System for early predicting onset of obstructive sleep apnea disease based on sleep behavior

Abstract

The invention provides a system for early predicting the incidence of obstructive sleep apnea diseases based on sleep behaviors. Specifically, the invention provides a sleep behavior-based early obstructive sleep apnea (OSA) disease occurrence risk assessment system, which comprises (a) an input module configured to input sleep behavior data of a certain object; (b) an evaluation module which is configured to perform preliminary evaluation on the input sleep behavior data, obtain a sleep score and perform discrimination processing; and (d) an output module configured to output the evaluation result value. The system provided by the invention can predict chronic obstructive sleep apnea more effectively, more early and more accurately, and provides effective prevention suggestions.

Description

Technical Field

[0001] The present invention relates to the field of medical disease prediction, and in particular to a system for early prediction of the onset of obstructive sleep apnea based on sleep behavior. Background Art

[0002] Obstructive sleep apnea (OSA) is recognized by the International Academy of Sleep Medicine as the most common type of sleep breathing disorder. It is the most common sleep disorder in the global population, affecting about 1 billion people worldwide. The disease progresses with the degree of airway collapse, and then symptoms such as awakening, sympathetic nerve excitement, hypoxemia and hypercapnia appear during sleep. It may also increase the risk of coronary heart disease (CHD), depression, hypothyroidism, asthma and inflammatory rheumatism (IRD) and type 2 diabetes (T2D), thereby reducing the health level of the population, and ultimately increasing the mortality rate of the population, and increasing the economic and social burden of the disease.

[0003] The gold standard for diagnosing OSA is to use polysomnography (PSG) for 12 hours of continuous monitoring throughout the night. Due to the high cost and long time (at least 2 days) of monitoring and diagnosing OSA with the help of PSG, coupled with the patients' neglect of the early symptoms of the disease, fewer people actively participate in sleep monitoring diagnosis, and patients with mild symptoms often miss the opportunity for timely diagnosis and medical treatment, thus developing moderate to severe OSA and leading to a variety of serious health consequences. It is estimated that only 1 in 50 OSA patients is evaluated and treated. Studies have pointed out that according to current standard clinical practice, the probability of misdiagnosis based on a single PSG test is as high as 20%, which increases to about 50% in patients with mild to moderate OSA. Due to the high misdiagnosis rate of PSG detection, OSA patients are easily missed and cannot be treated in time, resulting in an increasing burden of disease.

[0004] OSA is a complex, heterogeneous disease, and its main identified risk factors include obesity, male gender, and age. Studies have shown that sleep behaviors associated with OSA include snoring, sleep duration, and insomnia. In-depth research on OSA-related sleep behaviors plays a key role in improving the understanding of the disease mechanism, preventing the occurrence of the disease, and reducing the risk of adverse events caused by the disease.

[0005] With the rapid growth of OSA disease burden in recent years, how to conduct early warning and prevention of the disease has become an urgent problem to be solved in public health and clinical practice. At present, early screening of OSA is insufficient, and a quick and simple method is needed to achieve simple and fast prediction of early disease.

[0006] Therefore, there is an urgent need in the art to develop methods and devices that can more effectively, earlier and more conveniently predict and prevent OSA in its early stages. Summary of the invention

[0007] The present invention provides a method and device for predicting and preventing obstructive sleep apnea in an early stage more effectively, earlier and more conveniently.

[0008] In a first aspect of the present invention, a system for assessing the risk of early obstructive sleep apnea (OSA) disease based on sleep behavior is provided, the system comprising:

[0009] (a) an input module, the input module being configured to input sleep behavior data of a subject;

[0010] The sleep behaviors include the following sleep behavior combinations in group A:

[0011] (A1) snoring; (A2) excessive daytime sleepiness; (A3) ease of waking up in the morning; and (A4) daytime napping;

[0012] (b) an evaluation module, wherein the evaluation module is configured to perform a preliminary evaluation on the input sleep behavior data and obtain a sleep score, and perform a discrimination process; the sleep score is calculated by the following formula:

[0013]

[0014] Among them, S t Score sleep, W i is the weight coefficient of sleep behavior, S i To score sleep behavior;

[0015] The discrimination process includes comparing the sleep score with a control reference value to obtain a discrimination result; wherein, when the obtained sleep score is higher, the risk of the disease is lower; conversely, when the obtained sleep score is lower, the risk of the disease is higher; and

[0016] (d) An output module, configured to output an evaluation result value.

[0017] In another preferred example, the system optionally further comprises a suggestion module, wherein the suggestion module is configured to provide the subject with suggestions for improving sleep behavior based on the sleep score, thereby reducing the risk of disease.

[0018] In another preferred example, the improvement suggestion is output from the output module.

[0019] In another preferred example, the improvement suggestion and the evaluation result value are output from the output module simultaneously or at different times.

[0020] In another preferred example, the sleep behavior includes: risky sleep behavior and non-risky sleep behavior.

