Sleep disorders have become a significant public health problem globally, with epidemiological data highlighting their widespread prevalence. Studies indicate that approximately 33% of the global population experiences insomnia, while between 10 and 15% of individuals experience chronic insomnia. These results highlight the pressing necessity for action and additional studies to tackle this escalating public health issue (1, 2). In specific disease populations (e.g., Parkinson’s disease, diabetes, and Tourette syndrome), the prevalence of sleep disorders can reach 71–77.5% (3–5). Neuropsychiatric disorders, including stroke, depression, and anxiety, along with neurodegenerative diseases like Alzheimer’s, and metabolic conditions such as diabetes, frequently accompany sleep disorders, leading to a detrimental cycle. For instance, the incidence of sleep disorders is markedly elevated in individuals with depression and may predict disease progression (6–8). The impact of sleep disorders spans physiological health, mental well-being, quality of life, and socioeconomic burden. Physiologically, sleep disorders elevate cardiovascular and metabolic risks. Studies demonstrate that sleep disorders are linked to sympathetic overactivation, increasing risks of hypertension, arteriosclerosis, and cardiovascular events (9). Among obstructive sleep apnea (OSA) patients, 60% exhibit worsened diabetes control, and sleep disorders directly exacerbate the progression of diabetes and its complications (10, 11). Sleep disorders also impair neurological health; OSA is associated with heightened stroke risk (12), while chronic sleep deprivation (SD) may contribute to cognitive impairment, white matter impairments, and Alzheimer’s disease (7). Furthermore, chronic sleep disorders weaken immune function and may elevate cancer risk (13). Psychologically, sleep disorders significantly increase psychotic experiences, depression, anxiety, and fatigue, particularly among adolescents, where sleep issues and smartphone addiction synergistically worsen mental health (14). Additionally, sleep disorders severely impair quality of life and amplify socioeconomic burdens, with over 50% of patients experiencing comorbid conditions (e.g., insomnia coupled with circadian rhythm disorders), leading to daytime drowsiness, reduced productivity, and diminished social engagement (15).

Longitudinal cohort studies reveal dynamic interactions between sleep, brain, and gut microbiota beginning in infancy. A study tracking 162 healthy infants showed that daytime sleep duration significantly correlates with gut microbiota α-diversity (p < 0.05), while nighttime sleep continuity associates with specific microbial taxa abundance (16), suggesting co-evolution of sleep patterns and gut microbiota early in life. In adults, large-scale population-based research (n = 1,809) identified altered chronic insomnia patients’ abundances of Ruminococcaceae UCG-002/UCG-003, with dose–response relationships to bile acid metabolism dysregulation (17). Such microbiota–metabolite axis disruptions may mediate links between sleep disorders and cardiometabolic diseases.

As a complex ecosystem, the gut microbiota is affected by the host’s genetic makeup, environmental factors, and dietary habits, with dietary patterns exerting a dominant influence on microbial metabolic activity and homeostasis (18, 19). Traditional approaches, such as the sulfur microbial diet, which tracks sulfate-reducing bacterial pathways, highlight links between dietary sulfur and specific microbiota–metabolite interactions (20, 21). However, such targeted methods fail to capture the holistic impact of diet on microbial ecology. This new indicator of dietary index for gut microbiota (DI-GM) addresses this gap by integrating multidimensional parameters to quantify dietary quality and microbiota health (22). High DI-GM scores correlate with enhanced gut microbiota α-diversity, increased SCFA-producing taxa (e.g., Ruminococcus and Prevotella), and reduced pro-inflammatory bacteria (e.g., Desulfovibrionaceae) (19, 23, 24). Unlike single-dimensional indices, DI-GM dynamically reflects functional markers like SCFA synthesis and the Firmicutes/Bacteroidetes ratio, offering a comprehensive tool to study diet–microbiota interactions in long-term conditions like metabolic syndrome and heart diseases (18, 23, 25).

The American Heart Association (AHA) has crafted Life’s Essential 8 (LE8) as a comprehensive approach to optimize heart health and encourage longevity through lifestyle adjustments. LE8 evaluates eight key health metrics: quality of diet, levels of physical activity, nicotine intake, sleep routines, weight management, lipid profiles, glucose concentrations, and blood pressure management (26). By addressing these interconnected dimensions, LE8 provides a structured approach to improving overall health and extending the duration of life free from chronic disease. Recent studies suggest that the DI-GM may influence sleep quality via multiple LE8 dimensions. For instance, DI-GM improves insulin sensitivity and lipid metabolism, thereby reducing the risks of obesity and metabolic syndrome, which indirectly mitigates sleep disorders (27, 28).

