real consequences. In a large French cohort of more than 2,000 people with sleep apnea, the prevalence of central sleep apnea jumped from 5 percent to nearly 20 percent once hypopneas, in addition to apneas, were manually classified. That fourfold increase suggests a substantial population of patients whose underlying central ventilatory instability has been invisible to their diagnoses because their shallow breaths were assumed to be obstructive.

How, then, can a scorer tell the two event types apart on a routine polysomnogram? The current AASM criteria list snoring, flattening of the nasal pressure waveform, and thoracoabdominal desynchrony—out-of-phase motion of the chest and abdominal belts—as hallmarks of obstruction; when none of these features is present, the event should be scored as central. Winfried Randerath and colleagues developed a more elaborate algorithm, validated against esophageal manometry, the gold standard for measuring respiratory effort. Their stepwise approach adds the shape of the recovery breaths that follow an event, the position of the arousal relative to the recovery breaths, and the sleep stage in which the event occurs. Against esophageal pressure measurements, the algorithm correctly identified 60.5 percent of obstructive hypopneas and 76.9 percent of central hypopneas, for an overall accuracy of 68 percent. The two most informative signals in both frameworks are inspiratory flow flattening and respiratory paradox, with published sensitivities and specificities of 0.65 and 0.67 for flow flattening and 0.81 and 0.46 for paradox, underscoring that no single channel suffices and careful integration of multiple signals is essential.

Further physiological markers have been proposed by Javaheri and colleagues. Synchronous changes in airflow and effort, snoring during the recovery breaths rather than during the event itself, and a symmetric desaturation-resaturation pattern on the oximetry trace point toward central disease, whereas snoring during the event, arousals at event termination, and progressive prolongation of the inspiratory duty cycle—the fraction of the breath devoted to inspiration—characterize obstruction. That last feature has been confirmed experimentally in studies using inspiratory resistive loading, which mimics airway obstruction and reliably lengthens inspiratory time. A head-to-head comparison by Dupuy-McCauley and colleagues found the AASM and Randerath methods performed similarly, at 67 to 69 percent accuracy, but with only fair interrater reliability, a kappa of 0.30—meaning two trained scorers often disagree. Signal quality, particularly the notoriously variable nasal pressure signal, is a likely culprit.

To move the field forward, the panel proposes an operational definition: a central hypopnea shows no snoring during the event, waxing-and-waning airflow during recovery breaths, waxing-and-waning effort in the belts, and an arousal occurring in the middle of the recovery breaths rather than at their end. Just as importantly, it proposes a new category, the unclassified or indeterminate hypopnea, for events that meet no strict criteria, rather than forcing them into the obstructive bucket. Under this framework, a sleep report would include the overall apnea-hypopnea index along with separate obstructive, central, and unclassified indices that sum to the total. The panel stresses this is a provisional construct, not a guideline, and requires prospective validation.

Why does this matter now? Because sleep medicine is entering an era of mechanistically targeted therapies, and each therapy aims at a different piece of the physiology. Transvenous phrenic nerve stimulation, which paces the diaphragm to stabilize breathing, works on central ventilatory instability; hypoglossal nerve stimulation, which protrudes the tongue to hold the airway open, works on obstruction. In the pivotal trial of phrenic nerve stimulation, the likelihood of achieving at least a 50 percent reduction in the apnea-hypopnea index rose steadily with the baseline proportion of central events—from 37.5 percent when fewer than half of events were central to 76.5 percent when 90 percent or more were central. When hypopneas were classified retrospectively in this cohort, most residual events after implantation were found to be obstructive, and intriguingly, the proportion of central apneas did not correlate with the proportion of central hypopneas, meaning the two event types must be characterized independently rather than assumed to travel together. The same logic applies in reverse for hypoglossal nerve stimulation: a patient with a heavy load of unrecognized central hypopneas is a candidate for a disappointing result.

The stakes extend to routine positive airway pressure care. The AASM’s recent clinical practice guideline recommends adaptive servo-ventilation for central sleep apnea in patients with normal heart function when CPAP fails; if central events have been correctly enumerated from the outset, prolonged, fruitless trials of CPAP could potentially be avoided. The panel also draws attention to treatment-emergent central sleep apnea, which arises in roughly 4 to 19 percent of obstructive sleep apnea patients during CPAP initiation and persists in about one-third of those affected after months of therapy. Distinguishing pre-existing, treatment-resistant central disease from events that genuinely emerge after CPAP initiation matters for prognosis and management: emergent events are often transient and warrant re-evaluation after a few weeks, whereas persistent disease may respond to adaptive servo-ventilation, supplemental oxygen, or the carbonic anhydrase inhibitor acetazolamide. Longitudinal vigilance is essential, since central apnea can develop years into CPAP therapy with the onset of atrial fibrillation or left ventricular dysfunction, or with the use of opioids, baclofen, or ticagrelor—exposures that should prompt a fresh look at the pattern of breathing disturbances.