[0021] In another preferred embodiment, the non-risky sleeping behavior is selected from the following group: no snoring, no frequent daytime sleepiness, no daytime naps, easy to get up in the morning, sleeping 7-8 hours a day, morning type, never / rarely insomnia, or a combination thereof.

[0022] In another preferred embodiment, the sleep behavior further includes any sleep behavior selected from the following group B, or a combination thereof:

[0023] (B1) Sleep duration; (B2) Sleep time type; (B3) Insomnia.

[0024] In another preferred embodiment, the weight coefficient W i Is a positive number.

[0025] In another preferred embodiment, the weight coefficient W i The value of is 1.

[0026] In another preferred embodiment, when the weight coefficient W i When the values ​​of are all 1, the calculation formula of the sleep score is:

[0027]

[0028] In another preferred embodiment, the sleep score includes (a) a score of a single sleep behavior; and / or (b) a sum of multiple sleep scores.

[0029] In another preferred embodiment, the subject is a human.

[0030] In another preferred embodiment, the subject is an adult.

[0031] In another preferred example, the evaluation system includes a control module, and the control module is configured to control the operation of each module.

[0032] In another preferred embodiment, the input module is selected from the following group: a sleep behavior collector, a keyboard, a tablet computer (PAD), a smart phone, or a combination thereof.

[0033] In another preferred embodiment, the sleep behavior data is obtained by means of a questionnaire.

[0034] In another preferred embodiment, the questionnaire includes the following questions:

[0035] (i) Has your partner, close relatives or friends complained about your snoring?

[0036] (ii) How likely are you to doze off or fall asleep unintentionally during the day (e.g. while working, reading, or driving)?

[0037] (iii) Do you think it is easy for you to wake up early? and

[0038] (iv) Do you take naps during the day?

[0039] In another preferred embodiment, the questionnaire further includes questions selected from the following:

[0040] (v) How many hours do you sleep in a 24-hour period?

[0041] (vi) What kind of person do you think you are?

[0042] (vii) Do you have trouble falling asleep at night or wake up in the middle of the night?

[0043] or a combination thereof.

[0044] In another preferred example, the evaluation system further includes a processor and a memory.

[0045] In another preferred example, the data stored in the memory includes a sleep score reference value.

[0046] In another preferred embodiment, the output module includes a display, a printer, a tablet computer (PAD), a smart phone, or a combination thereof.

[0047] In another preferred embodiment, the modules are connected via wired or wireless means.

[0048] In a second aspect of the present invention, a method for assessing the risk of early-stage obstructive sleep apnea based on sleep behavior is provided, the method comprising:

[0049] (a) providing data, wherein the data includes sleep behavior data of a subject to be tested;

[0050] The sleep behavior data is from the following sleep behavior combination of group A:

[0051] (A1) snoring; (A2) excessive daytime sleepiness; (A3) ease of waking up in the morning; and (A4) daytime napping;

[0052] (b) analyzing and evaluating, evaluating the sleep behavior data according to the sleep scoring table of the present invention, and obtaining a sleep score, and comparing the sleep score with a control reference value; wherein a higher sleep score indicates a lower risk of disease onset; conversely, a lower sleep score indicates a higher risk of disease onset; and

[0053] (c) providing suggestions for improving sleep behavior to the subject based on the sleep score, thereby reducing the risk of disease.

[0054] In another preferred embodiment, the method is non-diagnostic and non-therapeutic.

[0055] In another preferred embodiment, the sleep behavior further includes any sleep behavior selected from the following group B, or a combination thereof:

[0056] (B1) Sleep duration; (B2) Sleep time type; (B3) Insomnia.

[0057] In another preferred embodiment, the subject is a human.

[0058] In another preferred embodiment, the subject is an adult.

[0059] In another preferred embodiment, the sleep behavior data is obtained by means of a questionnaire.

[0060] In another preferred embodiment, the questionnaire includes the following questions:

[0061] (i) Has your partner, close relatives or friends complained about your snoring?

[0062] (ii) How likely are you to doze off or fall asleep unintentionally during the day (e.g. while working, reading, or driving)?

[0063] (iii) Do you think it is easy for you to wake up early? and

[0064] (iv) Do you take naps during the day?

[0065] In another preferred embodiment, the questionnaire further includes questions selected from the following:

[0066] (v) How many hours do you sleep in a 24-hour period?

[0067] (vi) What kind of person do you think you are?

[0068] (vii) Do you have trouble falling asleep at night or wake up in the middle of the night?

[0069] or a combination thereof.

[0070] It should be understood that within the scope of the present invention, the above-mentioned technical features of the present invention and the technical features specifically described below (such as embodiments) can be combined with each other to form a new or preferred technical solution. Due to space limitations, they will not be described one by one here. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 It is shown that the healthy sleep scoring criteria of the present invention include Group A and Group B.

[0072] Figure 2 The figure shows the screening process of the research subjects in Example 1 of the present invention, which is training using the research subjects of UK Biobank.

