Analysis of UGT1A1 Polymorphism and Clinical Risk Factors for Neonatal Jaundice in Chinese Tibetan Newborns: Comparison with Han Newborns

Analysis of UGT1A1 Polymorphism and Clinical Risk Factors for Neonatal Jaundice in Chinese Tibetan Newborns: Comparison with Han Newborns | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Analysis of UGT1A1 Polymorphism and Clinical Risk Factors for Neonatal Jaundice in Chinese Tibetan Newborns: Comparison with Han Newborns Zhi Guo, Zhen Qu, Zhen Guo, Jun Chen, Qingsong Zeng, Xiaoming Cao, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6947581/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Neonatal jaundice is a common health issue in infants. Tibetan newborns living at high altitudes may face greater risks due to chronic hypoxia and potential genetic factors. To better understand this issue, we conducted a study to analyze the clinical features of neonatal jaundice and investigate UDP-glucuronosyltransferase 1A1 (UGT1A1) gene mutations among Tibetan newborns living in the Tibetan Plateau Region of China. Methods We collected umbilical cord blood samples, birth records, and maternal clinical data from healthy full-term Tibetan newborns born at Shannan Maternal and Child Health Hospital (3,000–4,000 m altitude). As a comparison group, Han newborns (the predominant ethnic group in China) born and residing in low-altitude regions were also included. UGT1A1 polymorphisms were analyzed by PCR and sequencing. Results A total of 121 Tibetan and 80 Han newborns were included. Among Tibetan newborns, Higher maternal red blood cell (RBC) counts, white blood cell (WBC) counts, and lower mid-pregnancy glucose levels were associated with neonatal jaundice. Meanwhile, compared to Han newborns, Tibetan newborns exhibited lower transcutaneous bilirubin (TcB) levels on postnatal days 2 and 3( P < 0.01), but no differences were observed on days 1, 4, or 5. Also, mothers of Tibetan newborns had higher blood pressure and liver/kidney function markers but lower MCV, WBC, NLR, and PLT ( P A mutations were associated with increased jaundice risk in both groups, while the frequency of -c.-3279T > G mutation was lower in our studied Tibetan newborns ( P = 0.023). Conclusion Tibetan neonates with UGT1A1 c.211G > A mutation or whose mothers have high RBC and WBC and low glucose levels may have a higher jaundice risk. The lower frequency of the c.-3279T > G mutation in Tibetans may indicate a genetic adaptation to high altitude, potentially reducing jaundice susceptibility. Hyperbilirubinemia Neonate Plateau Tibetans UGT1A1 Figures Figure 1 Introduction Neonatal jaundice is one of the most common clinical symptoms in the neonatal period, primarily caused by the accumulation of unconjugated bilirubin in the body. Although most cases of neonatal jaundice are physiological and resolve spontaneously within a few days after birth, excessively high bilirubin levels or unrecognized pathological jaundice may lead to bilirubin encephalopathy and even permanent neurological damage [1–3]. The etiology of jaundice is complex, and even within the same country or region, differences in geographical environment and genetic background can influence its occurrence [4]. The Tibet Autonomous Region of China, located on a plateau with an average altitude exceeding 3,500 meters, is characterized by hypoxia and high ultraviolet radiation. This unique geographical and climatic environment has distinct effects on maternal and infant health among the Tibetan population. The low pressure and hypoxic conditions at high altitudes may alter red blood cell metabolism and survival, making erythrocytes more prone to hemolysis and excessive bilirubin release [5]. However, Tibetans who have lived for generations in this