The panel is candid about the obstacles. Manual classification adds time and training demands for sleep technologists, and visual assessment suffers from inconsistent interrater reliability across centers. Overclassifying central hypopneas risks inappropriate therapy escalation and added cost; underclassifying them risks residual symptoms and missed combination therapy. The solution, many panelists believe, lies in automation. Several developmental pathways were outlined: event-level analysis extracting features from airflow, effort belts, diaphragmatic EMG, snoring, EEG, and heart rate signals; subject-level metrics such as central and obstructive indices that exploit the characteristic low cycle-length variability and sinusoidal airflow of central apnea, using tools like wavelet decomposition, cardiopulmonary coupling, and machine learning; multimodal models incorporating medications, comorbidities, and demographics; counterfactual prediction to resolve ambiguous events; and approaches that directly model therapeutic response. All of these depend on a prerequisite the panel emphasizes: harmonized, minimum signal-quality standards across laboratories, because algorithms trained on noisy, inconsistent signals will not generalize. With validation studies and human oversight, the panel concludes, routine hypopnea classification could become the foundation of a genuinely precision-based approach to sleep-disordered breathing, matching each patient’s endophenotype to the therapy most likely to quiet their nights and restore their days.

Subject of Research: Classification of obstructive versus central hypopneas in sleep-disordered breathing diagnosis and therapy selection

Article Title: The importance of classifying central hypopneas: a call to action

Article References: Ahn, A. P., Azarbarzin, A., Badr, M. S., Berry, R., DeYoung, P., Dupuy-McCauley, K., Morgenthaler, T. I., Pépin, J. L., Randerath, W., Sands, S., Tallavajhula, S., & Malhotra, A. (2026). The importance of classifying central hypopneas: a call to action. Journal of Clinical Sleep Medicine, 22(1), Article 149. https://doi.org/10.1007/s44470-026-00177-6

Image Credits: AI Generated

DOI: 10.1007/s44470-026-00177-6

Keywords: central sleep apnea, obstructive sleep apnea, hypopnea classification, polysomnography, loop gain, phrenic nerve stimulation, hypoglossal nerve stimulation, adaptive servo-ventilation, CPAP, treatment-emergent central sleep apnea, machine learning, sleep medicine

The patient himself was an unlikely candidate for a cardiac scare. He had been a consistent, well-controlled CPAP user for thirteen years, treated for obstructive sleep apnea with a fixed pressure of 7.6 millibars delivered through a nasal mask. He was not obese, his weight was stable, and his only medication was aspirin. A recent medical evaluation had already given him a clean bill of health where it counted: a normal neurological assessment, a normal brain MRI, a normal cardiac echocardiogram and a normal ergospirometry test. So when he noticed residual respiratory events accumulating on his device across several consecutive mornings, and the downloaded SD-card data from his ResMed AirSense10 revealed a total apnea-hypopnea index of 8.7 events per hour with central apneas at 8.5 per hour and Cheyne–Stokes respiration flagged at 11 percent of the record, the finding landed as a genuine surprise. The machine’s leak data, at a median of 1.2 liters per minute and a 95th percentile of 8 liters per minute, ruled out the most common technical culprit: a leaky mask corrupting the flow signal.

With device-detected Cheyne–Stokes respiration on the table, the team followed the logical sequence laid out in sleep medicine practice. The gold standard for confirming the pattern is fully attended polysomnography under American Academy of Sleep Medicine criteria, because it captures brain activity and precise sleep staging. Respiratory polygraphy, which records airflow, respiratory effort and oxygen saturation without EEG, is accepted as a reasonable first screening step when a CPAP device raises the alarm, ideally with a microphone attached to help distinguish obstructive from central events. The physicians withdrew CPAP for five days and ran a 659-minute polygraphy recording. The result reframed everything. The study showed a clear obstructive sleep apnea pattern, with an obstructive apnea-hypopnea index of 21.8 per hour within a total index of 27.8 per hour, an oxygen desaturation index of 29.3 per hour, and prominent snoring. There were no Cheyne–Stokes episodes. The central apnea-hypopnea index of 6.5 per hour was driven largely by mixed apneas carrying an initial central component, and the overall tracings produced airflow patterns that could genuinely mimic the periodic breathing signature of Cheyne–Stokes respiration.