[0073] Figure 3 It shows a schematic diagram of training using research subjects from UK Biobank and follow-up of research subjects in Example 1 of the present invention. DETAILED DESCRIPTION

[0074] After extensive and in-depth research and a large number of screenings, the inventors have developed for the first time an accurate and effective method and system for early auxiliary prediction and prevention of obstructive sleep apnea based on specific sleep behaviors. Specifically, the inventors have found that chronic obstructive sleep apnea can be predicted early based on specific sleep behaviors including: (A1) snoring; (A2) excessive daytime sleepiness; (A3) ease of getting up in the morning; (A4) daytime naps; (B1) sleep time; (B2) time type; and (B3) insomnia, etc., and prevention suggestions can be given based on the prediction results, thereby reducing the risk of OSA. The present invention was completed on this basis.

[0075] the term

[0076] As used herein, the terms "obstructive sleep apnea" and "OSA" are used interchangeably to refer to obstructive sleep apnea.

[0077] As used herein, the term "sleep time" refers to sleep time within 24 hours, ie, a day.

[0078] As used herein, the term "time type", also known as circadian rhythm type, refers to individual differences in what time a subject tends to fall asleep and wake up in a 24-hour period. Based on this pattern, subjects can be divided into three categories: morning type ("early bird type"), intermediate type, and evening type.

[0079] As used herein, the term "sleep scoring scale of the present invention" refers to Figure 1 The sleep scoring table shown is used to evaluate the sleep condition of the subject to be tested and to obtain the sleep scoring result value.

[0080] As used herein, the term "prediction" includes prediction of the onset of a disease for diagnostic or non-diagnostic purposes. The term includes early prediction and auxiliary diagnosis in the pre-onset period; it includes predictions for a group as well as predictions for an individual.

[0081] As used herein, the term "reference value" or "control reference value" refers to a value that is statistically correlated with a specific result when compared to the analysis result. In a preferred embodiment, the reference value is determined based on comparing the sleep behavior data of the subject to be tested with the sleep scoring table of the present invention and performing statistical analysis. Some such studies are shown in the Examples section of this article. However, studies from the literature and user experience of the methods disclosed herein can also be used to produce or adjust reference values. Reference values ​​can also be determined by taking into account circumstances and results that are particularly related to the ethnic group, medical history, genetics, age and other factors of the subject to be tested.

[0082] Sleep behavior

[0083] In the present invention, sleep behavior includes the following group of behaviors:

[0084] (A1) snoring; (A2) excessive daytime sleepiness; (A3) ease of getting up in the morning; (A4) daytime napping;

[0085] (B1) Sleep duration; (B2) Sleep time type; (B3) Insomnia.

[0086] In the present invention, sleep behavior can be obtained by filling out a questionnaire, such as Figure 1 As described above, reference may be made to it.

[0087] Sleep behaviors associated with obstructive sleep apnea (OSA)

[0088] Based on the sleep behavior of the present invention, early prediction of OSA can be performed. In the present invention, sleep behavior screening is performed according to the importance (β value) of each behavior in the model.

[0089] Based on the research of the present invention, Figure 1 As shown, the ranking of sleep characteristics associated with OSA is as follows:

[0090] Daytime sleepiness, snoring, ease of waking up early, daytime naps, insomnia, chronotype, sleep duration.

[0091] Therefore, the risk of developing OSA can be predicted by the following sleep behaviors: daytime sleepiness, snoring, easy early awakening, daytime napping, insomnia, chronotype, sleep duration, or a combination thereof.

[0092] In another preferred embodiment, the risk of developing OSA is predicted by the following sleep behaviors: daytime sleepiness, snoring, easy waking up early, daytime naps, or a combination thereof.

[0093] According to the study of the present invention, when a subject with a high risk of developing OSA improves his / her sleeping behavior according to the suggestions, a reduced risk of developing OSA can be obtained.

[0094] According to the research of the present invention, it was found that when there was no frequent daytime sleepiness, the risk of OSA was reduced by 65% ​​(p<0.0001); when there was no snoring, the risk of OSA was reduced by 64% (p<0.0001); when there were few daytime naps, the risk of OSA was reduced by 39% (p<0.0001); when it was relatively / very easy to get up early, the risk of OSA was reduced by 42% (p<0.0001); when sleeping 7-8 hours a day, the risk of OSA was reduced by 6% (p<0.0001); when being a morning type ("early bird"), the risk of OSA was reduced by 8% (p<0.0001); when never / rarely suffering from insomnia, the risk of OSA was reduced by 17% (p<0.0001).