Nevertheless, evidence linking DI-GM to sleep disorders remains scarce. Existing studies suggest that gut microbiota influences sleep rhythms through metabolite secretion and neuroimmune pathways (29, 30). However, population-level analyses quantifying the DI-GM in relation to sleep disorders risks remain scarce. Notably, associations between prefrontal cortex gray matter thickness and slow-wave sleep highlight how the microbiota–gut–brain axis (MGBA) contributes to sleep regulation (30). To address these gaps, this investigation uses data sourced from the American National Health and Nutrition Examination Survey (NHANES) to explore the link between DI-GM scores and the risk of sleep disorders, offering fresh insights into how dietary patterns that influence gut microbiota may contribute to sleep disorders. Furthermore, this study will examine the probable mediation of LE8 in this relationship.

The study investigated the connection between DI-GM and sleep disorders, based on NHANES data ranging from 2007 to 2018. These results indicated a robust negative connection between DI-GM and DI-GM beneficial scores and the risk of developing sleep disorders. Specifically, those with DI-GM scores of 5 or above showed a 34% or greater reduction in sleep disorders compared to those scoring 3 or below. Similarly, people with DI-GM beneficial scores of 6 or higher had a 35% lower risk than those with scores of 1 or less. Conversely, the analyses did not reveal any significant correlations between DI-GM unfavorable and sleep disorders. Subgroup analyses revealed that both DI-GM and DI-GM beneficial showed statistically significant negative associations with sleep disorders among males, current smokers, individuals consuming ≥12 alcoholic drinks/year, and those without hypertension, diabetes, or depression. The relationship between DI-GM and sleep disorders revealed a significant interaction with smoking. Meanwhile, the relationship between DI-GM beneficial and sleep disorders revealed a significant interaction with smoking and drinking. According to the findings, mLE8 appears to play a mediating role in the link between DI-GM, DI-GM beneficial, and sleep disorders. Elevated DI-GM scores correspond to a diminished prevalence of sleep disorders, with mLE8 playing a pivotal role in bridging this connection. These results affirm our initial hypothesis and underscore the significance of mLE8 as a mediating element in the connection between nutritional indices and sleep well-being.

The investigation focuses on the correlation between DI-GM and sleep disorders. Our results align with prior investigations linking gut microbiota diversity to sleep disturbances. To begin with, clinical studies consistently show that gut dysbiosis is present in patients suffering from insomnia and OSA, and other sleep disorders. For example, insomnia patients exhibit reduced abundances of Ruminococcaceae UCG-002 and UCG-003, which relate to the dysregulation of bile acid metabolism and may mediate cardiovascular metabolic risks through the microbiota–bile acid axis (17). OSA patients demonstrate depleted butyrate-producing bacteria (e.g., Roseburia and Faecalibacterium), with hypoxia severity (rather than apnea frequency) driving species-specific microbiota alterations (41, 42). Patients with type 1 narcolepsy (NT1) exhibited a significant difference in global bacterial community structure compared to controls (43). Second, microbiota diversity may predict sleep quality. A study in older adults found gut microbiota α-diversity (e.g., Shannon index) positively correlated with actigraphy-measured sleep quality, independent of age and lifestyle factors (44). Animal studies further support microbiota–sleep interactions. Comparisons between specific pathogen-free (SPF) and germ-free (GF) mice revealed that microbiota depletion alters sleep architecture. SD reduced fecal and hypothalamic butyrate levels in SPF mice but not GF mice, suggesting butyrate mediates sleep regulation via the MGBA (45). Chronic SD induces microbiota dysbiosis, activates NLRP3 inflammasomes found in the brain and colon, and impairs the function of the intestinal and blood–brain barriers, ultimately impairing cognition. Fecal microbiota transplantation confirmed that SD-induced dysbiosis directly drives cognitive deficits, highlighting the MGBA in sleep-related neurodegeneration (46, 47). SD-associated dysbiosis also promotes neuroinflammation (e.g., microglial activation AND elevated IL-1β/TNF-α), partially reversible via probiotic supplementation (48).