Why would a CPAP algorithm be so easily misled? The answer lies in how these machines measure breathing. The AirSense10 does not watch the chest or abdomen; it infers everything from airflow, using a forced oscillation technique in which small pressure pulses are injected into the circuit. When the measured flow falls, the device checks whether the oscillations dissipate. If they fade away, the upper airway is presumed open, and the event is classified as central. If the oscillations persist, the airway is presumed closed and the event is obstructive. To label Cheyne–Stokes respiration, the software additionally searches for a characteristic flow pattern: a specific crescendo-decrescendo cycle length, defined episode durations, and the presence of central events. The criteria and algorithms involved have never been standardized across manufacturers, and each company’s thresholds remain proprietary. The Belgian team points to earlier work from the AlertApnée study, in which researchers documented that apparent onset of Cheyne–Stokes respiration during CPAP telemonitoring was attributable to obstructive events occurring just before the flagged episodes, and that the AirSense10 software recurrently misclassified typical obstructive apneas as Cheyne–Stokes respiration.

Layered on top of the algorithmic blind spot is a physiological phenomenon that makes the mimicry genuinely convincing: ventilatory control instability. Obstructive sleep apnea emerges from an interplay of pathophysiological mechanisms, one of which is loop gain, a measure of how sensitive the breathing control system is to disturbance. Loop gain has three components, describing the control, the exchange and the connection elements of the ventilatory system. When loop gain exceeds one, the system becomes hypersensitive, overcorrecting every perturbation with an excessive ventilatory response that can manifest as periodic breathing or full-blown Cheyne–Stokes respiration. When loop gain stays below one, breathing remains stable. A high loop gain during non-rapid eye movement sleep in a patient with obstructive sleep apnea can therefore generate airflow patterns that look, to an algorithm reading only the flow channel, exactly like the real thing. The researchers hypothesized that this was precisely what happened in their patient: his recent nasal obstruction had destabilized his ventilatory control, and his CPAP device’s pattern-matching software read the resulting pseudo-periodic airflow as central disease.

The treatment course told the story in real time. First, the clinicians raised the fixed CPAP pressure progressively from 7.6 to 11 millibars and switched the patient from a nasal mask to a naso-buccal mask, deliberately bypassing the obstructed nasal passages. The change partially worked. The software stopped flagging Cheyne–Stokes respiration entirely, but residual events persisted, with the flow-based apnea-hypopnea index stuck at 11 per hour. Then came the decisive intervention, and it was almost mundane: nasal corticosteroids for the obstruction itself. One month later, with the CPAP settings completely unchanged, the flow-based index dropped below 5 per hour, the conventional threshold for well-controlled sleep apnea. The breathing instability had not been a new cardiac or neurological disease; it was a mechanical problem in the nose propagating upward into the control of breathing, and once the nose was treated, the entire pseudocentral picture dissolved.

The mechanism the authors propose is a chain of escalating instability. Nasal obstruction increases inspiratory effort, because the patient must pull harder against resistance to move the same volume of air. Greater effort makes arousals from sleep more frequent, and each arousal triggers a burst of hyperventilation. In a system primed with high loop gain, that post-arousal ventilation overshoots, blowing off carbon dioxide below the threshold needed to sustain breathing drive and setting up the next pause in an oscillating loop. Crucially, the authors emphasize, this is not a phenotypic conversion of obstructive sleep apnea into central sleep apnea. The patient’s underlying disease did not change character. Instead, the nasal blockage caused a secondary destabilization of ventilatory control, generating mixed apneas and unstable, pseudo-periodic airflow that the device’s flow-only analysis could not reliably untangle from true Cheyne–Stokes respiration.

The broader implication is a cautionary tale for the era of connected medical devices. CPAP telemonitoring has become a powerful surveillance tool, capable of flagging emergent central apnea and, by extension, serious cardiac disease years before symptoms demand attention. But that power depends on algorithms that, in this case at least, could not see respiratory effort and therefore could not reliably distinguish obstructive or unstable breathing from central periodicity. The authors argue that device-based algorithms relying solely on airflow are known to misclassify obstructive or unstable breathing as Cheyne–Stokes respiration in the absence of effort signals, and they call for two concrete improvements: better detection accuracy and standardized, manufacturer-independent criteria for what counts as device-detected Cheyne–Stokes respiration. Until then, the practical message for clinicians is that a CPAP report announcing new-onset central apnea in a stable, long-treated patient should trigger confirmatory testing rather than reflexive alarm, and that an apparently mundane complaint such as a stuffy nose deserves a place on the differential diagnosis list.

