Devices capable of simultaneous NO–CO measurements exist, but manufacturers are few and platforms differ in sensor technology (chemiluminescence, electrochemical, laser), response time, cross-sensitivities (humidity and CO2), calibration routines, gas mixtures, and calculation algorithms. These differences shift absolute values and complicate portability of reference equations across devices. Standardizing gas preparation, sampling, and computational steps; specifying minimum analyzer performance (rise time, drift, interference limits); and building software that can be updated as reference equations and models evolve are essential. In the United States, the absence of FDA clearance for NO–CO systems and limited clinician awareness have further concentrated DLNO as research tools only. A practical way forward is multi-site method-comparison trials that include device brand as a covariate, shared quality-assurance protocols, and regulatory-ready analytical validation packages. Laboratories should explicitly report device brand, analyzer type, software version, and breath-hold time (BHT) in manuscripts and clinical reports to improve comparability and accelerate harmonization.

Diffusing capacity depends on alveolar volume, hemoglobin, and reaction-diffusion kinetics; it also varies across populations and environments. In Mexican Hispanics, habitual residence at ∼2240m increased DLNO, DLCO, and alveolar volume compared with lowlanders, and models that included altitude and population terms reduced error and misclassification relative to race-neutral models [5]. Complementary work argued specifically for race-specific DLNO equations to reduce false positives and false negatives in clinical practice [6]. These findings contradict the recent American Thoracic Society statement advocating race-neutral reference equations for pulmonary function test interpretation [7], a recommendation issued without supporting evidence or a transparent evidentiary process. Where device-and timing-specific equations for majority populations already exist [5,8], laboratories should report z-scores that incorporate altitude and ancestry when they measurably improve calibration, and consider parallel reporting of race-neutral values when available. Practically, this means listing the exact reference set and device used, stating the altitude (or barometric pressure) at testing, and indicating whether ancestry-specific or race-neutral z-scores were used to define the lower limit of normal, along with any shift in classification that results from the choice.

The field needs multi-center, multi-ancestry DLNO–DLCO datasets that explicitly encode device brand, altitude, and breath-hold time. A standing consortium could harmonize protocols, pool raw data, and generate globally applicable equations with clear device/timing annotations. Current resources include validated sets for Mexican Hispanic [5], Black adults [6], and White populations [8], but larger samples are required in Blacks [6] and broader ancestry representation are also required to minimize misclassification and facilitate international adoption. A minimal data standard should include triplicate DLNO–DLCO maneuvers with 5–6s BHT, accepted quality criteria, hemoglobin values, body size, age and sex, and reporting of spirometry and lung volumes to anchor interpretation. When feasible, testing sites should report diffusing capacity using two different breath-hold times (approximately 5 seconds and 10 seconds) to facilitate assessment of ventilation heterogeneity effects.

Beyond mechanism, adding DLNO to DLCO materially improves case detection in several contexts. An individual-participant data (IPD) meta-analysis in heart failure found that the double-diffusion approach enhanced classification compared with DLCO alone [9]. In post-COVID cohorts, another IPD meta-analyses demonstrated that summed DLNO+DLCO z-score outperformed DLCO alone for identifying prior disease, improving both Matthews correlation and net reclassification [10]. In a Further IPD meta-analysis, hierarchical partitioning showed unique contributions from FEV1z-scores (R2=0.35)>DLNO z-scores (R2=0.21)>TLC z-scores (R2=0.11), totalling McFadden's R2=0.66 [11]. DLCO z-scores did not add any further useful information to the model. Also, category-free reclassification and Youden-threshold analyses showed small but favourable gains; the case–control risk gap improved by up to ∼5% when adding DLNO to a DLCO-based model [11]. The practical implication is simple: whenever a DLCO test is clinically indicated and equipment is available, measure DLNO in the same maneuver and report both values – and their ratio – together. Reports should include brief, mechanism-aware comments: e.g., disproportionately low DLNO with relatively preserved DLCO suggests a membrane/surface-path process; disproportionately low DLCO relative to DLNO suggests a blood-volume/hematocrit limitation or mixed pathology when both are reduced.

Clinicians now encounter two ways to interpret diffusing capacity (Table 1). The classic Roughton–Forster (RF) framework treats total resistance as two serial terms – membrane (1/DM) and blood (1/(θVc)) – and has spurred back-calculations of DM and Vc from paired DLNO–DLCO [2]. A time-based diffusion–reaction framework instead derives uptake from first principles as the sum of an equivalent diffusion time (δeq) and a reaction time (τ) [12]. Because τNO is orders of magnitude smaller than δeq, DLNO behaves as surface absorption dominated by the membrane–plasma path and accessible RBC surface, whereas DLCO behaves as volume absorption that scales with hematocrit and engaged red blood cell (RBC) volume [12,13]. Importantly, exact solutions and 3-D models show that RBC peripheries are not equi-concentration surfaces, and diffusion times in series do not add, so extrapolating 1/DLCO versus PO2 to a unique “DMCO” is biased [14,15]. In reports, it is safer to present DLNO, DLCO, alveolar volume (VA), and the DLNO/DLCO ratio as observables and to flag any DM–Vc values as model-dependent. This approach preserves familiar language while aligning interpretation with physical constraints.

First, align technology: agree on minimum analyzer performance, require routine calibration checks for electrochemical NO sensors, publish cross-device transfer functions, and encourage shared biological controls. Second, lock in reference modeling: maintain device- and timing-specific equations, include altitude explicitly, and report ancestry-aware and race-neutral z-scores side-by-side where each adds value [5–8]. Third, refine interpretation: report DLNO and DLCO with their ratio and, when feasible, use two breath-hold times to probe heterogeneity [12–15]. Fourth, coordinated regulatory submissions backed by multi-center trials must be pursued so laboratories can adopt DLNO as readily as DLCO [10,11].

DLNO began as a technically elegant measurement and is now a clinically useful companion to DLCO. Its broader adoption hinges on a healthy device ecosystem, ancestry- and altitude-specific reference equations, clear interpretive language that acknowledges model dependence, and well-designed clinical validation. None of these barriers are insurmountable. With deliberate alignment across manufacturers, laboratories, and professional societies, DLNO can move from promising to routine within the next few years.

This author does not have any financial or personal relationships that could constitute a conflict of interest.