Reduced serum levels of mitochondria-derived peptide MOTS-c in patients with obstructive sleep apnea

Abstract

Recent research has identified the mitochondrial open reading frame of the 12S rRNA-c (MOTS-c) as a crucial mitochondrial peptide that significantly influences metabolic regulation, mimics the effects of exercise, and mitigates oxidative stress. This study aims to investigate the relationship between serum MOTS-c levels and obstructive sleep apnea (OSA) to enhance our understanding of the disease’s pathophysiology. By elucidating this relationship, we hope to uncover new insights into the mechanisms underlying OSA and its associated metabolic complications. Seventy-seven participants were enrolled in this study, including 53 patients with OSA and 24 controls. We measured serum MOTS-c levels and collected participants’ demographic characteristics, polysomnography (PSG) data, complete blood count (CBC) data, and sleep-related questionnaires. The study included 77 participants, consisting of 8 patients with mild OSA, 16 with moderate OSA, 29 with severe OSA, and 24 controls. The cohort comprised 26 women and 51 men. Analysis revealed that serum MOTS-c levels were significantly correlated with BMI, AHI (Apnea–Hypopnea Index), and ODI (Oxygen Desaturation Index), independent of age. Additionally, the severity of OSA was inversely related to serum MOTS-c levels, with lower levels observed in patients with more severe OSA. Variations in serum MOTS-c levels were also noted across different BMI classifications. Analysis of covariance (ANCOVA), with BMI as a covariate, demonstrated that the severity of OSA was an independent factor influencing serum MOTS-c levels. Serum MOTS-c levels correlate with both severity of OSA and BMI classification, suggesting that MOTS-c may have significant therapeutic potential for treating OSA.

Introduction

Obstructive sleep apnea (OSA) is an increasingly prevalent sleep disorder, affecting approximately 176 million people in China, the highest prevalence among the top ten countries globally [1]. The primary characteristic of OSA is the recurrent collapse of the upper airway during sleep, resulting in apnea or hypopnea, which causes nocturnal hypoxemia and disrupts sleep architecture [2]. OSA not only affects patients’ sleep quality and daytime alertness but is also closely associated with various metabolic disorders and diabetes [3]. Our previous research has shown that the genioglossus muscle, one of the primary muscles responsible for maintaining upper airway patency, exhibits decreased nighttime activity and impaired mitochondrial function under conditions of chronic intermittent hypoxia (CIH), the main pathophysiological state of OSA [4]. Furthermore, as mitochondria are the cellular energy powerhouses, their functional impairment can lead to metabolic disorders [5], exacerbating OSA pathophysiology.

MOTS-c, a peptide encoded by a short open reading frame within mitochondrial DNA, has gradually revealed its functions. Intracellularly, MOTS-c acts as a bioactive peptide regulating nuclear gene expression and cellular metabolism, while extracellularly, it functions hormonally [6]. Skeletal muscle appears to be its primary target organ, where it enhances glucose uptake by activating the AMPK pathway and significantly improves systemic insulin sensitivity [7]. Accumulation of fat is a risk factor for OSA, and the upper airway in OSA patients is more prone to collapse than in healthy individuals. This suggests that MOTS-c may play a crucial role in the development and the progression of OSA.

Recent studies have discovered that MOTS-c can alleviate skeletal muscle atrophy by inhibiting lipid infiltration. Intraperitoneal injection of MOTS-c suppresses muscle creatinine expression via the PTEN/AKT/FOXO1 signaling pathway, thereby preventing diet-induced muscle wasting [8]. Furthermore, MOTS-c can be induced by various forms of exercise, such as aerobic exercise and high-intensity interval training, and can mimic the effects of exercise as a therapeutic agent [9, 10]. Muscle function training (oropharyngeal exercises) has shown significant efficacy in OSA patients [11], yet no studies have examined the relationship between the genioglossus muscle and MOTS-c.

Another hallmark of OSA patients is systemic chronic intermittent hypoxia during sleep [12], which leads to cellular oxidative stress damage and affects overall health. As an oxidative stress-related peptide, MOTS-c exerts antioxidant defense by activating the Nrf2/ARE signaling pathway and inhibiting the NF-κB pathway [13], with circulating levels decreasing in various disease states [14, 15].