[0095] Early obstructive sleep apnea (OSA) disease risk assessment system (equipment) based on sleep behavior

[0096] The present invention provides a system for assessing the risk of early obstructive sleep apnea (OSA) disease based on sleep behavior, the system comprising:

[0097] (a) an input module, the input module being configured to input sleep behavior data of a subject;

[0098] The sleep behaviors include the following sleep behavior combinations in group A:

[0099] (A1) snoring; (A2) excessive daytime sleepiness; (A3) ease of waking up in the morning; and (A4) daytime napping;

[0100] (b) an evaluation module, wherein the evaluation module is configured to perform a preliminary evaluation on the input sleep behavior data and obtain a sleep score, and perform a discrimination process; the sleep score is calculated by the following formula:

[0101]

[0102] Among them, S t Score sleep, W i is the weight coefficient of sleep behavior, S i To score sleep behavior;

[0103] The discrimination process includes comparing the sleep score with a control reference value to obtain a discrimination result; wherein, when the obtained sleep score is higher, the risk of the disease is lower; conversely, when the obtained sleep score is lower, the risk of the disease is higher; and

[0104] (d) An output module, configured to output an evaluation result value and a recommendation result.

[0105] In another preferred example, the system optionally further comprises a suggestion module, wherein the suggestion module is configured to provide the subject with suggestions for improving sleep behavior based on the sleep score, thereby reducing the risk of disease.

[0106] In another preferred example, the improvement suggestion is output from the output module.

[0107] In another preferred example, the improvement suggestion and the evaluation result value are output from the output module simultaneously or at different times.

[0108] In another preferred embodiment, the sleep behavior further includes any sleep behavior selected from the following group B, or a combination thereof:

[0109] (B1) Sleep duration; (B2) Sleep time type; (B3) Insomnia.

[0110] In another preferred embodiment, the weight coefficient W i Is a positive number.

[0111] In another preferred embodiment, the weight coefficient W i The value of is 1.

[0112] In another preferred embodiment, when the weight coefficient W i When the values ​​of are all 1, the calculation formula of the sleep score is:

[0113]

[0114] In another preferred embodiment, the discrimination process includes: Figure 1 Obtain sleep scores using the scoring table shown.

[0115] In another preferred embodiment, the sleep behavior data is obtained by means of a questionnaire, wherein the questionnaire includes the following questions:

[0116] (i) Has your partner, close relatives or friends complained about your snoring?

[0117] (ii) How likely are you to doze off or fall asleep unintentionally during the day (e.g. while working, reading, or driving)?

[0118] (iii) Do you think it is easy for you to wake up early? and

[0119] (iv) Do you take naps during the day?

[0120] In another preferred embodiment, the questionnaire further includes questions selected from the following:

[0121] (v) How many hours do you sleep in a 24-hour period?

[0122] (vi) What kind of person do you think you are?

[0123] (vii) Do you have trouble falling asleep at night or wake up in the middle of the night?

[0124] or a combination thereof.

[0125] In another preferred embodiment, the sleep score is processed as follows according to the answers to the questionnaire:

[0126] The answer to the question "Does your partner, close relatives, or friends complain about your snoring?" was "No", which was considered a low-risk sleep behavior and scored 1 point; other answers were scored 0 points;

[0127] For the question “How likely are you to accidentally doze off or fall asleep during the day (e.g., while working, reading, or driving)?”, the answer of never / rarely / sometimes was considered a low-risk sleep behavior and was scored 1 point; other answers were scored 0 points;

[0128] For the question “Do you think it is easy for you to wake up early?”, the answer of “easy / very easy” was judged as low-risk sleep behavior and scored 1 point; other answers were scored 0 points;

[0129] For the question “Do you take a nap during the day?”, the answer of never / rarely / sometimes was judged as low-risk sleep behavior and scored 1 point; other answers were scored 0 points;

[0130] For the question “How many hours do you sleep in a 24-hour period?”, the answer of 7-8 hours is considered as low-risk sleep behavior and is scored as 1 point; other answers are scored as 0 points;

[0131] For the question “What kind of person do you think you are?”, the answer of “morning type” was judged as low-risk sleep behavior and scored 1 point; other answers were scored 0 points;

[0132] For the question “Do you have trouble falling asleep at night or wake up in the middle of the night?”, the response of never / rarely / sometimes was considered low-risk sleep behavior and was scored 1 point; other responses were scored 0 points.

[0133] A sleep behavior with a score of 1 was determined as a low-risk sleep behavior, and a sleep behavior with a score of 0 was determined as a high-risk sleep behavior.

[0134] And according to an embodiment of the present invention, when all four sleep behaviors in group A were low-risk, the risk of OSA was reduced by 88% (p<0.0001) compared to when there was no or only one low-risk sleep behavior.

[0135] When any three sleep behaviors in group A were low-risk, the risk of OSA was reduced by 71% (p<0.0001) compared with none or only one low-risk sleep behavior.

[0136] When any two sleep behaviors in group A were low-risk, the risk of OSA was reduced by 48% (p<0.0001) compared with none or only one low-risk sleep behavior.

[0137] When patients showed any 6 or more low-risk sleep behaviors in group A and group B, the risk of OSA was reduced by 85% (p<0.0001) compared with patients with no or only one low-risk sleep behavior.

[0138] When any 4 or 5 sleep behaviors in group A and group B were low-risk, the risk of OSA was reduced by 79% (p<0.0001) compared with none or only one low-risk sleep behavior.

