Which biomarkers, such as inflammation markers, insulin, or cortisol, best explain how sleep affects cardiovascular risk?

Why inflammation markers keep showing up

Obstructive sleep apnea exposes the body to repeated episodes of low oxygen (intermittent hypoxia), fragmented sleep, and large swings in pressure inside the chest. These stresses trigger sympathetic activation, oxidative stress, and systemic inflammation, which in turn promote endothelial dysfunction, hypercoagulability, and metabolic dysregulation—all steps toward cardiovascular disease [1]. Inflammation is not just a bystander here; it is one of the main pathways through which disordered breathing is thought to damage blood vessels.

In a cross-sectional study of 172 patients with sleep apnea, the monocyte-to-HDL cholesterol ratio (MHR)—a marker that combines immune-cell activity with cholesterol handling—was the only index that rose significantly as sleep apnea severity increased [2]. Other inflammation-based ratios (neutrophil-to-lymphocyte, platelet-to-lymphocyte, systemic inflammatory index) did not show the same stepwise increase. That study also found that patients with cardiovascular disease were older, had higher body mass index, and were more likely to have hyperlipidemia and diabetes, making it hard to separate the effect of sleep apnea from these other risk factors [2].

Inflammation is only part of the story

A broader review of cardiovascular risk in sleep apnea describes a 'perfect storm' of hypoxic oxidative stress, autonomic dysregulation, endothelial damage, and inflammation occurring at the microvascular level [4]. This means inflammation markers capture only one thread of a tangled process. Endothelial dysfunction—damage to the lining of blood vessels—is another key mechanism, and researchers are exploring endothelial progenitor cell alterations as potential biomarkers for early diagnosis and treatment targets [5].

The same review notes that repetitive hypoxia and high carbon dioxide levels drive sympathetic stimulation, which then produces harmful hematologic effects including hypercoagulability and abnormal platelet aggregation [4]. These are not inflammation markers in the usual sense, but they are part of the same cascade. So when researchers ask which biomarker 'best' explains cardiovascular risk in sleep apnea, the honest answer is that the risk is distributed across several overlapping systems, and a single marker is unlikely to capture it all.

How much do biomarkers actually add to risk prediction?

A large systematic review of vascular inflammation biomarkers—covering 39 studies and nearly 176,000 people without known coronary heart disease—found that these markers do improve risk discrimination, but modestly. The added prognostic value, measured as a change in the c-index (a statistic where 0.5 is no better than chance and 1.0 is perfect prediction), was about 3 percentage points for major adverse cardiovascular events [3]. That is a real but limited improvement over standard clinical risk factors.

The same review found that imaging-based biomarkers of vascular inflammation, such as high-risk plaque features on coronary CT angiography and perivascular fat imaging, added more prognostic value than circulating blood markers—up to about 8 percentage points for perivascular adipose tissue [3]. This suggests that how inflammation is measured matters: a snapshot of circulating molecules may miss what is happening in the vessel wall itself. The review did not focus on sleep apnea specifically, so these numbers describe the general predictive power of inflammation biomarkers, not their performance in sleep apnea patients.

What this means for someone with sleep apnea

For now, the most practical takeaway is that sleep apnea severity alone—measured by the apnea–hypopnea index—does not reliably predict who will develop cardiovascular problems [1]. Patients with high apnea–hypopnea index, high oxygen desaturation index, and low sleep efficiency appear to be at higher risk and may warrant closer monitoring [2]. But the biomarkers that best capture that risk are still being validated, and no single marker has emerged as the clear winner.

Researchers are moving toward combining multiple indicators—physiological measures like hypoxic burden and heart rate response, circulating biomarkers of inflammation and coagulation, and phenotype-based classifications—into multimodal prediction models [1]. The hope is that such models will identify high-risk patients more precisely than any one marker could. Whether insulin or cortisol will prove to be key pieces of that puzzle remains an open question; the evidence so far points to inflammation and endothelial dysfunction as the more established links, with metabolic and stress-hormone markers as plausible but less directly tested contributors.

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