Method for distinguishing of particulate matter exposure and predicting of pregnancy-related complications from particulate matter

Abstract

The present invention relates to a technology for classifying fine dust exposure and predicting pregnancy-related complications caused by fine dust exposure. Since the classification of fine dust exposure and prediction of pregnancy-related complications using the method of the present invention are based on biochemical markers, it has the advantage of enabling the formulation of appropriate prevention or intervention measures for chronic diseases and respiratory diseases in children caused by fine dust exposure, as well as rapid and highly accurate prediction of pregnancy-related complications such as low birth weight, premature birth, and gestational diabetes, which are difficult to identify until birth or onset.

Description

Technology Field

[0001] The present invention relates to a technology for classifying fine dust exposure and a method for predicting pregnancy-related complications caused by fine dust exposure. Specifically, the technology relates to a method for providing information to classify pregnant women exposed to fine dust or to predict pregnancy-related complications caused by fine dust, a composition and kit for classifying fine dust exposure, and a composition and kit for predicting the onset of pregnancy-related complications caused by fine dust exposure. Background Technology

[0002] Fine dust is airborne particulate matter composed of various complex components. It is mostly generated from automobile exhaust and dust produced during road travel, and its health effects are determined by particle size and chemical composition. Exposure to fine dust is associated with the development of respiratory and cardiovascular diseases and is reported to increase mortality rates. In particular, small dust particles smaller than 10 µm pose a significant threat to health because they can enter the lungs and bloodstream.

[0003] In a study targeting elderly residents of Illinois, USA, the fine dust concentration was 10 µg / m³ 3 It was found that when it increased, the mortality rate increased 2.5 times for people with myocardial infarction and 2.0 times for people with diabetes, and a study in Korea showed that patients with heart failure had a mortality risk about 2.5 times higher.

[0004] Furthermore, mortality rates from respiratory diseases increased in children exposed to fine dust, and exposure of the respiratory system to such hazardous substances during the period of lung development continues to affect lung function into adulthood. A follow-up study of students in California revealed that children exposed to these pollutants for a long period were 4.9 times more likely to experience a decline in lung function as adults. Additionally, this decline in lung function is known to increase the risk of developing cardiovascular disease and diabetes.

[0005] In addition, fine dust 2.5 (PM), one of the air pollutants 2.5 , particles with an aerodynamic diameter ≤2.5 μm) have been reported to be associated with various unfavorable pregnancy outcomes. In particular, PM 2.5 Although associations have been reported between low birth weight, birth of children with low birth weight relative to gestational age, and prematurity, most studies have only assessed exposure to fine dust based on outdoor air quality measurements.

[0006] Low birth weight (LBW, birth weight <2,500g, regardless of gestational age), small for gestational age (SGA, birth weight below the 10th percentile for gestational age), and preterm birth (PTB, delivery at <37 weeks of gestation) are pregnancy complications that directly affect the prognosis of newborns. The incidence of low birth weight varies by country but ranges from 6% to 25%, and it is known to be associated with short- and long-term complications. Additionally, the prevalence of small for gestational age is known to be approximately twice that of low birth weight.

[0007] In the past, the majority of research was conducted based on outdoor air quality measurements from outdoor monitoring stations; however, with 80–90% of the population currently residing indoors and the importance of indoor air quality being increasingly emphasized, the significance of measuring indoor fine dust concentrations and managing indoor air quality is growing. PM ​​[is linked to] the development of various pregnancy complications, including fetal growth issues. 2.5 Understanding of the pathogenesis of the disease remains insufficient, and research on biomarkers developed for monitoring exposure is also lacking. Although there have been attempts to explain fetal growth based on DNA methylation or telomere length in umbilical cord blood, there is a problem in that umbilical cord blood is not suitable for in vivo monitoring because it is difficult to sample during pregnancy.

[0008] As such, exposure to fine dust is closely linked to chronic diseases, respiratory diseases in children, and pregnancy-related complications; therefore, there is a need to develop methods to identify pregnant women exposed to fine dust in advance to devise appropriate prevention or intervention measures, or to predict pregnancy-related complications caused by fine dust exposure with high accuracy without affecting the fetus during pregnancy. The problem to be solved

[0009] The object of the present invention is to provide a method for providing information to distinguish a pregnant woman exposed to fine dust, comprising the step of (a) quantifying the level of one or more metabolites selected from the group consisting of arabinose, ribose, and xylose from a sample of a subject suspected of being exposed to fine dust.

[0010] Another objective of the present invention is to provide a composition for classifying fine dust exposure or a composition for predicting the onset of pregnancy-related complications caused by fine dust exposure, comprising a preparation for quantifying one or more metabolites selected from the group consisting of arabinose, ribose, and xylose.

[0011] Another objective of the present invention is to provide a kit for classifying fine dust exposure or a kit for predicting the onset of pregnancy-related complications, comprising a composition including a preparation for quantifying one or more metabolites selected from the group consisting of arabinose, ribose, and xylose. means of solving the problem

[0012] In the following, redundant details will be omitted to prevent clutter. In other words, the content of the invention is not limited solely to the following, and should be interpreted in accordance with the overall context of the invention.

