Stroke is the third most common cause of death worldwide, and it is the leading cause of death in China, with the country accounting for approximately one-third of all stroke-related deaths (Wang et al., 2022). Ischemic stroke, which make up 60–80% of these cases, is projected to double every 10 years due to the aging of population, which will pose a significant health threat for patients (Benjamin et al., 2018; Wang et al., 2017). As the ongoing improvements in treatment level and nursing technology, the survival rate of stroke has been gradually increased. By 2050, the number of stroke survivors will reach 200 million worldwide (Brainin et al., 2020). Most elderly survivors endure various complications, including sleep disorder, post-stroke depression (PSD), and disability, which results in the impairments of health-related quality of life (Khot and Morgenstern, 2019; Yousufuddin and Young, 2019; Li et al., 2019).

Poor sleep is one of the major prevalent and burdening symptoms and can have debilitating effect on physical and/or mental function and health (Davis et al., 2019). Elderly patients of acute ischemic stroke often face multiple sleep issues, including poor sleep quality, difficulty initiating asleep, nocturnal awakenings, and excessive daytime sleeping (Fan et al., 2022). A meta-analysis found that 49–66% of stroke patients suffered from poor sleep quality, and more than half were older than 60 years old (Luo et al., 2023). Sleep disturbances are strongly correlated with increased risk for emotional disorders, cognitive impairment, and neurodegenerative diseases in older adults (Culebras, 2021). A reliable predictive model for poor sleep quality is essential to pinpoint high-risk patients early and formulate appropriate preventive strategies timely.

Previous studies have also reported that demographic information, lifestyle behaviors, clinical characteristics, and laboratory parameters were connected with sleep quality (Pace et al., 2015; Lindley, 2018). A large population-based study showed that neck circumference, body mass index (BMI), waist circumference, age, and the National Institutes of Health Stroke score were the most influential variables in predicting post-stroke sleep-disordered breathing (Brown et al., 2020). While, Brown et al. (2020) suggested that objective tests are needed to differentiate ischemic stroke patients with and without sleep disturbance. Emerging evidence suggests that incorporating a set of biomarkers might improve the sensitivity and specificity for detecting sleep–wake disturbances (Sauchelli et al., 2016).

Hypocretin-1 (Hcrt-1, also known as orexin-A), primarily produced by the lateral hypothalamic neurons, critically regulates the sleep–wake cycle (De Luca et al., 2022). Meanwhile, Hcrt-1 provides neuroprotection against cerebral ischemia–reperfusion injury, reducing neurological deficits and infarct size (Pace et al., 2015). Intriguingly, mean cerebrospinal fluid (CSF) Hcrt-1 concentrations were significantly lower among ischaemic stroke patients compared with control subjects (Kotan et al., 2013). A research based on rat models of ischemic stroke also demonstrated that Hcrt was lowly expressed in brain of rats with ischemic stroke (Wu et al., 2022). Studies in neurodegenerative diseases show an association between poor sleep quality/sleep fragmentation and elevated Hcrt-1 levels in CSF (Liguori et al., 2016; Liguori et al., 2019). Conversely, Friedman et al. (2007) found a negative association between Hcrt-1 concentrations and sleep disturbances. Sauchelli et al. (2016) demonstrated that Hcrt-1 levels significantly correlate with poor sleep quality in patients with anorexia nervosa. However, other research reported no significant link between decreased CSF Hcrt-1 levels and clinical sleep disturbances (Ogawa et al., 2022; Compta et al., 2009). Thus, the relationship between CSF Hcrt-1 levels and sleep remains inconclusive, and evidence on the role of Hcrt-1 on sleep regulation among acute ischemic stroke patients is lack. Whether the Hcrt-1 levels in the CSF might be a useful biomarker for predicting poor sleep quality among elderly acute ischemic stroke patients?

Given the above, the implications of CSF Hcrt-1 levels and their significance as an independent predictor of poor sleep quality in elderly patients with acute ischemic stroke remain unexplored. This study aims to: (1) assess the association between CSF Hcrt-1 levels and sleep quality in elderly acute ischemic stroke patients; and (2) develop a poor sleep quality predictive model and evaluate it is efficacy, with and without the inclusion of Hcrt-1 levels.