Only one study has explored the relationship between MOTS-c and OSA, primarily focusing on insulin resistance [16]. This study had limitations, including the lack of comprehensive OSA-related indicators and no mention of treatment details. Additionally, no clinical studies have been conducted on East Asian populations. Given these limitations, our study aims to evaluate potential changes in serum MOTS-c levels in OSA patients and investigate their associations with other relevant indicators (age, gender, BMI, CBC data, and PSG data), thereby providing clinical evidence for the relationship between OSA and MOTS-c.

Materials and methods

Subjects

This single-center, cross-sectional study included 77 participants recruited from the Otolaryngology outpatient clinic of Jiangsu Province People’s Hospital between January and February 2024. All OSA patients had not received any related treatment. The negative control group comprised patients with other conditions, such as epistaxis, tinnitus, hoarseness, and chronic pharyngitis.

The exclusion criteria were as follows: under 18 years of age, pregnant women, acute inflammation or infection, surgery within the past year, current or previous benign/malignant tumors, diabetes, hyperlipidemia, liver or kidney dysfunction, and cardiopulmonary dysfunction.

Sample size calculation was performed by the method described by Suyeon Park et al. [17].

The diagnostic criteria and the severity of OSA were classified according to the most commonly used clinical method recommended in the 2023 International Consensus Statement on OSA [18].

BMI classification was based on the “Guidelines for the Prevention and Control of Overweight and Obesity in Chinese Adults” published by the Disease Control Department of the Ministry of Health of the People’s Republic of China:

• 1.Obesity: BMI ≥ 28.0 kg/m²,

• 2.Overweight: BMI ≥ 24.0 and < 28.0 kg/m²,

• 3.Normal weight: BMI ≥ 18.5 and < 24.0 kg/m²,

• 4.Underweight: BMI < 18.5 kg/m².

Measurement

Demographic characteristics of each study participant, including age, gender, height, weight, medical history, and Epworth Sleepiness Scale (ESS) score were recorded. All participants in the non-control group underwent an overnight portable polysomnography examination, with pulse oximetry recording blood oxygen saturation and pulse data.

Venous blood samples were centrifuged at 4000 rpm for 15 min after collection. The serum was then frozen and stored at −80 °C until analysis. Serum MOTS-c concentrations were measured using a commercially available ELISA kit (Mitochondrial Open Reading Frame of the 12S rRNA-c: MOTS-c kit, cat. CEX132Hu, Cloud-Clone Corp., Wuhan, China) with a detection range of 2.47 pg/ml to 200 pg/ml.

Statistical analysis

Data were expressed as mean ± standard deviation (SD). Parametric data were analyzed using Student’s t test and ANCOVA, while non-parametric data were analyzed using Mann–Whitney U and Kruskal–Wallis tests. Post hoc analysis was performed using least significant difference (LSD) method to identify the sources of intergroup differences. Spearman’s rank correlation coefficient was used for correlation analysis. Data analysis was conducted using SPSS and R (version 4.3.1). A P value of < 0.05 was considered statistically significant.

Results

A total of 77 participants were included in this study, comprising 8 with mild OSA, 16 with moderate OSA, 29 with severe OSA, and 24 controls. Table 1 shows demographics, PSG data, blood counts, and MOTS-c data of the participants.

Table 1.