Current studies reveal that the association between gut microbiota and sleep disorders involves sophisticated two-way regulatory mechanisms. The DI-GM score primarily serves as an index of the variety of gut microbiota and is strongly linked to the synthesis of SCFAs. This interaction is likely mediated by the MGBA, encompassing multiple pathways such as metabolic, immune, and neural signaling. First, metabolic pathways: gut microbiota-derived SCFAs (e.g., butyrate and propionate) penetrate the blood–brain barrier to modulate the function of the central nervous system. Butyrate alleviates hypothalamic metabolic disturbances induced by SD and improves sleep architecture via vagus nerve activation (45, 49). Chronic insomnia correlates with bile acid dysregulation, where specific taxa (e.g., Ruminococcaceae) influence cardiometabolic risks through bile acid metabolism, underscoring the metabolic axis in sleep disorders (17). Additionally, microbiota metabolites (e.g., tryptophan and serotonin precursors) regulate serotonin and melatonin synthesis, directly modulating sleep–wake cycles (50, 51). Second, immune and inflammatory pathways: SD compromises intestinal barrier integrity (e.g., reduced tight junction proteins), enabling bacterial byproducts (e.g., lipopolysaccharides) to trigger systemic inflammation (e.g., NF-κB activation) and impair sleep quality (46–48). Chronic sleep restriction has been shown to activate NLRP3 inflammasomes in the colon and brain, resulting in the secretion of pro-inflammatory cytokines, including IL-1β and IL-18. This inflammatory cascade contributes to neuroinflammation and is associated with cognitive impairments (46, 47, 49). Gut microbiota also modulates Th17/Treg balance and IL-17 secretion, indirectly influencing immune status in sleep-related brain regions (e.g., hypothalamus) (47, 49). Additionally, neural pathways: microbiota-derived signals are transmitted to the central nervous system via the vagus nerve, regulating hypothalamic–pituitary–adrenal (HPA) axis activity, cortisol levels, and stress responses, thereby disrupting circadian rhythms (45, 50, 52). Microbial metabolites (e.g., γ-aminobutyric acid, GABA) directly stimulate enterochromaffin cells to synthesize serotonin and modulate sleep-associated neurotransmitters (e.g., dopamine and norepinephrine) through the gut–brain axis (50, 51).

As a novel cardiovascular health metric, the relationship between LE8 and gut microbiota/sleep disorders remains underexplored. This investigation may provide significant evidence regarding the intersection of the “gut–brain axis” with cardiovascular health (53, 54). Current research suggests LE8 exhibits reciprocal interactions with sleep health across multiple dimensions. First, sleep itself—as an independent LE8 component—directly modulates cardiovascular risk via quantitative metrics (e.g., recommended 7–9 h duration) and qualitative metrics (e.g., reduced sleep fragmentation). SD lowers LE8 scores and activates inflammatory pathways (55–57). Second, metabolic indicators (BMI, blood glucose, and non-HDL cholesterol) exhibit bidirectional relationships with sleep. Obesity disrupts sleep homeostasis through chronic inflammation, while sleep insufficiency exacerbates insulin resistance and lipid dysregulation. Concurrently, HPA axis dysregulation (e.g., abnormal cortisol rhythms) further impairs sleep (58). Third, the behavioral components of LE8, including physical activity, dietary patterns, and smoking cessation, act synergistically to improve sleep architecture through distinct yet complementary biological mechanisms: regular exercise enhances adenosine accumulation that promotes sleep drive, balanced diets optimize the serotonin–melatonin axis critical for circadian rhythm regulation, and smoking cessation eliminates nicotine’s disruptive stimulation of acetylcholine receptors, collectively reducing sleep fragmentation and stabilizing sleep–wake cycles (59–61). Therefore, LE8 establishes a dynamic interaction network between sleep and cardiovascular health through integrated pathways encompassing direct sleep assessment, metabolic-inflammatory regulation, and behavioral interventions. The DI-GM may improve LE8 through the following pathways. By reflecting diet-induced modulation of microbial diversity, DIGM potentially regulates the production of gut-derived metabolites such as SCFAs and tryptophan derivatives (e.g., serotonin and melatonin precursors) (62). These metabolites act via the MGBA axis to directly enhance sleep metrics of LE8, including prolonged deep sleep duration and reduced sleep onset latency, through central nervous system modulation (62). For instance, SCFAs can cross the blood–brain barrier to normalize HPA axis activity, thereby lowering stress hormone levels and improving sleep quality (63). Furthermore, DIGM-optimized dietary patterns (rich in fiber and polyphenols) not only foster microbial equilibrium but also indirectly boost LE8 scores through systemic anti-inflammatory effects, circadian rhythm regulation, and enhanced intestinal barrier integrity (45, 64, 65).