hypoxic environment may possess unique regulatory mechanisms and metabolic traits that indirectly affect the risk factors and incidence of neonatal jaundice. Previous studies have shown that compared to Han Chinese living on the plateau, Tibetans exhibit a lower incidence of chronic mountain sickness, reduced immune stress, lighter hepatic and renal burdens, lower hemoglobin concentrations, and notably higher resting ventilation rates [6]. These differences are likely driven by genetic or physiological factors inherent to Tibetans that enhance their adaptation to high-altitude hypoxia [7]. Nevertheless, epidemiological data on neonatal jaundice in Tibetans remain scarce, and systematic analyses of case characteristics, incidence, risk factors, and intervention strategies are lacking. Hematological examinations are commonly used to understand metabolic status. This study aims to collect clinical and hematological data of Tibetan newborns and their mothers from one local Hospital in Tibetan Plateau Region (altitude 3,000–4,000 meters) to characterize the clinical features, laboratory parameters, and influencing factors of neonatal jaundice in Tibet, providing a scientific basis for early identification, diagnosis, and health management. UDP-glucuronosyltransferase 1A1 (UGT1A1) plays a central role in bilirubin conjugation and excretion. Genetic polymorphisms of the UGT1A1 gene significantly affect enzyme activity and thus the efficiency of bilirubin conjugation [8], which is considered a key genetic factor contributing to neonatal jaundice. China is a multiethnic country with 56 officially recognized ethnic groups, each with distinct genetic backgrounds. Studies have shown significant differences in UGT1A1 polymorphisms among various ethnic populations [9]. However, data on UGT1A1 polymorphisms in Chinese ethnic minorities such as Tibetans, Uyghurs, and Mongolians remain limited. Our previous research on Han and Uyghur newborns revealed significant differences in the (TA)n repeat polymorphism in the UGT1A1 promoter region [10]. This study focuses specifically on the Tibetan population. Increasing evidence suggests that Tibetans, who have inhabited the Qinghai-Tibet Plateau for thousands of years, have developed unique genetic adaptations distinguishing them from other Chinese ethnic groups [11]. These adaptations likely involve metabolism-related genes, including UGT1A1. Understanding UGT1A1 polymorphisms in Tibetan newborns will help clarify the genetic risk factors for hyperbilirubinemia in this population and fill the gap in comparative studies among ethnic groups. This knowledge will provide genetic and theoretical support for the early precise diagnosis and individualized prevention of neonatal jaundice in Tibetans. Accordingly, this study aims to compare UGT1A1 gene polymorphisms between Han and Tibetan newborns, explore their association with jaundice risk, and reveal ethnic-specific patterns in bilirubin metabolism, offering new insights to improve neonatal health. Method Study subjects and sample collection Tibetan newborns were recruited from the Maternal and Child Health Hospital of Shannan City, located on the Tibetan Plateau in China (altitude 3,000–4,000 meters), between January 2024 and April 2025. Relevant clinical data were collected via the hospital’s case management system, including date of birth, sex, birth weight, mode of delivery, gestational age, Apgar scores, postnatal transcutaneous bilirubin (TcB) levels, hematological test results, and maternal information such as age, pre- and post-delivery weight, and laboratory test results. All data were reviewed by designated physicians. This study retrospectively analyzed clinical records of hospitalized neonates. Ethnicity was confirmed based on