Comparison of basic data between patients with OSA and controls

	Control group (n=24)	Mild group (n=8)	Moderate group (n=16)	Severe group(n=29)	P value
Male/female (n/n)	9/15	5/3	10/6	27/2
MOTS-c (ng/ml)	6.79±3.25	5.58±2.69	5.01±2.64	4.82±1.67	0.039
Age (years)	37.42±13.79	34.25±9.32	45±15.75	37.59±9.16	0.231*
Height (cm)	166.71±7.71	170.5±5.9	168.12±7.47	172.69±6.71	0.032
Weight (kg)	63.83±11	76.38±12.28	70.31±9.53	83.38±12.19	0.000
BMI (kg/m2)	22.93±3.4	26.2±3.57	24.82±2.28	27.94±3.66	0.000
AHI (/h)	–	8.43±3.3	21.54±4.72	64.21±18.71	0.000*
AI (/h)	–	4.01±2.34	15.93±5.46	56.24±18.55	0.000*
HI (/h)	–	4.42±4.19	5.61±4.88	7.96±7.53	0.000*
ODI (/h)	–	7.61±5.18	12.71±6.35	50.8±19.82	0.000*
LSaO2 (%)	–	83.88±5.84	77.69±10.15	68.03±9.7	0.000*
TS90 (%)	–	1.1±1.19	5.67±6.41	29.17±22.06	0.000*
TS85 (%)	–	0.15±0.28	1.66±2.36	15.78±18.42	0.000*
TS80 (%)	–	0.03±0.07	0.45±0.87	7.8±12.22	0.000*
WBC (109/L)	6.05 ± 1.29	7 ± 1.53	7.18 ± 1.22	8.05 ± 1.54	0.000
LY# (109/L)	2.02 ± 0.7	2.27 ± 0.64	2.45 ± 0.49	2.5 ± 0.59	0.039
MO# (109/L)	0.41 ± 0.11	0.47 ± 0.14	0.5 ± 0.13	0.57 ± 0.16	0.000
NE# (109/L)	3.44 ± 0.74	4.06 ± 1.28	3.96 ± 0.94	4.71 ± 1.25	0.000*
EO# (109/L)	0.14 ± 0.11	0.17 ± 0.09	0.23 ± 0.33	0.22 ± 0.18	0.185*
BA# (109/L)	0.03 ± 0.02	0.03 ± 0.03	0.04 ± 0.02	0.04 ± 0.02	0.063*
LY% (%)	32.97 ± 6.83	33.09 ± 9.42	34.58 ± 7.29	31.33 ± 6.78	0.537
MO% (%)	7.06 ± 2.46	6.85 ± 1.81	7.06 ± 1.66	7.13 ± 1.87	0.001*
NE (%)	57.29 ± 7.54	57.24 ± 7.91	54.67 ± 7.59	58.27 ± 6.99	0.485
EO% (%)	2.2 ± 1.5	2.39 ± 1.02	3.12 ± 3.49	2.73 ± 1.97	0.735
BA% (%)	0.49 ± 0.27	0.44 ± 0.29	0.56 ± 0.2	0.53 ± 0.26	0.372
RBC (109/L)	4.61 ± 0.63	4.98 ± 0.22	4.93 ± 0.74	5.21 ± 0.49	0.003*
HGB (g/L)	138.96 ± 19.47	155.12 ± 9.63	144.69 ± 13.14	153.62 ± 11.37	0.010*
HCT (%)	42.43 ± 5.25	45.71 ± 2.88	43.44 ± 3.56	46.58 ± 3.28	0.005*
MCV (fL)	92.33 ± 4.92	91.69 ± 3.15	88.97 ± 6.18	89.87 ± 6.11	0.273*
MCH (pg)	30.22 ± 1.98	31.1 ± 0.78	29.65 ± 2.37	29.33 ± 2.77	0.068*
MCHC (g/L)	455.83 ± 634.64	339.38 ± 9.18	333.06 ± 12.2	329.86 ± 8.85	0.028*
RDW-CV (%)	12.71 ± 0.63	12.51 ± 0.25	12.59 ± 1.12	12.77 ± 0.97	0.662*
PLT (109/L)	238.21 ± 64.04	233.12 ± 34.2	238.12 ± 51.97	274.45 ± 64.93	0.081
PCT (%)	0.24 ± 0.06	0.23 ± 0.03	0.23 ± 0.05	0.27 ± 0.05	0.028*
MPV (fL)	9.94 ± 1.03	9.71 ± 0.51	9.99 ± 1.04	10.03 ± 0.94	0.872
PDW (%)	14.19 ± 2.71	13.65 ± 2.77	11.74 ± 2.59	14.49 ± 2.59	0.044*

MOTS-c Mitochondrial Open Reading Frame of the 12S rRNA-c, BMI Body Mass Index, AHI Apnea–Hypopnea Index, AI Apnea Index, HI Hypopnea Index, ODI Oxygen Desaturation Index, LSaO2 Lowest Saturation of Oxygen, TS90 Time Spent Below 90% Oxygen Saturation, TS85 Time Spent Below 85% Oxygen Saturation, TS80 Time Spent Below 80% Oxygen Saturation, WBC White Blood Cell Count, LY# Absolute Lymphocyte Count, MO# Absolute Monocyte Count, NE# Absolute Neutrophil Count, EO# Absolute Eosinophil Count, BA# Absolute Basophil Count, LY% Lymphocyte Percentage, MO% Monocyte Percentage, NE% Neutrophil Percentage, EO% Eosinophil Percentage, BA% Basophil Percentage, RBC Red Blood Cell Count, HBG Hemoglobin, HCT Hematocrit, MCV Mean Corpuscular Volume, MCH Mean Corpuscular Hemoglobin, MCHC Mean Corpuscular Hemoglobin Concentration, RDW-CV Red Cell Distribution Width-Coefficient of Variation, PLT Platelet Count, PCT Platelet, MPV Mean Platelet Volume, PDW Platelet Distribution Width