The mediating role of LE8 as an intermediate variable may arise from the following mechanisms. Dietary components (e.g., tryptophan) serve dual roles as both scoring elements in DIGM and precursors for melatonin synthesis, directly influencing sleep metrics of LE8. DIGM-optimized gut microbiota may transmit signals via vagal pathways with the nucleus tractus solitarius (NTS) in the brainstem, modulating autonomic balance metrics of LE8—such as heart rate variability (HRV)—thereby affecting sleep architecture (64). Conversely, improvements in sleep parameters of LE8 (e.g., extended sleep duration) may reciprocally enhance the abundance of specific microbial taxa (e.g., Lactobacillus genus), establishing a positive feedback loop (66). Additionally, UK Biobank research suggests that the DIGM–sleep association may be modulated by polygenic risk scores (PRS). For instance, individuals carrying specific microbiota-related genetic variants exhibit more pronounced improvements in LE8 sleep scores through DIGM interventions (67, 68). Conversely, the non-significant mediation effects of individual mLE8 components underscore the importance of integrated health management.

Our study holds significant clinical implications to demonstrate positive connections between the DI-GM, DI-GM beneficial, and sleep disorders using large-scale population data. The findings suggested that those with DI-GM scores of 5 or above showed a 34% or greater reduction in sleep disorders compared to those scoring 3 or below, while a DI-GM beneficial score ≥6 corresponded to a 35% reduction compared to those with scores of 1 or less. Although causal relationships cannot be definitively established, these findings imply that optimizing gut microbiota composition through dietary modifications may be utilized as a novel method of intervention for preventing or alleviating sleep disorders, such as increasing intake of beneficial ingredients (e.g., cranberries, avocados, broccoli, whole grains, and fermented dairy products) and reducing unfavorable components (e.g., red meat and processed foods). Importantly, beneficial DI-GM components, such as polyphenols in coffee and green tea, may improve sleep quality via mechanisms involving gut–brain axis modulation and systemic inflammation reduction (69). Interestingly, no statistically significant link was found between unfavorable dietary components (DI-GM unfavorable) and sleep disorders, highlighting that prioritizing beneficial food intake over restrictive dietary approaches may be more effective for sleep health. Subgroup analyses demonstrated that this association remained statistically significant among males, current smokers, alcohol consumers, and individuals without hypertension, diabetes, or depression. These findings contribute to the understanding that cross-system regulation of the “diet–microbiota–sleep” axis and advocate for integrating DI-GM assessments into clinical nutrition protocols for sleep disorders patients, offering quantifiable criteria for personalized dietary guidelines. Furthermore, as a composite metric encompassing diet, physical activity, metabolism, cardiovascular health, and mental well-being, even after adjusting for sleep in mediation analyses, mLE8 still mediated the association between DI-GM and sleep disorders.

Nevertheless, our study acknowledges several limitations. First, the cross-sectional structure of NHANES data precludes causal inference, as the temporal sequence between DI-GM and sleep disorders risk cannot be established. Second, sleep disorders were assessed via self-report, introducing potential recall bias and lacking objective validation (e.g., polysomnography). This may lead to underdiagnosis of some patients with sleep disorders, reflecting a limitation of NHANES. Therefore, we included sleep duration as a covariate in our study to minimize measurement bias. Third, although multiple confounders were adjusted for, residual confounding from unmeasured genetic or environmental factors may persist. While subgroup analyses revealed significant associations in smokers and alcohol consumers, heterogeneity across other subgroups (e.g., racial/ethnic groups or individuals with metabolic comorbidities) requires validation in larger samples. Moreover, DI-GM differs from existing indices by incorporating specific food items (e.g., fermented dairy) rather than broad food groups (22, 70), and excludes foods not yet linked to gut microbiota in prior studies (22). The independent mechanisms of specific DI-GM components (e.g., fermented dairy or whole grains) remain unclear, necessitating experimental research to elucidate microbiota-mediated biological pathways. Finally, the use of mLE8 in mediation analyses may not fully capture the original LE8 construct due to the exclusion of sleep components. Future longitudinal cohorts and dietary intervention trials are warranted to confirm the clinical utility of DI-GM-guided strategies for sleep health.

DI-GM and DI-GM beneficial were both significantly and inversely associated with sleep disorders. Those with DI-GM scores of 5 or above showed a 34% or greater reduction in sleep disorders compared to those scoring 3 or below. Similarly, people with DI-GM beneficial scores of 6 or higher had a 35% reduction than those with scores of 1 or less in sleep disorders. Additionally, mediation analysis revealed that mLE8 played a mediating role in this relationship. Developing integrated intervention programs that combine nutritional counseling and lifestyle management may represent a more optimal approach for patients with sleep disorders.