parent-reported demographic information (name and ethnicity). Newborns were included only if both parents were Tibetan, the mother had no major illness or genetic disease, and the neonate was a full-term singleton without significant birth defects or serious illness. Based on existing studies, most newborns develop jaundice within 2–3 days after birth, with bilirubin levels typically peaking around day 5 and then declining [12]. Therefore, TcB values from days 1 to 5 after birth were recorded. This study was approved by the Ethics Committees of Yangtze University and the participating hospital. To further explore the causes of neonatal jaundice in Tibet, potential confounding factors were excluded. Using more stringent criteria, we continuously collected clinical data from Tibetan newborns born between January and June 2024 at the Shannan Maternal and Child Health Hospital, and Han Chinese newborns born in the low-altitude region of Jingzhou, China (The two affiliated hospitals of Yangtze University). With informed consent from the mothers, umbilical cord blood samples were collected and stored at -80°C for subsequent genetic analysis. Inclusion criteria were: gestational age > 37 weeks, birth weight > 2.5 kg, no major birth defects or severe diseases, Apgar score of 10, and no maternal complications such as diabetes or hypertension. Based on the recorded bilirubin levels (TcB and/or total serum bilirubin, TSB), the neonates were divided into a case group and a control group. The case group included newborns whose TcB/TSB values were at or above the 95th percentile on the specific time-based TcB/TSB percentile charts established by the Chinese Multicenter Study Group for Neonatal Hyperbilirubinemia [13,14]. UGT1A1 analysis Genomic DNA was extracted from the collected EDTA-anticoagulated umbilical cord blood samples using the Tiangen DNA extraction kit. Polymerase Chain Reaction (PCR) amplification and direct sequencing were performed using five pairs of primers targeting the enhancer, promoter, all five exons, and exon-intron boundaries of the UGT1A1 gene, as detailed in our previous study[15,16]. Data Analysis Differences in clinical and hematological data between Tibetan neonates with and without jaundice were compared. Categorical variables were analyzed using the Chi-square test or Fisher’s exact test, as appropriate. For continuous variables, independent samples t-tests were used for normally distributed data, while the Mann-Whitney U test was applied for non-normally distributed data. Neonatal jaundice status was treated as the dependent variable in a binary logistic regression analysis using the backward-LR method to identify relevant influencing factors. Gestational age, sex, and birth weight were retained in the model throughout to control for potential confounders. Common UGT1A1 mutations were summarized, and differences in mutation frequencies between the two ethnic groups were compared. Multivariate logistic regression analyses were then conducted to evaluate the association between specific UGT1A1 variants and the risk of neonatal jaundice, adjusting for potential confounders such as sex, gestational age, and birth weight. Variables with P < 0.100 were retained in the model, and UGT1A1 genotypes were forced into the model. For each UGT1A1 locus, association analyses were conducted under codominant, dominant, and recessive genetic models. All statistical analyses were performed using SPSS version 16.0, and P < 0.05 was considered statistically significant. Results Clinical data were collected from 121 Tibetan pregnant women and their newborns. Meanwhile, a total of 43 umbilical cord blood samples were collected from Tibetan neonates in high-altitude regions of China. Among the collected 43 samples, 