^*P value is based on Kruskal–Wallis test

There were no significant differences in age between the groups (37.42 ± 13.79, 34.25 ± 9.32, 45 ± 15.75, 37.59 ± 9.16 years; P = 0.231). However, BMI varied significantly across groups (22.93 ± 3.4, 26.2 ± 3.57, 24.82 ± 2.28, 27.94 ± 3.66 kg/m²; P < 0.001). Serum MOTS-c levels also showed significant differences between groups (6.79 ± 3.25, 5.58 ± 2.69, 5.01 ± 2.64, 4.82 ± 1.67 ng/ml; P = 0.039; Figure 1). Post hoc LSD analysis revealed that the primary differences in serum MOTS-c levels were between the moderate/severe OSA groups and the control group, while no significant differences were observed between moderate and severe OSA groups. Additionally, serum MOTS-c levels differed significantly across BMI classifications, with the main differences observed between obese and overweight groups.

Fig. 1.

Boxplot of circulating MOTS-c concentration in control, Mild, Moderate and Severe OSA groups. Lower border of the box plots represents the 25th percentile and the upper border represents the 75th percentile ± SD. Short–inside the Boxplot represents statistical mean for MOTS-c. *: P < 0.05, based on least significant difference (LSD) method

Further analysis revealed significant correlations between serum MOTS-c levels and key parameters in the overall population. Serum MOTS-c was negatively correlated with BMI (including Height and Weight, Figure 2a), AHI (including Apnea Index and Hypopnea Index, Figure 2b), and ODI (Figure 2c). AHI also demonstrated associations with various hematological parameters, including White Blood Cell (WBC), Lymphocyte Count (LY#), Monocyte Count (MO#), Neutrophil Count (NW#), and Basophil Count (BA#) as summarized in Table 2.

Fig. 2.

These scatter plots show a negative correlation between MOTS-c concentration and BMI (a), AHI (b), and ODI (c)

Table 2.

The analysis of the correlation between AHI and continuous parameters

	Spearman correlation coefficient (r)	P value
WBC (109/L)	0.514	0.000
LY# (109/L)	0.299	0.008
MO# (109/L)	0.400	0.000
NE# (109/L)	0.506	0.000
EO# (109/L)	0.190	0.098
BA# (109/L)	0.244	0.032
LY% (%)	−0.139	0.228
MO% (%)	0.050	0.667
NE (%)	0.119	0.303
EO% (%)	0.046	0.692
BA% (%)	0.070	0.545
RBC (109/L)	0.398	0.000
HGB (g/L)	0.278	0.014
HCT (%)	0.332	0.003
MCV (fL)	−0.241	0.034
MCH (pg)	−0.244	0.033
MCHC (g/L)	0.023	0.843
RDW-CV (%)	−0.016	0.887
PLT (109/L)	0.213	0.063
PCT (109/L)	0.281	0.013
MPV (fL)	0.115	0.319
PDW (%)	0.034	0.772

Considering the effect of BMI on serum MOTS-c, we performed ANCOVA analysis with BMI as a covariate for moderate and severe versus control groups, and the results (F value=8.860, P=0.004) showed that OSA severity remained as an influencing factor for serum MOTS-c.

Discussion

In this study, we identified a correlation between decreased serum MOTS-c levels and OSA severity in patients. This correlation remained even after adjusting for BMI, indicating that MOTS-c, a peptide from mitochondrial DNA, may be crucial in OSA development. Previous research found a similar correlation in a Turkish population [16], and our findings align with those in East Asian populations. Unlike the previous study, we focused primarily on OSA-related indicators, with less emphasis on metabolic and endothelial function factors.

To date, three mitochondrial-derived peptides have been identified: Humanin (HN), Mitochondrial Open Reading Frame of the 12S rRNA-c (MOTS-c), and Small Humanin-like Peptides (SHLPs1-6). MOTS-c participates in nuclear translocation and regulates gene expression within cells. Under metabolic stress conditions, such as glucose restriction and oxidative stress, MOTS-c can quickly and transiently move to the nucleus, where it directly modulates adaptive nuclear gene expression to maintain cellular homeostasis [19]. Clinical studies have demonstrated that serum MOTS-c levels are downregulated in patients with Type 2 diabetes (T2D) [20]. Our findings align with this, showing a correlation between BMI and serum MOTS-c levels, implying that abnormal fat accumulation may compromise mitochondrial function, particularly the coding capacity of short open reading frames although the precise pathological mechanisms remain unclear.