[0139] When any 2 or 3 sleep behaviors in groups A and B were low-risk, the risk of OSA was reduced by 58% (p<0.0001) compared with none or only one low-risk sleep behavior.

[0140] Suggested modules of the system of the present invention

[0141] The system (device) for assessing the risk of early obstructive sleep apnea (OSA) based on sleep behavior of the present invention preferably assesses 4 types of sleep behaviors, and assesses the risk of obstructive sleep apnea of ​​a subject after the assessment. The suggestion module of the system of the present invention can provide suggestions for improved sleep behavior based on the input content and the sleep score results.

[0142] If a subject scores one point and the score is no snoring in the low-risk sleep behavior, it can be suggested that the subject can improve the symptoms of daytime sleepiness, naps and difficulty waking up early. Improving these three can reduce the risk of disease by 88%, improving two of these three can reduce the risk of disease by 71%, and improving one of these three can reduce the risk of disease by 48%.

[0143] In addition, if a subject believes that a combination of several of the four sleep behaviors is difficult to improve, the system described in the invention can provide additional suggestions: if a subject scores one point, and the score is no snoring in the low-risk sleep behavior, the subject can be advised to improve the symptoms of daytime sleepiness, naps, and difficulty waking up early, and improving two of the three can reduce the risk of disease by 71%. If the subject believes that the three are difficult to improve, the system can provide 7 additional 6 sleep behaviors in the combination of Group A + Group B, and suggest that the subject flexibly choose to improve 1-2 of the 6 sleep behaviors to reduce the risk of disease by 58%, and flexibly choose to improve 3-4 sleep behaviors to reduce the risk of disease by 79%.

[0144] The main advantages of the present invention include:

[0145] (a) For the first time, a method for early prediction and prevention of obstructive sleep apnea is provided with high accuracy and extremely low knowledge requirements.

[0146] (b) The method of the present invention is not only simple but also non-invasive.

[0147] (c) The obstructive sleep apnea early prediction system or device of the present invention can not only be promoted and applied in clinical practice, but also facilitate the realization of remote intelligent auxiliary prediction.

[0148] (d) The obstructive sleep apnea early prediction system or device of the present invention uploads data through the input terminal, and does not need to wear the device for a long time, thereby greatly shortening the time of the subject and medical staff, and greatly improving the speed and breadth of sleep behavior data collection, and also helping to improve the popularity of disease prediction and prevention.

[0149] (e) After the system of the present invention predicts a disease, it can provide sleep behavior suggestions and facilitate improvement of the subject, thereby expanding disease prevention measures.

[0150] The present invention will be further described below in conjunction with specific examples. It should be understood that these examples are intended to illustrate the present invention only and are not intended to limit the scope of the present invention. The experimental methods in the following examples where specific conditions are not specified are usually performed under conventional conditions, such as those described in Sambrook et al., Molecular Cloning: A Laboratory Manual (New York: Cold Spring Harbor Laboratory Press, 1989), or under conditions recommended by the manufacturer. Unless otherwise indicated, percentages and parts are weight percentages and weight parts.

[0151] Materials and Methods

[0152] Study subjects

[0153] The subjects used in the present invention are from the UK Biobank database of more than 500,000 people. More than 500,000 participants aged 37-73 were recruited from 2006 to 2010, and their personal epidemiological and genetic data were recorded during follow-up visits.

[0154] Example 1 Eligible Population and Disease Determination

[0155] Finally, 355,133 people entered the sleep behavior assessment, 420,218 people entered the genetic risk assessment, and the study subjects were included in the following Figure 2 Determine the target population baseline time, OSA outcome occurrence time, and study termination time, and define the study termination time as September 30, 2021. Organize the patient's baseline characteristics, follow-up time, and outcome event information. The follow-up time is from the baseline time to the earliest date of OSA outcome occurrence or study termination date, such as Figure 3 The OSA outcome was defined as OSA according to the disease code G473 of the International Classification of Diseases-Tenth Revision (ICD-10).

[0156] Implement 2 covariate feature screening

[0157] Based on previous research literature and experience, and taking into account the accessibility of various variables in actual scenarios, gender, age, education, income, smoking, drinking, BMI, waist circumference, social deprivation (using the Townsend Deprivation Index (TDI)), resting metabolic equivalent intensity (MET), and common chronic medical diseases (diabetes, hypertension) were included as covariates.

[0158] TDI is a region-based socioeconomic status alternative provided in the UK Biobank study. It is an indicator used to measure the socioeconomic environment of a population and can be used as a substitute for education and income; MET is the working metabolic rate compared to the standard resting metabolic rate. 1 MET is considered to be the resting metabolic rate obtained while sitting quietly, 3 to 6 METs are general intensity activities, and greater than 6 METs are vigorous activities.