[0013] The present invention aims to provide a method for providing information to distinguish pregnant women exposed to fine dust or to predict pregnancy-related complications caused by fine dust, a composition and kit for distinguishing fine dust exposure, and a composition and kit for predicting the onset of pregnancy-related complications caused by fine dust exposure.

[0014] As one embodiment for achieving the above objective, a method for providing information to distinguish a pregnant woman exposed to fine dust is provided, comprising the step of (a) quantifying the level of one or more metabolites selected from the group consisting of arabinose, ribose, and xylose from a sample of a subject suspected of being exposed to fine dust.

[0015] As a specific embodiment thereof, a method for providing information to distinguish a pregnant woman exposed to fine dust comprises: (a) a step of quantifying the level of one or more metabolites selected from a group consisting of arabinose, ribose, and xylose from a sample of a subject suspected of exposure to fine dust; and (b) a step of determining that the subject is exposed to fine dust if, as a result of quantifying the level of the metabolites, the level increases when compared to one or more selected from a group consisting of arabinose, ribose, and xylose of a corresponding normal control.

[0016] More specifically, it may further include the step of (b) predicting that there is a risk of developing pregnancy-related complications due to fine dust exposure if the level of the metabolite is increased when compared to one or more selected from a group consisting of arabinose, ribose, and xylose of a corresponding normal control group as a result of quantifying the level of the metabolite.

[0017] In other words, it can primarily provide a classification of pregnant women based on fine dust exposure and additionally provide a prediction of the risk of developing pregnancy-related complications due to fine dust exposure.

[0019] The above "fine dust" may preferably be ultrafine dust or extremely fine dust (PM 2.5) with a particle size of 2.5㎛ or less.

[0020] The statement above, "comparing levels with metabolites (arabinose, ribose, xylose) of a corresponding normal control group," means comparing the metabolic level of a specific metabolite of the individual being predicted.

[0021] The above "level increase" may be the quantification of the quantity, level, distribution, distribution ratio, or concentration of metabolites (arabinose, ribose, xylose) within a biological sample.

[0022] Preferably, the subject may be a mammal. In some embodiments, the subject may be a human.

[0023] The term "sample of a subject" as used in this invention refers to any sample obtained from an individual in which the metabolite of this invention can be detected.

[0024] According to a preferred embodiment of the present invention, the sample of the subject is selected from the group consisting of blood, saliva, feces, vaginal fluid, and urine separated from the subject, and can be prepared by processing by a method commonly used in the art of the present invention.

[0025] The above normal control group may refer to biological samples recovered from healthy people (especially pregnant women) or mammals that have not been exposed to fine dust.

[0026] The above "step of quantifying metabolites" may be performed using one or more methods selected from the group consisting of nuclear magnetic resonance spectroscopy, chromatography, ultraviolet spectroscopy, infrared spectroscopy, ELISA (enzyme-linked immunosorbent assay), and mass spectrometry. As such methods are known, their description is omitted.

[0027] According to one embodiment of the present invention, the metabolite according to the present invention showed a significant increase in the high-exposure group to fine dust, and it was confirmed that the metabolite distinguishes the fine dust exposure group with high accuracy. Accordingly, by distinguishing pregnant women exposed to fine dust through the method according to one example of the present invention, the risk caused by fine dust exposure can be identified in advance, and appropriate prevention or intervention measures can be devised.

[0028] In addition, the present invention provides, in a specific embodiment, a method for providing information for predicting the occurrence of pregnancy-related complications due to fine dust exposure, comprising: (a) a step of quantifying the level of one or more metabolites selected from the group consisting of arabinose, ribose, and xylose from a sample of a subject suspected of fine dust exposure; and (b) a step of predicting that there is a risk of developing pregnancy-related complications due to fine dust exposure if the level of the metabolites quantified is increased when compared to one or more selected from the group consisting of arabinose, ribose, and xylose of a corresponding normal control group.

[0029] Preferably, the subject may be a mammal. In some embodiments, the subject may be a human, more specifically a pregnant woman suspected of being exposed to fine dust.

[0030] According to a preferred embodiment of the present invention, the sample of the subject is a biological sample isolated from a pregnant woman, and is not particularly limited thereto, but preferably is any one selected from blood, saliva, feces, vaginal fluid, and urine isolated from the subject, and can be prepared by processing by a method commonly used in the art of the present invention.

[0031] The above normal control group may refer to a group with low estimated concentrations of fine dust exposure during pregnancy. More specifically, it may be an experimental group randomly selected and quantified from a group with low individual estimated concentrations of fine dust exposure during pregnancy. Additionally, depending on the case, it may be a biological sample recovered from a mother who delivered a baby weighing 2.5 kg or more at 37 weeks or more without developing pregnancy-related complications, or a biological sample isolated from a pregnant woman who does not exhibit symptoms of pregnancy-related complications.