Skeletal muscle, the primary target organ for MOTS-c, is influenced by its activity. A study involving healthy individuals discovered a potential association between serum MOTS-c concentration and greater muscle mass, strength, and power during jumping activities [21]. This association may be attributed to MOTS-c’s capacity to significantly enhance myotube formation [22] and promote skeletal muscle glucose uptake through the activation of casein kinase 2 (CK2) [23]. In the majority of OSA patients, the primary cause of airway collapse is the relaxation of the muscles surrounding the upper airway. However, changes in MOTS-c levels in the soft palate muscles (such as the Levator Veli Palatini and Tensor Veli Palatini), tongue muscles (including the Hyoglossus and Genioglossus), and pharyngeal muscles remain unknown, which will be the focus of our future research. Notably, MOTS-c exhibits exercise-mimicking effects, with circulating levels increasing post-exercise [24]. Oropharyngeal training has been shown to effectively improve snoring frequency, snoring intensity, and daytime sleepiness in OSA patients [25]. However, whether this training increases MOTS-c levels in muscles remains to be determined.

Our study identified a negative correlation between serum MOTS-c levels and Oxygen Desaturation Index (ODI) in patients. A primary characteristic of OSA patients is chronic intermittent hypoxia during the night, which triggers a systemic inflammatory response and an increase in reactive oxygen species (ROS) [26]. Excessive ROS disrupts the oxidant–antioxidant balance, leading to oxidative stress and subsequent cellular damage [27]. MOTS-c, as a mitigating factor for oxidative stress damage, activates the Nrf2 pathway via the AMPK signaling pathway, thereby enhancing the expression of antioxidant response elements (ARE) within cells and increasing resistance to oxidative stress. Previous research has demonstrated that resveratrol alleviates CIH-induced myocardial injury by activating AMPK and enhancing Nrf2-mediated antioxidant responses [28]. Similarly, MOTS-c has been shown to mitigate myocardial damage under diabetic conditions by modulating the Nrf2/ARE and NF-κB pathways [29]. These findings suggest that MOTS-c may have the potential to alleviate OSA-induced damage through the AMPK-Nrf2 pathway.

Other research has found that human plasma MOTS-c levels decrease with age. Compared to young adults (18–30 years), middle-aged individuals (45–55 years) and older adults (70–81 years) show a reduction in circulating MOTS-c levels by 11% and 21%, respectively [30]. In our study, the impact of age on serum MOTS-c did not reach statistical significance, possibly due to small sample size and insufficient representation across different age groups. Age is a known risk factor for OSA development [31]. Whether the increased incidence of OSA with age is related to a decline in circulating MOTS-c levels and subsequent deterioration of bodily functions remains to be further investigated.

Our study also found that peripheral white blood cell counts increase with OSA severity. This may be due to CIH activating the body’s inflammatory pathways, promoting the release of inflammatory factors, such as IL-6 and TNF-α, thereby stimulating the bone marrow to produce more white blood cells, including neutrophils and lymphocytes [32]. This observation may be related to the decrease in MOTS-c, which leads to an increase in local inflammatory responses. MOTS-c has been shown to reduce inflammation by activating the AMPK pathway [33, 34]. Differences in hemoglobin, hematocrit, and mean corpuscular hemoglobin concentration have also been corroborated by other studies [35]. Given that this is a single-center, cross-sectional study involving an East Asian population with a relatively small sample size, these results should be interpreted with caution.

This study substantiates an independent correlation between serum MOTS-c levels and OSA severity, offering novel insights into the role of mitochondrial damage in OSA pathogenesis. Nevertheless, research on the involvement of mitochondrial-derived peptides, such as MOTS-c, in OSA development and progression remains incomplete. Additionally, the therapeutic potential of exogenously administered MOTS-c warrants further investigations.

Author contributions

Min Yin contributed to the study conception and design. Qingfeng Pang provided guidance on the study design. Yawen Shi offered guidance on the statistical methods. Material preparation, data collection and analysis were performed by Zhuoding Luo, Renjing Ye and Rui Ji. The first draft of the manuscript was written by Zhuoding Luo and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Funding

Supported by Beijing Bethune Charitable Foundation (2023YWZJ006-ym).

Declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies were approved by the experimental animal ethics committee of Nanjing Medical University and have therefore been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.