[0159] Other continuous covariates such as BMI and waist circumference were used to classify obesity and healthy weight in the population. For BMI, ≥30 kg / m 2The obese group was defined as the obese group, and the non-obese group was defined as the non-obese group. Men with a waist circumference of 102 cm or more and women with a waist circumference of 88 cm or more were grouped as the unhealthy weight group, and the non-healthy weight group was defined as the healthy weight group. Gender, age, BMI, TDI, MET, smoking, drinking and other information were recorded in the preliminary assessment, and chronic disease information was extracted from hospital admission records.

[0160] The chi-square test (categorical variables) and t-test (quantitative variables) were used to test the differences between the obstructive sleep apnea case group (case group) and the normal control group (control).

[0161] The baseline characteristics of the 355,133 subjects who had a first diagnosis of OSA or died after the initial assessment are shown in Table 1 .

[0162] Table 1

[0163]

[0164]

[0165]

[0166] Note: REF: reference group

[0167] Implement genetic risk assessment for 3 research subjects

[0168] The UK Biobank data is divided into a base dataset and a target dataset, and GWAS is performed using the base dataset (i.e., 50.0% of participants randomly selected from the study population). The Scalable and Accurate Implementation of Generalized mixed model (SAIGE) is used for analysis to obtain the SAIGE T test quantity, thereby obtaining a more accurate p value.

[0169] The standard clustering and thresholding (C+T) method was used to perform PRS in the target dataset. The parameters of this method were the cutoff of the correlation r2 and p-value threshold, and the parameters of r2 and p were 0.05 and 5.0×10 -6 . SNPs were selected using a window size of 1000Kb. The genetic risk of the participants was grouped into: high (<1st tertile), intermediate (1-2nd tertile), and low (>2nd tertile) genetic risk.

[0170] Implementation 4 Data Analysis

[0171] Cox proportional hazards model was used to calculate hazard ratio (HR) and 95% confidence interval (CI) to estimate the association between sleep behavior and healthy sleep score and OSA incidence. The model also adjusted for possible confounding factors. (1) Univariate regression model without adjustment; (2) Adjustment for gender and age; (3) Based on model 2, additional adjustment for smoking, drinking, waist circumference, TDI, BMI, MET, previous history of hypertension, and diabetes was added.

[0172] The inventors analyzed the correlation between 7 non-risk sleep behaviors and OSA, as shown in Table 2. The first four sleep behaviors related to OSA were used to form Group A sleep behaviors, and the remaining 3 sleep behaviors were grouped into Group B.

[0173] As shown in Table 3, the inventors used 4 sleep behaviors to perform healthy sleep scoring and analysis on OSA, and found that when all 4 sleep behaviors in group A were low-risk, the risk of OSA was reduced by 88% (p<0.0001) compared with no or only one low-risk sleep behavior; when any 3 sleep behaviors in group A were low-risk, the risk of OSA was reduced by 71% (p<0.0001) compared with no or only one low-risk sleep behavior; when any 2 sleep behaviors in group A were low-risk, the risk of OSA was reduced by 48% (p<0.0001) compared with no or only one low-risk sleep behavior.

[0174] Table 2. Hazard ratio HR (95% CI) of multivariate risk regression analysis of sleep behavior and OSA

[0175]

[0176] Note: *Model 1 single factor; Model 2: adjusted for gender and age; Model 3: Adjusted for sex, age, body mass index (BMI), resting metabolic rate (MET), Townsend deprivation index (TDI), smoking, drinking, diabetes, and history of hypertension.

[0177] Table 3 Hazard ratio HR (95% CI) of healthy sleep score and OSA in multivariate risk regression analysis of sleep behavior in group A

[0178]

[0179] Note: *Model 1 single factor; Model 2: adjusted for gender and age; Model 3: Adjusted for sex, age, body mass index (BMI), resting metabolic rate (MET), Townsend deprivation index (TDI), smoking, drinking, diabetes, and history of hypertension.

[0180] In order to balance the different weights of sleep behavior in group A related to OSA in different populations, the inventors used the following formula to calculate the sleep score:

[0181] S t =1.02*S1+1.06*S2+0.50*S3+0.55*S4

[0182] In the formula, the weight W i The value of is the absolute value of β obtained from the analysis of Model 3 in Table 2, that is, |β|.

[0183] The results are shown in Table 4. After weighting, there was no significant difference in the healthy sleep behavior score of Group A compared with the unweighted score (see Table 4), indicating that although different sleep behaviors have different weights, the protective effect against disease is greater when the score is higher.

[0184] Table 4 Weighted healthy sleep score of sleep behavior in group A and multivariate risk regression analysis of OSA hazard ratio HR (95% CI)

[0185]

[0186] Note: *Model 1 single factor; Model 2: adjusted for gender and age; Model 3: Adjusted for sex, age, body mass index (BMI), resting metabolic rate (MET), Townsend deprivation index (TDI), smoking, drinking, diabetes, and history of hypertension.