The case, documented with device downloads, polygraphy tracings and a straightforward pharmacological resolution, adds a human-scale illustration to a problem that will only grow as home-based sleep data multiplies. Millions of nightly records flow from CPAP machines into cloud dashboards, screened by software and reviewed by clinicians who may never hear the patient’s own observation that something changed, in this instance the recent onset of nasal obstruction, before the numbers did. The Belgian team’s conclusion is measured but pointed: physicians should be aware of the limits of CPAP data collection reliability. A blocked nose, it turns out, can speak fluent Cheyne–Stokes to an algorithm that listens only to airflow, and only a careful look at the whole patient can tell the difference between a cardiac warning and a treatable case of congestion.

Subject of Research: False detection of Cheyne–Stokes respiration by CPAP devices due to nasal obstruction-induced ventilatory instability

Article Title: False Detection of Cheyne–Stokes Respiration on Continuous Positive Airway Pressure Resolved After Treatment of Nasal Obstruction

Article References: Castermans, E., Impens, D., Libert, W., & Bruyneel, M. (2026). False Detection of Cheyne–Stokes Respiration on Continuous Positive Airway Pressure Resolved After Treatment of Nasal Obstruction. Respirology Case Reports, 14(9), Article e70745. https://doi.org/10.1002/rcr2.70745

Image Credits: AI Generated

DOI: 10.1002/rcr2.70745

Keywords: sleep apnea, CPAP, Cheyne–Stokes respiration, nasal obstruction, ventilatory instability, loop gain, respiratory polygraphy, polysomnography, central sleep apnea, telemonitoring, case report, False

The study, led by Azarbarzin and colleagues and published in the Journal of Clinical Sleep Medicine, drew on 7,530 participants from two well-established cohort studies, the Sleep Heart Health Study and the Osteoporotic Fractures in Men Study, known as MrOS. Over nearly nine years of follow-up, 2,258 major adverse cardiovascular events occurred in this population. The investigators computed an oxygen overshoot burden for each participant: the cumulative area under the oxygen saturation curve that lies above an individual’s stable-sleep baseline, normalized for total sleep time. This is a deliberately individualized measure. Rather than comparing every patient against a fixed cutoff, the analysis anchors the signal to each person’s own resting saturation during uneventful sleep, so that even small relative rises above the personal baseline accumulate into a meaningful summary of nightly physiological stress.

The headline result is striking. Among participants with central sleep apnea and high overshoot burden, 54.6 percent experienced a major adverse cardiovascular event during follow-up, compared with 25.6 percent of control participants, corresponding to an adjusted hazard ratio of 1.45. Crucially, central sleep apnea with low overshoot burden carried no elevated risk relative to controls, and no corresponding association appeared in obstructive sleep apnea. The relationship survived statistical adjustment for the apnea-hypopnea index, the central apnea index, hypoxic burden, and, in the MrOS cohort, an estimate of loop gain, the control-system parameter that describes how vigorously breathing responds to disturbances in blood gases. That specificity is intriguing: it suggests the overshoot signal is not merely a proxy for how often someone stops breathing, but may capture something distinct about the instability of their ventilatory control.

The authors and commentators have proposed an intuitive biological explanation rooted in oxidative stress. The cycle of desaturation and reoxygenation that characterizes sleep apnea resembles ischemia-reperfusion injury, and repeated swings are thought to generate reactive oxygen species, inflammation, and vascular damage. Hypoxic burden, which integrates the depth and duration of event-related desaturation, already predicts cardiovascular outcomes better than a simple count of respiratory events, lending weight to the idea that the shape of the oxygen trace carries mechanistic information. Extending that logic above the baseline line is tempting: if falling oxygen is harmful, perhaps overshooting oxygen is harmful too, and the cumulative area of overshoot might quantify a dose of oxidative injury delivered night after night.

Yet a careful reading of the physiology counsels caution before accepting that interpretation. Oxygen overshoot as measured here is a relative oximetry signal, not demonstrated hyperoxia. A rise in peripheral oxygen saturation above a person’s stable-sleep baseline does not establish an elevated arterial partial pressure of oxygen, nor does it demonstrate increased tissue oxygen exposure. The study provides no mechanistic pathway, no oxidative biomarker measurements, and no bench or animal evidence showing that this specific above-baseline signal causes oxidative injury. Evidence from the intermittent hypoxia and reoxygenation literature cannot simply be transferred to this different signal. Indeed, previous work in obstructive sleep apnea found that greater post-event saturation overshoot was associated with lower nocturnal glucose, a pattern that argues against harm and even hints at benefit. Treating the oxygen rise itself as the causal exposure is, for now, premature.

