Abstract
Obstructive sleep apnea (OSA) is a highly prevalent sleep-related breathing disorder that drives substantial
cardiovascular, metabolic, and cerebrovascular morbidity through recurrent cycles of intermittent hypoxia, sleep
fragmentation, oxidative stress, and systemic inflammation. Globally, OSA affects close to one billion adults, with roughly 425
million living with moderate-to-severe disease; prevalence estimates in India range from 3.7% to 21%, with about 5% of adults
affected at a moderate-to-severe level. Despite this burden, the apnea-hypopnea index (AHI) — the mechanical, frequency-based
metric that anchors current diagnostic and severity classification — correlates poorly with individual comorbidity risk and
symptom burden, as it disregards event duration, desaturation depth, sleep-stage distribution, and cumulative hypoxic load.
Major sleep and cardiovascular society guidelines have not yet incorporated inflammatory or REM-specific biomarkers into
diagnostic or prognostic frameworks, even though expert consensus repeatedly flags this as an unmet need. This review
synthesizes the rationale for and evidence behind a set of low-cost, routinely available blood-derived biomarkers — serum uric
acid, fibrinogen, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and the systemic inflammatory
response index (SIRI) — as candidate correlates of polysomnographic severity in OSA, with particular attention to REM-specific
indices and to patients with OSA-associated comorbidities such as hypertension, diabetes, and chronic obstructive pulmonary
disease. We map the evidentiary, conceptual, methodological, measurement, population, temporal, and translational gaps that
currently limit clinical adoption of these markers, particularly in resource-constrained and under-represented settings such as
India. Addressing these gaps through adequately powered, stage-specific, and longitudinal studies could establish affordable
biomarker panels as scalable adjuncts to AHI for risk stratification and could inform decision-support pathways for respiratory
therapists and clinicians managing OSA in low- and middle-income countries.