Our study showed that 43.17% patients after acute ischemic stroke developed poor sleep quality at 1 month. Low CSF Hcrt-1 levels (≤74.94 pg/mL) were significantly associated with poor sleep quality. In addition, we developed the predictive model, including five predictors (hypertension, stroke history, NIHSS, depression, and Hcrt-1), and demonstrated the Hcrt-1 levels as a significant predictor to improve the clinical performance of the poor sleep quality predictive model for elderly patients with acute ischemic stroke greatly. It indicates that identified risk factors have a good predictive performance for the development of poor sleep quality. To the best of our knowledge, this is the first poor sleep quality predictive model incorporating CSF Hcrt-1 as an independent predictor among elderly patients with acute ischemic stroke.

Most research focuses on pre-stroke sleep disturbance and post-stroke depression (Fan et al., 2022; Dong et al., 2021). However, little is known about the role of depression in post-stroke poor sleep quality. It is well known that sleep disturbance have been considered the core secondary symptom of depression in the past decades. Fortunately, depression has been usually regarded as a risk factor for developing sleep disturbance (Fang et al., 2019). One meta-analysis study has suggested that sleep disturbance and depression had certain common physiopathology, including the activation of neurotransmitter and neuroendocrine systems like serotonin, oxidative stress, and inflammatory response (Wang et al., 2015). The possible hypothesis may be that levels of serotonin, norepinephrine and dopamine metabolites are decreased, and abnormal genetic regulation of serotonergic transmission has been observed in depression (Krishnan and Nestler, 2008). Those changes disturb the function of cholinergic and monoaminergic neurons, which also play a vital role in sleep parameters across all phases of sleep architecture (Wang et al., 2015).

In our study, stroke severity was another risk factor for sleep quality, which was similar to previous studies (Bassetti et al., 2006). A possible explanation is that limb movement disorders are common among acute ischemic stroke patients (Seitz et al., 2011). Some elderly patients develop myotonia and muscle tension which may lead to inconvenient movements, nocturnal urination, and difficulty in coughing up phlegm and getting out of bed, which all affect sleep quality (Luik et al., 2013).

Additionally, a history of stroke was an independent predictor of elderly patients with acute ischemic stroke (Moerch-Rasmussen et al., 2016; Oza et al., 2017). However, Huang et al. (2022) examining sleep disturbance in early acute ischemic stroke patients have found no association between stroke history and sleep quality. One research has shown that telomere length, as an indicator of aging, was significantly associated with stroke recurrence in ischemic stroke patients aged >65 years, but not in younger patients (Moerch-Rasmussen et al., 2016). In the present study, all patients were over 60 years, which may account for the differences in our findings compared to other reported findings (Huang et al., 2022).

Hcrt-1 concentration in the CSF might be a useful biomarker for the assessment of progression of brain tissue damage during the early stages of ischemic stroke (Kotan et al., 2013). Acute ischemic stroke patients with subjective sleep problems had lower CSF Hcrt-1 levels compared to controls without sleep disturbance (Liguori et al., 2016). The relationship between Hcrt-1 and poor sleep quality could be best explained by the following mechanisms. According to recent studies, Hcrt-1 is involved in blood pressure regulation (Abreu et al., 2020; Huber et al., 2018). Hcrt knock-out mice and Hcrt neuron-ablated transgenic rats have lower basal blood pressure (Li et al., 2013). One study reported that the blockage of Hcrt receptors attenuates blood pressure in hypertensive rats (Clifford et al., 2015). Another study demonstrated that intravenous administration of Hcrt decreases infarct volume by increasing cerebral blood flow (Bulbul et al., 2008). Wu et al. (2022) have shown that Hcrt-1 was lowly expressed in the brains of rats with ischemic stroke. Upregulating Hcrt-1 could improve sleep architecture of post-stroke rats, such as total sleep time, non-rapid eye movement frequency, and frequency of rapid eye movement (Wu et al., 2022). Given that hypertension is a risk factor for stroke, the promotion of Hcrt-1 secretion might be involved in the onset of sleep problems by regulating blood pressure (Wang et al., 2011; Gorgui et al., 2014).