[0032] The above term "pregnancy-related complications" may be any one selected from low birth weight, premature birth, and gestational diabetes.

[0033] The terms "low birth weight," "low birth weight," and "LBW" in this invention have the same meaning and refer to the birth of a fetus with a birth weight of less than 2.5 kg, regardless of gestational age. Among these, a case where the weight is less than 1.5 kg is referred to as an extremely low birth weight infant. Since low birth weight infants are often premature infants who are small for gestational age due to intrauterine growth retardation, and all physical and metabolic functions such as thermoregulation, spontaneous respiration, circulation, and digestion are underdeveloped, and resistance to infection is also weak, it is very important to predict the birth of a low birth weight infant.

[0034] The terms "premature birth," "preterm birth," "PTB," etc. of the present invention have the same meaning and refer to the birth of a fetus before 37 weeks. Preferably, in the present invention, premature birth may be a premature birth before 34 weeks with a particularly high risk.

[0035] The gestational age at which complications in premature infants become similar to those in full-term newborns is after 34 weeks of gestation. Since lung maturation is completed by 34 weeks, newborns born before 34 weeks are at a higher risk of developing fetal respiratory distress syndrome and require medical assistance. Among premature infants born before 34 weeks, those weighing less than 1.5 kg are placed in an incubator; they are removed from the incubator once they reach 34 weeks of gestation or later, weigh 1.8 kg or more, and are able to regulate their own body temperature. For this reason, predicting premature birth before 34 weeks of gestation is very important.

[0036] The terms "gestational diabetes," "gestational diabetes," and "GDM" of the present invention have the same meaning and refer to cases where diabetes develops in a person who did not originally have diabetes after 20 weeks of pregnancy. Normally, when the pregnant woman's insulin function decreases due to hormones secreted by the fetus, the pancreas increases insulin secretion to overcome this, but if the insulin secretion is insufficient at this time, gestational diabetes develops.

[0037] High caution is required when gestational diabetes is present, as it can affect both the fetus and the mother and carries a high risk of developing type 2 diabetes later in life. Furthermore, gestational diabetes increases the probability of birth defects, macrosomia, neonatal hypoglycemia, and neonatal respiratory distress syndrome. Additionally, the likelihood of childhood obesity and metabolic syndrome after birth is approximately twice as high compared to normal mothers, and there is a high probability of developing gestational hypertension and premature birth; therefore, predicting gestational diabetes is very important.

[0038] According to one embodiment of the present invention, the metabolite according to the present invention showed a significant increase in level compared to other sugar metabolites, particularly in a high-exposure group to fine dust, and it was confirmed that the metabolite is associated with low birth weight, premature birth, and gestational diabetes caused by exposure to fine dust. Accordingly, the method according to one example of the present invention, that is, the metabolite according to the present invention, can be used as a predictive indicator for premature birth of less than 37 weeks, low birth weight of less than 2.5 kg, and gestational diabetes.

[0039] In the present invention, the term "prediction of pregnancy-related complications caused by fine dust exposure" refers to determining whether a pregnant woman exposed to fine dust is likely to develop low birth weight, premature birth, or gestational diabetes, whether the likelihood of developing said disease is relatively high, or whether she is likely to show signs of said disease.

[0040] The present invention can be used to delay or prevent the onset of the disease in pregnant women at high risk of developing the aforementioned pregnancy-related complications through special and appropriate management. Furthermore, the present invention can be used clinically for treatment decisions by predicting the likelihood of the disease developing early and selecting the most appropriate treatment method.

[0041] Accordingly, through the detection method described above, the present invention can determine the amount of metabolites (arabinose, ribose, xylose) of a normal control group and the amount of metabolites (arabinose, ribose, xylose) of a pregnant woman exposed to fine dust, and can predict pregnancy-related complications caused by fine dust exposure by comparing the levels with the normal control group.

[0042] As described above, the present invention provides, as one embodiment for achieving the above objective, a composition for classifying fine dust exposure in pregnant women or a composition for predicting the onset of pregnancy-related complications caused by fine dust exposure, comprising a preparation for quantifying one or more metabolites selected from the group consisting of arabinose, ribose, and xylose.

[0043] The above preparation may quantify the quantity, level, distribution, distribution ratio, or concentration of metabolites in a biological sample.

[0044] The composition according to the present invention may be configured to further include any of the aforementioned markers based on any one or more of the aforementioned markers. That is, in the case of the markers, they may also be utilized in combinations of two or more.

[0045] According to one embodiment of the present invention, sugar metabolites are extracted from the urine of pregnant women and, compared with those of normal people, three markers that specifically increase in level in a high-exposure group to fine dust are identified, and the above is used to predict the disease.

[0046] More specifically, the present invention confirmed that the levels of arabinose, ribose, and xylose increase in pregnant women who develop pregnancy complications (low birth weight, premature birth, and gestational diabetes) due to exposure to fine dust. Accordingly, by utilizing the mechanism described above identified in the present invention, it is possible to classify pregnant women exposed to fine dust and predict pregnancy-related complications caused by fine dust exposure.