A more plausible reading is that the oximetric overshoot is the visible tail of a much larger ventilatory and autonomic response. When an apnea terminates, the accumulated carbon dioxide and chemoreflex drive produce a vigorous recovery breath; the resulting hypocapnia is a direct signature of unstable ventilatory control, which is precisely the physiology that generates central sleep apnea and Cheyne-Stokes respiration in the first place. Human studies of apnea have documented marked sympathetic nerve activation and blood pressure surges around the termination of each event, followed by vagal modulation tied to lung inflation during the recovery phase. These autonomic oscillations, repeated hundreds of times a night, offer a credible route to myocardial infarction, arrhythmia, and stroke that does not require a modest rise in saturation to be intrinsically toxic. On this view, overshoot burden is a marker of the force of each recovery, not a poison in its own right.

This interpretation also exposes an important analytical gap in the new study. Adjusting for event frequency and hypoxic burden does not establish that overshoot is independent of the severity of each respiratory event. A longer or more severe apnea accumulates more hypercapnia, more chemoreflex stimulation, more arousal-related and sympathetic activation, and a longer loss of the vagal restraint normally provided by lung inflation. Those stimuli can then generate a larger recovery breath and a larger oxygen overshoot, and desaturation depth alone does not fully represent them. Consistent with this, work in obstructive sleep apnea has shown that ventilatory burden, a measure of the effort expended against collapsed airways, predicts cardiovascular outcomes and explains much of the variation in hypoxic burden. Oxygen overshoot may play an analogous role in central sleep apnea, summarizing a hidden physiological load that conventional indices leave unmeasured, without being the injurious agent itself.

Residual cardiac confounding remains another live possibility. In older community cohorts, central sleep apnea and Cheyne-Stokes respiration often reflect underlying cardiac dysfunction, elevated left-sided filling pressures, and prolonged circulation time, rather than mediating the cardiovascular consequences of those conditions. Heart failure identified through self-report and clinical records may miss subclinical disease, so some of the apparent association between overshoot burden and events could reflect undiagnosed cardiac impairment that both destabilizes breathing control and drives outcomes. Adjustment for loop gain in one cohort helps address the ventilatory-instability pathway, but detailed cardiac phenotyping, with objective measures of structure and function, is still needed before the direction of the arrow can be declared with confidence.

Two further cautions temper clinical translation. The central sleep apnea subgroup comprised only 303 participants, so confidence intervals around the headline event proportions deserve as much attention as the proportions themselves, and the findings require replication in larger and more diverse samples. The demonstration that a subject-specific baseline outperformed a fixed threshold of saturation at or above 96 percent confirms that the result depends on small relative differences in the oximetry signal, which places a premium on signal quality and measurement precision. The cohorts were 88 percent White, and known differential pulse-oximeter error by skin pigmentation is relevant to any saturation-based metric, although its effect on an above-baseline area calculation is currently unknown and warrants direct study.

The path forward is clear enough. The next study should measure event duration, airflow, respiratory effort, carbon dioxide, arousals, and event-level autonomic responses alongside oximetry, with detailed cardiac phenotyping and representative recruitment, and then test whether oxygen overshoot adds prognostic information once those features are accounted for. Until that work is done, oxygen overshoot burden should be regarded as a promising risk marker, a way of reading the recovery half of the apnea cycle that standard metrics ignore, rather than a demonstrated oxidative mechanism or a validated treatment target. Even so, the study is a reminder that the familiar oximetry trace still holds unexploited information, and that the line above the dips may matter as much as the dips themselves.

Subject of Research: Oxygen overshoot burden measured on overnight oximetry traces in central sleep apnea and its association with major adverse cardiovascular events.

Article Title: Above the line: oxygen overshoot in the oximetry trace

Article References: Manuel, A. R. G. (2026). Above the line: oxygen overshoot in the oximetry trace. Journal of Clinical Sleep Medicine, 22(1), Article 169. https://doi.org/10.1007/s44470-026-00183-8

Image Credits: AI Generated

DOI: 10.1007/s44470-026-00183-8

Keywords: central sleep apnea, oxygen overshoot, pulse oximetry, Cheyne-Stokes respiration, cardiovascular risk, hypoxic burden, ventilatory control, autonomic surges, oxidative stress, Sleep Heart Health Study, loop gain, sleep apnea