[0187] In addition, after the inventors performed health scoring on the 7 sleep behaviors of group A+B, they analyzed the correlation between different health scores and OSA and found that, as shown in Table 5, when the weight of each sleep behavior was not considered, when any 6 or more sleep behaviors in group A and group B were low-risk, the risk of OSA was reduced by 85% (p<0.0001) compared with no or only one low-risk sleep behavior; when any 4 or 5 sleep behaviors in group A and group B were low-risk, the risk of OSA was reduced by 79% (p<0.0001) compared with no or only one low-risk sleep behavior; when any 2 or 3 sleep behaviors in group A and group B were low-risk, the risk of OSA was reduced by 58% (p<0.0001) compared with no or only one low-risk sleep behavior.

[0188] Table 5. Hazard ratio HR (95% CI) of healthy sleep score and OSA in multivariate risk regression analysis of sleep behavior in group A+B

[0189]

[0190]

[0191] Note: *Model 1 single factor; Model 2: adjusted for gender and age; Model 3: Adjusted for sex, age, body mass index (BMI), resting metabolic rate (MET), Townsend deprivation index (TDI), smoking, drinking, diabetes, and history of hypertension.

[0192] The Cox proportional hazards model was used to calculate the hazard ratio (HR) and 95% confidence interval (CI) to estimate the association between healthy sleep scores and OSA incidence in different genetic risk groups. The model also corrected for possible confounding factors. (1) Univariate regression model without adjustment; (2) adjustment for sex and age; (3) based on model 2, additional adjustment for smoking, drinking, waist circumference, TDI, BMI, MET, previous history of hypertension, and diabetes.

[0193] The inventors used low genetic risk and a healthy sleep score equal to 2 points as a reference and found that, as shown in Table 6, in the high genetic risk group, when all four sleep behaviors in group A were low risk, the risk of OSA was reduced by 86% (p<0.0001) compared to no or only one low-risk sleep behavior; when any three sleep behaviors in group A were low risk, the risk of OSA was reduced by 66% (p<0.0001) compared to no or only one low-risk sleep behavior; when any two sleep behaviors in group A were low risk, the risk of OSA was reduced by 40% compared to no or only one low-risk sleep behavior.

[0194] (p<0.0001).

[0195] In the medium genetic risk group, when all four sleep behaviors in group A were low-risk, the risk of OSA was reduced by 88% compared with no or only one low-risk sleep behavior (p<0.0001); when any three sleep behaviors in group A were low-risk, the risk of OSA was reduced by 71% compared with no or only one low-risk sleep behavior (p<0.0001); when any two sleep behaviors in group A were low-risk, the risk of OSA was reduced by 54% compared with no or only one low-risk sleep behavior (p<0.0001); when any 1 or 0 sleep behaviors in group A were low-risk, the risk of OSA was not reduced compared with no or only one low-risk sleep behavior in the high genetic risk group (p<0.0001).

[0196] In the low genetic risk group, when all four sleep behaviors in group A were low risk, the risk of OSA was reduced by 87% compared with no or only one low-risk sleep behavior (p<0.0001); when any three sleep behaviors in group A were low risk, the risk of OSA was reduced by 67% compared with no or only one low-risk sleep behavior (p<0.0001); when any two sleep behaviors in group A were low risk, the risk of OSA was reduced by 43% compared with no or only one low-risk sleep behavior (p<0.0001); when any one or zero sleep behaviors in group A were low risk, the risk of OSA was not reduced compared with no or only one low-risk sleep behavior in the high genetic risk group (p<0.0001).

[0197] Table 6. Hazard ratio HR (95% CI) of healthy sleep score of sleep behavior in group A and multivariate risk regression analysis of OSA in subjects with different genetic backgrounds

[0198]

[0199] Note: *Model 1 single factor; Model 2: adjusted for gender and age; Model 3: Adjusted for sex, age, body mass index (BMI), resting metabolic rate (MET), Townsend deprivation index (TDI), smoking, drinking, diabetes, and history of hypertension.

[0200] In summary, the results of the present invention show that the healthy sleep score of the four sleep behavior combinations can predict the risk of OSA with or without considering genetic factors, and when the four sleep behaviors of Group A are judged as low risk, the risk of the disease can be significantly reduced.

[0201] In addition, after further analysis and comparison, any 4 low-risk sleep behaviors among the 7 sleep behaviors in group A and group B can also significantly reduce the risk of disease, which is conducive to expanding the improvement methods of sleep behavior. The present invention is suitable for popularization and application among the population to achieve early prediction of diseases and provide suggestions for improving sleep behaviors to reduce the risk of diseases.

[0202] discuss

[0203] The present invention evaluated the association between seven sleep behaviors (sleep duration, chronotype, insomnia, snoring, excessive daytime sleepiness, daytime naps, and ease of getting up in the morning) and the disease in a large prospective cohort of middle-aged and elderly men and women, and analyzed the association between the healthy sleep score integrated with 7 low-risk sleep behaviors defined by these 7 sleep behaviors and the incidence of OSA events. It is important to evaluate the combination of these sleep behaviors because these sleep-related factors are often interrelated. For example, frequent insomnia may lead to shortened sleep time and excessive daytime sleepiness, while participants with night time types usually have short sleep time. Although no studies have explored the mechanisms by which a combination of sleep behaviors may affect the risk of OSA disease; it can be considered that these sleep behaviors may work synergistically through multiple mechanisms to increase the risk of OSA disease.