[0047] As described above, the present invention provides, as one embodiment for achieving the above objective, a kit for classifying fine dust exposure in pregnant women or a kit for predicting the onset of pregnancy-related complications, comprising a composition including a preparation for quantifying one or more metabolites selected from the group consisting of arabinose, ribose, and xylose.

[0048] The above kit may be any one selected from the group consisting of a nuclear magnetic resonance spectroscopy device, a chromatography device, an ultraviolet spectroscopy device, an infrared spectroscopy device, an ELISA (enzyme-linked immunosorbent assay) device, and a mass spectrometry device.

[0049] Since the kit of the present invention comprises the composition described above, redundant details are omitted to avoid excessive complexity in this specification. Effects of the invention

[0050] Since the classification of fine dust exposure and the prediction of pregnancy-related complications caused by fine dust exposure using the method of the present invention are based on biochemical markers, there is an advantage in that it is possible to measure the risk of fine dust exposure in advance and devise prevention or intervention measures, and in particular, to make rapid and highly accurate predictions of pregnancy-related complications such as low birth weight, premature birth, and gestational diabetes, which are difficult to identify until birth or onset. Brief explanation of the drawing

[0051] Figure 1 is PM 2.5 This is the result confirming the association with pregnancy complications according to exposure concentration. Figure 2 is PM 2.5 This is the result of a VIP scores plot, listing metabolites that showed significant differences based on exposure levels from top to bottom in order of greatest impact. Figure 3 is PM 2.5This is the result of a fold change plot for metabolites that showed significant differences depending on the level of exposure. Figure 4 shows the ROC curve results for ribose, arabinose, and xylose. Specific details for implementing the invention

[0052] Preferred embodiments are presented below to aid in understanding the present invention. However, the following embodiments are provided merely to facilitate a better understanding of the invention, and the scope of the invention is not limited by the following embodiments.

[0053] Examples 1. Cohort Investigation of the effects of fine dust on mothers and fetuses through the establishment of research

[0054] go. Materials and Methods

[0055] (1) Cohort Research Design of I

[0056] The study included 1,880 pregnant women who gave birth to live infants at Ewha Womans University Mokdong Hospital between 2010 and 2015 (EUMC 2020-07-043). For the study, information on maternal age, body mass index (BMI), gestational age at birth (GAB), neonatal sex, birth weight and height, APGAR (Appearance, Pulse, Grimacing, Activity, and Respiration) scores, and residence at the time of delivery was collected. The Air Korea database is used by the Ministry of Environment for sulfur dioxide, PM 10 Fine dust 2.5 (PM ) including hourly cumulative air pollution monitoring data for components such as carbon monoxide, nitrogen dioxide, and ozone 2.5) Used for exposure assessment. PM 2.5 Exposure levels were measured using the CMAQ (Community Multiscale Air Quality) modeling system. PM obtained from the CMAQ modeling data. 2.5was estimated using meteorological studies and forecasting models consisting of three overlapping meteorological data sources at 3, 9, and 27 km during a specific period. PM during pregnancy 2.5 The level of exposure was determined based on the residential address (city, county, district). Daily PM for each address 2.5 Concentrations were matched to each individual according to the delivery date. Groups were stratified according to birth weight and height, and the students' t A test was performed. Low birth weight infants were defined as having a birth weight of <2,500g, and low birth height (LBH) was defined as having a birth height of <46.3cm for boys and <45.6cm for girls.

[0057] (2) Cohort Research Design of II

[0058] For the Multicenter Prospective Cohort Study (APPO) on air pollution in pregnant women, over 1,200 participants were recruited from seven university hospitals nationwide from January 2021 to December 2023. The selected hospitals were located in the metropolitan area, industrial complexes, or mountainous regions, and participants were pregnant women without underlying medical conditions prior to 28 weeks of gestation. In this study, 333 women who had given birth were selected as participants. For the experiment, data on baseline demographics and health-related characteristics, including age, BMI, socioeconomic status, and obstetric history, were collected. Additionally, urine samples were collected during the second trimester of pregnancy, and pregnancy outcomes were evaluated after delivery.

[0059] This study was approved by the Ethics Research Committee of Ewha Womans University Mokdong Hospital (EUMC 2021-04-032), Yonsei University Severance Hospital (4-2021-0414), Kangwon National University Hospital (KNUH-B-2021-04-012-008), Keimyung University Dongsan Hospital (2021-04-073), Korea University Guro Hospital (2021GR0233), Ewha Womans University Seoul Hospital (2021-04-022), and Ulsan National University Hospital (2022-04-020). All participants provided written consent.