Neuroinflammation, involving the over-expression of inflammatory mediators and proinflammatory cytokines, might be another primary hypothesis for the mechanism of poor sleep development and Hcrt-1. Inflammation conditions lead to Hcrt-1 neuron damage and a large increase in the amounts of wakefulness (Gerashchenko and Shiromani, 2004). A recent study reported that acute ischemic stroke people were reported to exhibit abnormal expression of pro-inflammatory cytokines, which might result in an increased risk for poor sleep quality (Song et al., 2015). Tumor necrosis factor alpha (TNF-α), an important neuroinflammatory cytokine, impaired the function of the Hcrt-1 system and regulated the process of sleep (Zhan et al., 2011; Zhan et al., 2019).

Several studies highlighted the anti-inflammatory function of Hcrt-1 in neuroinflammation diseases and oxidative stress caused by ischemic stroke (Xiong et al., 2013). Intracerebroventricular administration of Hcrt-1 before ischemia stroke reduces infarct size (Harada, 2014). Under ischemic conditions, Hcrt-1 promotes the survival of primary cortical neurons in vitro and alleviates neuronal damage by modulating post-ischemic glucose intolerance in vivo (Kitamura et al., 2010). Meanwhile, Hcrt-1 regulates infection-induced inflammation by modulating the IL-6 and TNF-α in microglia and has a protective role against ischemia stress (Xiong et al., 2013). However, exploring if there is a causal relationship between poor sleep and Hcrt-1 and the executive mechanisms is beyond the current study. Further studies are needed to explore the causal relationship between poor sleep and Hcrt-1 and the executive mechanisms.

The strengths of this study include the performance of the poor sleep quality predictive model for elderly patients with acute ischemic stroke. It was greatly improved after including hypocretin-1 as an independent predictor. Additionally, elderly patients with cerebrospinal fluid levels of hypocretin-1 lower than 74.94 pg/mL at admission were more likely to experience poor sleep quality. The findings emphasize the importance of routine Hcrt-1 assessment and further guide the development of strategies to prevent poor sleep quality for elderly patients with acute ischemic stroke. Moreover, the integration of this model into clinical practice can facilitate a more personalized care paradigm, ensuring that healthcare interventions are not merely reactive but anticipatory, thus fostering a holistic approach to stroke rehabilitation. This shift toward precision medicine could lead to significant advancements in patient-centered care, aligning treatment modalities with the unique needs of elderly stroke patients.

Our study also had several limitations. Firstly, as we included relatively small sample sizes in a single Department of the Stroke Center, the findings of this study cannot be generalized to all elderly patients with acute ischemic stroke in our society. Secondly, we just measured sleep quality and depression with self-report questionnaires. Thirdly, CSF Hcrt-1 levels were measured only at admission, and it is necessary to further explore the relationship between dynamic changes in Hcrt-1 levels and the development of sleep quality. Fourthly, as the included participants had minor stroke, this limits the generalizability of the study. Fifthly, it is difficult to obtain CSF from healthy elderly persons, so the results of Hcrt-1 levels among healthy controls was lack. Sixth, we did not perform an external validation of this predictive model by another independent cohort. Nevertheless, a bootstrap resampling procedure was conducted for internal validation by adjusting for model over-fitting, which demonstrated adequate value and clinical utility of Hcrt-1 of this model in predicting poor sleep quality. Further studies with expanded sample sizes, objective depression and sleep measures, large-scale multi-center studies, and a health control arm should be conducted.

In summary, this prospective cohort study developed a poor sleep quality predictive model incorporating Hcrt-1 among elderly patients with acute ischemic stroke. The predictive model including Hcrt-1 can improve performance and clinical utility in predicting poor sleep quality. The validations also demonstrated the accurate and stable predictive performance of this model. We postulate that the evidence derived from this study is strong enough to support the implementation of routine Hcrt-1 assessment and further guide the development of strategies to prevent poor sleep quality for elderly patients with acute ischemic stroke.