[0204] The present invention is a large-sample prospective study that explores the impact of waking up early on the onset of OSA. The present invention provides a positive reference framework for sleep behavior by optimizing low-risk sleep behaviors defined by 4 types of sleep behaviors (no snoring, no excessive daytime sleepiness, no frequent daytime naps, and easy getting up in the morning), and is also valuable in identifying high-risk populations and promoting health management. From a public health perspective, a simple scoring algorithm makes epidemiological results easier to interpret and translate into practice, and is therefore more referenceable for the general population. For certain sleep behaviors that cannot be self-recognized or changed, such as snoring and insomnia, other self-controllable sleep behaviors such as controlling sleep time, time type, and reducing daytime sleepiness can be changed to improve healthy sleep scores and reduce the risk of OSA.

[0205] The advantages of the present inventors' implementation of this study are: first, by calculating a weighted healthy sleep score based on the contribution of each sleep behavior to the risk of disease in British white people, the contribution of each sleep behavior is taken into account to verify the robustness of the analysis results obtained using the score; second, by performing analysis by excluding possible covariates, it is helpful to eliminate confounding effects; third, by analyzing multiple sleep behaviors, the risk factors for OSA in the population are identified, and more good sleep behaviors are found, providing more ideas for disease prevention and prediction.

[0206] All documents mentioned in the present invention are cited as references in this application, just as each document is cited as reference individually. In addition, it should be understood that after reading the above teachings of the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the claims attached to this application.

Claims

1. A system for assessing the risk of early obstructive sleep apnea (OSA) based on sleep behavior, characterized in that: The system comprises: (a) an input module, the input module being configured to input sleep behavior data of a subject; The sleep behaviors include the following group A sleep behaviors: (A1) snoring; (A2) excessive daytime sleepiness; (A3) ease of waking up in the morning; and (A4) daytime napping; (b) an evaluation module, wherein the evaluation module is configured to perform a preliminary evaluation on the input sleep behavior data and obtain a sleep score, and perform a discrimination process; the sleep score is calculated by the following formula: Among them, S t Score sleep, W i is the weight coefficient of sleep behavior, S i To score sleep behavior; The discrimination process includes comparing the sleep score with a control reference value to obtain a discrimination result; wherein, when the obtained sleep score is higher, the risk of the disease is lower; conversely, when the obtained sleep score is lower, the risk of the disease is higher; and (d) An output module, configured to output an evaluation result value.

2. The system according to claim 1, characterized in that The system optionally further comprises a suggestion module, which is configured to provide the subject with suggestions for improving sleep behavior based on the sleep score, thereby reducing the risk of disease.

3. The system according to claim 1, characterized in that The sleep behavior also includes any sleep behavior selected from the following group B, or a combination thereof: (B1) Sleep duration; (B2) Sleep time type; (B3) Insomnia.

4. The system according to claim 1, characterized in that The object is a person.

5. The system according to claim 1, wherein: The sleep behavior data is obtained by means of a questionnaire, wherein the questionnaire includes the following questions: (i) Has your partner, close relatives or friends complained about your snoring? (ii) How likely is it that you will doze off or fall asleep unintentionally during the day? (iii) Do you think it is easy for you to wake up early? and (iv) Do you take naps during the day? 6. The system according to claim 5, characterized in that The questionnaire also included questions selected from the following: (v) How many hours do you sleep in a 24-hour period? (vi) What kind of person do you think you are? (vii) Do you have trouble falling asleep at night or wake up in the middle of the night? or a combination thereof.

7. The system according to claim 1, characterized in that The evaluation system also includes a processor and a memory.

8. The system of claim 1, wherein: The output module includes a display, a printer, a tablet computer (PAD), a smart phone, or a combination thereof.

9. A method for assessing the risk of early obstructive sleep apnea based on sleep behavior, characterized in that: The method comprises: (a) providing data, wherein the data includes sleep behavior data of a subject to be tested; The sleep behavior data is from a sleep behavior combination including the following group A: (A1) snoring; (A2) excessive daytime sleepiness; (A3) ease of waking up in the morning; and (A4) daytime napping; and (b) Analysis and evaluation: evaluating the sleep behavior data according to the sleep scoring table of the present invention to obtain a sleep score, and comparing the sleep score with a control reference value; wherein, a higher sleep score indicates a lower risk of disease; conversely, a lower sleep score indicates a higher risk of disease. (c) providing suggestions for improving sleep behavior to the subject based on the sleep score, thereby reducing the risk of disease.

10. The method according to claim 9, characterized in that The sleep behavior also includes any sleep behavior selected from the following group B, or a combination thereof: (B1) Sleep duration; (B2) Sleep time type; (B3) Insomnia.

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