[0060] (3) Fine dust exposure assessment

[0061] Indoor PM 2.5 Concentrations were measured by placing a fine dust meter in the living room of the pregnant woman's home at her breathing level. The AirGuard K (Kweather, Co., Korea) device was used for measurements using the sensor method. Indoor PM 2.5 It was measured at 1-minute intervals. Measured indoor PM 2.5 Data was stored on an indoor air quality monitoring platform (IAQ station) using long-term evolution. Measurements were performed for at least one week per trimester of pregnancy, and the measured concentration values ​​were recorded in real time.

[0062] Outdoor PM 2.5 Concentrations were collected from nearby urban air measurement networks based on the residential addresses of the study participants. The urban air monitoring station data used in this study was obtained from the Air Korea database of the Ministry of Environment of Korea (https: / / www.airkorea.or.kr / web).

[0063] (4) Urine sample collection

[0064] 15 ml of urine was collected. Blood samples were collected along with regular follow-up observations and stored in freezing tubes. On the day of collection, the samples were transported to the institution (Seegene Medical Foundation, Seoul, Korea) and refrigerated at -80°C to prevent deterioration.

[0065] (5) Statistical analysis

[0066] Participants were <10 µg / m³, which is the World Health Organization (WHO) annual limit standard concentration (2005 Air Quality Guidelines), depending on their PM 2.5 exposure levels. 3 (Low PM 2.5 Concentration exposure group) and ≥10 µg / m² 3 (High PM 2.5 Divided into concentration exposure groups.

[0067] Categorical variables were analyzed using the chi-square and Fisher's exact test. Continuous variables were compared using the Mann-Whitney U test or Student's t-test. Statistical significance P It was defined as <0.05. All statistical analyses were performed using the statistical package for social sciences (version 20.0) (Chicago, Illinois, USA).

[0068] B. Results

[0069] (1) Cohort I. Fine dust (PM) on a study group 2.5 Analysis of the association between exposure and pregnancy complications

[0070] Fine dust (PM 2.5 As a result of examining the association between exposure and pregnancy complications, as shown in Table 1 below, out of 1,880 newborns, 1,569 were born with normal birth weight (NBW) and 311 were born with low birth weight (LBW). The two groups were compared based on gestational age at birth (GAB), birth weight, height, and APGAR score, and preterm birth (PTB) as one of the pregnancy complications ( P Significant differences were observed in <0.01, but there were no significant differences in maternal age, maternal pre-pregnancy BMI (pre-BMI), or newborn sex.

[0071] In addition, as shown in Table 2 below, the fine dust concentration to which pregnant women who gave birth to low-birth-weight infants were exposed during the second trimester was 27.21 μg / m³ 326.23 μg / m² in the group of pregnant women who gave birth to normal-weight infants 3 It appeared significantly higher.

[0072] Characteristics of the Cohort I Study Group characteristic NBW (n=1569) LBW (n=311) p-value Mother's age (years) 33.20 4.16 32.79 4.47 0.12 Pre- BMI (kg / m 2 ) 21.46 3.59 21.52 3.65 0.92 GAB Co., Ltd. 39.02 1.35 33.30 3.49 <0.01* Newborn Gender (Male) 791 82.14% 172 17.86% 0.12 Newborn Gender (Female) 780 84.78% 140 15.22% birth weight (g) 3266.44 394.83 1824.04 497.55 <0.01* Birth height (cm) 49.76 1.88 42.08 4.06 <0.01* APGAR 1 minute 9.30 1.12 6.76 2.54 <0.01* APGAR 5 minutes 9.88 0.55 8.27 2.24 <0.01* PTB 81 24.40% 251 75.60% <0.01*

[0073] Categorical variables were expressed as frequencies (percentages) and analyzed using the chi-square test. Continuous variables were expressed as mean ± standard deviation, and t-test Comparisons were made using (right column of NBW, LBW).* BMI, Body Mass Index; GAB, Gestational Age at Birth; APGAR, Appearance, Pulse, Glare, Activity, Respiration; PTB, Prematurity. * P < 0.05 is considered statistically significant.

[0074] Fine dust (PM) by gestational age in normal-weight and low-weight infants 2.5 ) Exposure amount (μg / m² 3 Comparison of ) Exposure period NBW (n=1567) LBW (n=313) p-value Total pregnancy period 27.17 ± 4.30 4.67± 27.40 0.43 Early pregnancy (1 st trimester) 28.75 ± 7.63 28.04 ± 7.70 0.15 Mid-pregnancy (2 nd trimester) 26.23 ± 6.78 27.21 ± 6.53 0.02* Late pregnancy (3 rd trimester) 26.40 ± 6.43 27.04 ± 8.56 0.23

[0075] Continuous variables are expressed as mean ± standard deviation, and t-test Compare using. * P < 0.05 is considered statistically significant. Meanwhile, as shown in Table 3 below, when newborns were divided into the full-term birth group (>37 weeks) and the PTB group, when divided into the preterm birth group (PTB, <37 weeks), PM for low birth weight infants (LBW) 2.5 The exposure concentration was 27.29 μg / m² 3 The concentration of normal-weight newborns (NBW) (25.89 μg / m²) 3 It was found to be significantly higher than ) P =0.02). Also, in the preterm infant (PTB) group, PM 2.5 The normal birth weight (PNBW) and low birth weight (PLBW) groups according to exposure concentration showed significant differences between the early and late stages, respectively (P = 0.05, P = 0.04).

[0076] Meanwhile, as shown in Table 4 below, for models adjusted for maternal age, fertility history, maternal pre-pregnancy body mass index, preeclampsia, and gestational age, PM 2.5 Pregnant women exposed to PM 2.5 The risk of low birth weight (LBW) was significantly higher than in pregnant women not exposed to it, and the adjusted odds ratio (OR) was 1.06 (95% confidence interval [CI]: 1.01-1.10).

[0077] Fine dust (PM) by gestational age according to normal birth weight (NBW) and low birth weight (LBW) infants among premature infants 2.5 ) Exposure amount (μg / m² 3 Comparison of ) Exposure period NBW (n=84) LBW (n=260) p-value Total pregnancy period 25.89 ± 4.15 27.29 ± 4.78 0.02 Early pregnancy (1 st trimester) 25.81 ± 6.63 27.58 ± 7.57 0.05 Mid-pregnancy (2 nd trimester) 25.57 ± 6.26 27.25 ± 6.58 0.04 Late pregnancy (3 rd trimester) 26.69 ± 6.15 27.23 ± 8.98 0.55

[0078] Continuous variables are expressed as mean ± standard deviation, and t-test Compare using. * P < 0.05 is considered statistically significant.

[0079] Fine dust (PM) by pregnancy stage 2.5 ) Exposure amount (μg / m² 3 Comparison of Associations Between ) and Low Birth Weight (LBW): Logistic Regression Analysis Risk of LBW by PM 2.5 Exposure (1 μg / m 3 Risk of low birth weight due to ) OR 95%, CI p-value Total pregnancy period 1.06 1.01, 1.10 0.02* Early pregnancy (1 st trimester) 1.02 1.00, 1.05 0.08 Mid-pregnancy (2 nd trimester) 1.03 1.00, 1.06 0.04* Late pregnancy (3 rd trimester) 1.00 0.98, 1.03 0.80

[0080] OR, Odds ratio; Cl, ​​Confidence interval * P < 0.05 is considered statistically significant.

[0081] (2) Cohort II. Fine dust (PM) on a study group 2.5 Association between exposure concentration and pregnancy complications

[0082] As shown in Table 5, the average birth height is PM 2.5 It was found to be 48.9 cm in the high-concentration exposure group, PM 2.5 It was found to be significantly lower compared to the low-concentration exposure group (49.5 cm). P =0.04). Also, PM2.5 In the high-concentration exposure group, preterm birth (PTB) increased significantly from 4.2% to 13.2%, and early birth relative to gestational age (SGA) increased significantly from 0.6% to 5.1%. P <0.01, P =0.01).

[0083] In addition, as shown in Fig. 1, high concentrations of PM 2.5 It was found that the greater the exposure, the higher the risk of admission to the Neonatal Intensive Care Unit (NICU) due to pregnancy complications such as premature birth, gestational diabetes, and low birth weight.

[0084] Fine dust (PM 2.5 Association between exposure and pregnancy complications PM 2.5 low concentration Exposure group (n=191) PM 2.5 High concentration Exposure group (n=115) p-value GAB Co., Ltd. 38.22 ± 1.59 37.96 ± 1.95 0.22 Delivery mode 0.86 ND 61 36.5% 51 37.5% CS 106 63.5% 85 62.5% birth weight (g) 3150.7 ± 391.6 3112.0 ± 522.5 0.50 Birth height (cm) 49.5 ± 2.2 48.9 ± 2.8 0.04* APGAR 1 minute 8.45 ± 1.22 8.36 0.61 APGAR 5 minutes 9.43 ± 0.84 9.21 ± 1.37 0.13 LBW 4 5.1% 4 6.9% 0.47 NBW 5 2.6% 8 7.0% 0.07 PTB 8 4.2% 15 13.2% <0.01* SGA 1 0.6% 7 5.1% 0.01* NICU admission 20 12.0% 23 16.9% 0.32

[0085] Categorical variables were expressed as frequencies (percentages) and analyzed using the chi-square and Fisher's exact test. Continuous variables were expressed as mean ± standard deviation (SD) or median (interquartile range). t-test Or compared using the Mann-Whitney U test.* GAB, gestational age at birth; ND, normal delivery; CS, cesarean section; APGAR, appearance, pulse, grimace, activity, and respiration; LBW, low birth weight; LBH, short stature at birth; PTB, preterm birth; SGA, delivery early relative to gestational age; NICU, neonatal intensive care unit. *P < 0.05 is considered statistically significant.

[0086] Examples 2. For predicting pregnancy complications caused by fine dust exposure biomarkers Selection

[0087] go. Experimental Subjects, Materials, and Experimental Methods

[0088] (1) Sample pretreatment method

[0089] The subjects of the experiment were pregnant women identical to the subjects of Cohort II.

[0090] (2) Sample pretreatment method

[0091] 50 µl of urine sample was placed in a microtube, and 100 µl of a urease solution dissolved in water at a concentration of 1 mg / ml was added. Subsequently, to decompose urea, which is saturated and acts as an interfering substance for analysis, ultrasonic grinding was performed using a water bath ultrasonic grinder at room temperature for 1 hour, and after the ultrasonic grinding was finished, 150 µl of methanol was added.

[0092] Subsequently, liquid-liquid extraction (LLE) was performed using chloroform. Specifically, 150 µl of chloroform was added, and the solution was vortexed for about 1 minute to ensure sufficient mixing. Afterward, centrifugation was performed at room temperature at a force of 13,000 × g for 5 minutes to separate the layers. When the layers were separated through centrifugation into a lower chloroform layer (non-polar metabolite layer), a middle layer (protein layer), and an upper layer (water and methanol solution layer) (polar metabolite layer), only the lower chloroform layer was aliquoted using a micropipette.

[0093] Subsequently, 150 µl of chloroform was added again, and the above process was repeated once. The entire chloroform layer was aliquoted, and only the water-methanol solution layer was aliquoted from the remaining protein layer and water-methanol solution layer. The aliquoted water-methanol solution layer was filtered using a PTFE syringe filter with a particle size of 0.45 µm. Afterward, the sample was evaporated and dried using a nitrogen concentrator.

[0094] Subsequently, derivatization was carried out using methoxyamine and N,O-Bis(trimethylsilyl)trifluoroacetamide with trimethylchlorosilane. 50 µl of a methoxyamine solution dissolved in pyridine at a concentration of 20 mg / ml was added to the evaporated sample, and the mixture was incubated at 37°C for 90 minutes. Afterward, pretreatment was performed by adding 50 µl of an N,O-Bis(trimethylsilyl)trifluoroacetamide with trimethylchlorosilane solution and incubating at 70°C for 1 hour.

[0095] (3) Instrumental analysis

[0096] Gas chromatography was performed on a Shimadzu QP2010 GC-MS system from Shimadzu Corporation using a DB-5MS (30 m x 0.25 mm, 0.25 µm) chromatography column. 1 µl of sample was injected, and the injection port temperature was set to 270°C. The injection method was splitless, the column flow rate was 1 ml / min, and high-purity helium was used as the mobile phase gas. The oven was maintained at an initial temperature of 70°C for 2 minutes, then heated to 100°C at a heating rate of 4°C / min and maintained for 3 minutes, heated to 160°C at a heating rate of 3°C / min and maintained for 1 minute, heated to 200°C at a heating rate of 4°C / min and maintained for 2 minutes, and heated to 300°C at a heating rate of 8°C / min and maintained for 10 minutes, for a total analysis of 68 minutes. The temperature of the gas chromatography-mass spectrometer connection was set to 300°C, and the ion source temperature was set to 250°C. Analysis was performed in scan mode within the 40-600 m / z range. Data analysis was performed using Shimadzu GCMS Real Time Analysis and Shimadzu GCMS Postrun Analysis, and the NIST Mass Spectral Library (NIST 08) was used for searching the mass spectrometry spectrum library.

[0097] (4) Metabolites discrimination

[0098] Metabolites were identified after standard analysis by comparing the retention index on the chromatogram and the mass spectrometry spectrum of the standard and the urine sample.

[0099] (5) Statistical analysis

[0100] Raw data was converted into a format suitable for statistical analysis using MetAlign software. The converted data was statistically processed using MetaboAnalyst 5.0.

[0101] me. Experimental results

[0102] (1) Multivariate statistical analysis

[0103] (a) Orthogonal Partial least squares Discriminant analysis OPLS -DA)

[0104] As shown in Fig. 2, PM 2.5 High-concentration exposure group (hereinafter high-concentration group) and PM 2.5 Metabolites that showed significant differences among the low-concentration exposure group (hereinafter low-concentration group) were listed from the top in order of their impact. As a result, it was confirmed that among them, Ribose, Arabinose, and Xylose all had high significance with a VIP score of 1.0 or higher.

[0105] In addition, as shown in Figure 3, the difference in the ratio of sugar metabolites between the low-concentration and high-concentration groups was logarithmically represented using a fold change plot of the sugar metabolites. When the ratio was defined as differing by more than twofold to be significant metabolites, no disaccharides or hexoses were identified as significant metabolites, but all three identified pentose metabolites (Ribose, Arabinose, and Xylose) were confirmed to be significant metabolites. Specifically, while some disaccharide and hexose metabolites decreased rather than increasing in the high-concentration group, in the case of pentoses, Arabinose increased by approximately 2.6 times, Ribose by approximately 2.7 times, and Xylose by approximately 14.6 times in the high-concentration group compared to the low-concentration group.

[0106] All three detected pentoses (Ribose, Arabinose, and Xylose) were identified as significant metabolites, and judging from the fact that they showed different patterns of change compared to other metabolites, it was determined that exposure to fine dust had a major effect on the metabolism of pentoses.

[0107] (me) Student's T-test

[0108] As shown in Table 6, it was confirmed that all three types of pentoses (Ribose, Arabinose, and Xylose) showed a significant difference between the high concentration group and the low concentration group at a 95% confidence level (P value < 0.05).

[0109] Student's T-test results Metabolite p-value Ribose 8.99E-07 Arabinose 6.52E-05 Xylose 9.80E-03

[0110] OR, Odds ratio; Cl, ​​Confidence interval * P < 0.05 is considered statistically significant.

[0111] (c) ROC curve

[0112] The ROC curve is a graph showing the performance of a classification model, and a wider AUC (Area Under the Curve) indicates that the model's performance in distinguishing groups is superior. As shown in Figure 4, when the criterion was set to classify only metabolites with an AUC greater than 0.7 as significant metabolites, the three pentose metabolites—ribose, arabinose, and xylose—were all classified as significant metabolites, having AUC values ​​of 0.95, 0.876, and 0.89, respectively.

[0113] Examples 3. Selected biomarkers Re-verification of the prediction of pregnancy complications caused by fine dust exposure using

[0114] In order to verify whether the biomarker selected in Example 2 above is suitable as a biomarker for predicting pregnancy complications caused by actual fine dust exposure, an experiment was conducted using the urine samples used in Example 2. As a control group, urine samples from mothers randomly selected from the group with the lowest individual measured fine dust concentration during pregnancy were used.

[0115] Specifically, samples in which the detection amount of ribose or arabinose from urine samples is significantly enhanced compared to the control group are selected, and the accuracy is verified by confirming the childbirth information of the pregnant woman of the sample ( P-value ) judged.

[0116] Experimental results confirmed that both ribose and arabinose are capable of accurately predicting pregnancy complications (premature birth, preeclampsia, gestational diabetes, NICU admission due to low birth weight, birth defects).

[0117] PM using ribose as a biomarker 2.5 Prediction of pregnancy complications based on exposure level Low Med High P-value PTB 0 0 13.30% 0.044* Reeclampsia 0 0 6.70% 0.157 GDM 0 6.70% 20.00% 0.049* NICU 0 3.30% 13.30% 0.097 Anomaly 0 0 6.70% 0.157

[0118] PM using arabinose as a biomarker 2.5 Prediction of pregnancy complications based on exposure level Low Med High P-value PTB 0 0 13.30% 0.044* Preeclampsia 0 0 6.70% 0.157 GDM 6.70% 6.70% 13.30% 0.512 NICU 0 3.30% 13.30% 0.097

[0119] * PTB, premature birth; Preeclampsia, preeclampsia; GMD: gestational diabetes; NICU, admission to the Neonatal Intensive Care Unit due to pregnancy complications such as low birth weight; Anomaly, birth of a deformed baby. * P < 0.05 is considered statistically significant.

Claims

Claim 1 (a) a step of quantifying the level of one or more metabolites selected from a group consisting of arabinose, ribose, and xylose from a sample of a subject suspected of exposure to fine dust; and (b) a step of predicting that there is a risk of developing pregnancy-related complications due to fine dust exposure if the level of the metabolites quantified is increased when compared to one or more selected from a group consisting of arabinose, ribose, and xylose of a corresponding normal control group; wherein the pregnancy-related complications are any one selected from low birth weight, premature birth, and gestational diabetes, and the sample is any one selected from blood, saliva, feces, vaginal fluid, and urine separated from the subject. Claim 2 delete Claim 3 delete Claim 4 delete Claim 5 delete Claim 6 A method for providing information according to claim 1, wherein the normal control group is a healthy person or mammal not exposed to fine dust. Claim 7 In paragraph 1, the normal control group is a soldier with a low estimated concentration of fine dust exposure, and a method for providing information. Claim 8 delete Claim 9 A method for providing information according to claim 1, wherein the step of quantifying the metabolite is performed using one or more selected from the group consisting of nuclear magnetic resonance spectroscopy, chromatography, ultraviolet spectroscopy, infrared spectroscopy, ELISA (enzyme-linked immunosorbent assay), and mass spectrometry. Claim 10 delete Claim 11 A composition for predicting the occurrence of pregnancy-related complications due to fine dust exposure in pregnant women, comprising a preparation for quantifying one or more metabolites selected from the group consisting of arabinose, ribose, and xylose, wherein the pregnancy-related complication is any one selected from low birth weight, premature birth, and gestational diabetes. Claim 12 delete Claim 13 A kit for predicting the occurrence of pregnancy-related complications due to fine dust exposure in pregnant women, comprising a composition including a preparation for quantifying one or more metabolites selected from the group consisting of arabinose, ribose, and xylose, wherein the pregnancy-related complication is any one selected from low birth weight, premature birth, and gestational diabetes.

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