13 were excluded based on the predefined criteria, leaving 30 samples for final analysis. Additionally, clinical data from 80 Han pregnant women and their newborns, as well as 30 cases of umbilical cord blood samples from Han neonates in the plain region of China, were collected as controls. Clinical features of jaundice in Tibetan neonates Based on TSB and/or TcB levels, the 121 Tibetan neonates were divided into a jaundice group ( n = 68) and a non-jaundice group ( n = 53). A systematic comparison was conducted between the two groups regarding basic maternal and neonatal information, including maternal age, gestational age, blood pressure, altitude of residence, neonatal birth weight, and various hematological indicators. Preliminary results showed that mothers in the jaundice group had significantly higher mean red blood cell (RBC) counts (4.314 ± 0.574 vs. 4.018 ± 0.461, P = 0.003) and white blood cell (WBC) counts (9.778 ± 3.258 vs. 8.550 ± 2.908, P = 0.036) than those in the non-jaundice group, suggesting a possible hypoxic adaptation or immune activation state in the jaundice group mothers. In contrast, maternal fasting blood glucose levels during the second trimester were significantly lower in the jaundice group than in the non-jaundice group (4.516 ± 0.6515 vs. 5.013 ± 1.388, P = 0.032), indicating possible energy insufficiency during pregnancy among mothers of the jaundice group. In addition, from day 2 to day 5 after birth, TcB values were significantly higher in the jaundice group than in the non-jaundice group ( P < 0.01), suggesting that early TcB monitoring may have predictive value for the development of jaundice. No significant differences were found between the two groups regarding neonatal sex, mode of delivery (cesarean section and/or vaginal delivery), or breastfeeding status ( Table 1 ). Table 1 Tibetan Neonates with Jaundice Compared to Non-Jaundiced Neonates: Clinical Information and Hematological Data. Jaundice ( n = 68) N(%)/Mean (SD) Non-Jaundiced ( n = 53) N(%)/Mean (SD) Mean Difference 95% CI p General Information of Neonates Sex of neonates NS Male 35 (51.5%) 35 (66.0%) Female 33 (48.5%) 18 (34.0%) Mode of delivery NS Cesarean section 13 (19.1%) 10 (18.9%) Vaginal delivery 55 (77.9%) 43 (81.1%) Feeding NS Exclusive breast feeding 45 (86.5%) 32 (97.0%) Mixed feeding 7 (113.5%) 1 (3.0%) Birthweight (g) 3098.62 (399.19) 3159.92 (470.59) 61.31 -100.19, 222.81 NS TcB (Day 1 After Birth) (mg/dl) 6.17 (2.00) 5.87 (2.06) -0.30 -1.18, 0.58 NS TcB (Day 2 After Birth) (mg/dl) 7.15 (2.33) 6.07 (2.15) -1.08 -1.90, -0.27 0.010 TcB (Day 3 After Birth) (mg/dl) 10.90 (2.30) 9.22 (3.68) -1.68 -2.92, -0.45 0.006 TcB (Day 4 After Birth) (mg/dl) 12.37 (1.40) 10.57 (1.74) -1.80 -2.52, -1.071 < 0.001 TcB (Day 5 After Birth) (mg/dl) 12.32 (1.54) 10.52 (1.58) -1.80 -2.57, -1.03 < 0.001 Maternal general information and laboratory test Age 30.34 (4.86) 29.75 (4.376) -0.58 -2.29, 1.12 NS Gestational age (day) 277.03 (8.23) 277.28 (8.30) 0.25 -2.78, 3.28 NS Systolic blood pressure 116.34 (15.00) 117.72 (13.47) 1.38 -3.88, 6.63 NS Diastolic blood pressure 75.92 (10.28) 76.472 (10.35) 0.55 -3.23, 4.33 NS Red blood cell count (RBC) 4.31 (0.57) 4.02 (0.46) -0.30 -0.49, -0.11 0.003 Hemoglobin (Hb) 133.33 (27.90) 129.12 (24.83) -4.21 -14.04, 5.61 NS Mean corpuscular volume (MCV) 90.45 (9.74) 90.24 (8.72) -0.22 -3.66, 3.22 NS White blood cell count (WBC) 9.78 (3.26) 8.55 (2.91) -1.23 -2.38, -0.08 0.036 Neutrophils/lymphocytes (NLR) 5.26 (2.52) 4.57 (1.94) -0.69 -1.51, 0.13 NS Platelet count (PLT) 212.05 (54.46) 207.19 (55.95) -4.86 -25.25, 15.54 NS First prenatal checkup FBG 4.57 (0.64) 4.64 (0.61) 0.07 -0.27, 0.42 NS Second-trimester FBG 4.52 (0.652) 5.01 (1.39) 0.50 0.043, 0.95 0.032 Second-trimester 1hPBG 7.29 (1.52) 7.56 (1.45) 0.27 -0.40, 0.94 NS Second-trimester 2hPBG 6.39 (0.97) 6.53 (0.97) 0.13 -0.30, 0.57 NS ALT 17.13 (10.39) 16.82 (10.50) -0.31 -4.25, 3.63 NS AST 34.18 (19.16) 32.31 (19.72) -1.87 -9.07, 5.34 NS Total bilirubin (TBIL) 81.02 (90.42) 72.34 (75.61) -8.68 -39.22, 21.87 NS Serum total protein (TP) 56.56 (7.93) 56.08 (8.20) -0.47 -3.46, 2.52 NS Albumin 35.28 (5.60) 34.49 (4.65) -0.80 -2.68, 1.09 NS Serum creatinine (CRE) 53.05 (11.58) 52.87 (9.07) -0.18 -4.00, 3.64 NS Serum uric acid (UA) 321.60 (69.42) 312.22 (65.30) -9.38 -34.33, 15.57 NS Neonatal laboratory test results Red blood cell count (RBC) 4.87 (0.53) 4.74 (0.50) -0.12 -0.35, 0.10 NS Hemoglobin (Hb) 177.75 (20.90) 168.60 (22.40) -9.15 -18.40, 0.10 0.052 Mean corpuscular volume (MCV) 104.03 (5.30) 103.49 (6.68) -0.54 -3.08, 1.99 NS White blood cell count (WBC) 11.94(4.46) 12.75 (4.77) 0.08 -1.16, 2.79 NS Platelet count (PLT) 240.44 (57.26) 253.44 (51.66) 13.01 -10.60, 36.61 NS ALT 13.13 (6.38) 13.42 (5.92) 0.29 -2.37, 2.95 NS AST 56.68 (21.35) 56.26 (23.69) -0.41 -10.01, 9.19 NS Indirect bilirubin (IBIL) 152.92 (65.82) 136.76 (65.14) -59.30 -85.43, -33.18 NS Serum total protein (TP) 51.70 (4.14) 51.61 (3.71) -0.08 -1.79, 1.62 NS Albumin 35.70 (2.41) 34.91 (2.24) -0.79 -1.79, 0.22 NS Serum creatinine (CRE) 56.21 (13.83) 53.84 (13.57) -2.36 -8.26, 3.53 NS Serum uric acid (UA) 338.30 (100.65) 310.70 (86.50) -27.59 -68.37, 13.18 NS Abbreviations: CI, confidence interval; d, days; SD, standard deviation; NS, P > 0.1; Multivariate Analysis of Risk Factors for Neonatal Jaundice in Tibetan Newborns After controlling for potential confounding factors and imputing missing values with means, the final model selected using the backward-LR method of logistic regression showed that maternal red blood cell count (OR = 9.657, P < 0.001) and white blood cell count (OR = 1.234, P = 0.038) were significantly positively associated with neonatal jaundice, while maternal mid-pregnancy fasting glucose level (OR = 0.359, P = 0.035) and neonatal white blood cell count (OR = 0.863, P = 0.027) were significantly negatively associated with jaundice (Table 2 ). Table 2 Multivariate Logistic Regression Analysis of Factors Influencing Neonatal Jaundice in the Tibetan Region. Variable β SE Wald X 2 P OR OR 95% CI Gestational age 0.003 0.028 0.013 0.909 1.00 0.95, 1.06 Sex of neonate 0.059 0.467 0.016 0.900 1.06 0.42, 2.65 Birthweight 0.000 0.001 0.140 0.709 1.00 1.00, 1.00 Maternal red blood cell count (RBC) 1.355 0.478 8.036 0.005 3.88 1.52, 9.90 Maternal white blood cell count (WBC) 0.178 0.081 4.854 0.028 1.20 1.02, 1.40 Maternal second-trimester FBG -1.026 0.487 4.430 0.035 0.36 0.14, 0.93 Neonatal white blood cell count (WBC) -0.148 0.067 4.908 0.027 0.86 0.76, 0.98 Comparative Analysis of Clinical Data Between Tibetan and Han Pregnant Women and Newborns Considering the potential impact of disease on bilirubin levels, we conducted a comparative analysis of clinical data from 36 non-hospitalized full-term healthy Tibetan newborns and 74 non-hospitalized full-term healthy Han newborns and their mothers. No significant differences were found between the two groups in maternal age, gestational age, or newborn birth weight. We observed that similar to Han newborns, the TcB levels in Tibetan newborns peaked on the 5th day after birth. Also, on days 1, 4, and 5 after birth, TcB levels showed no statistically significant differences between Tibetan and Han newborns. However, on days 2 and 3, TcB levels of Tibetan newborns were significantly lower as compared with Han newborns ( P < 0.01). In addition, Tibetan mothers living at high altitudes had significantly higher systolic blood pressure ( P = 0.042), alanine aminotransferase (ALT) ( P = 0.019), aspartate aminotransferase (AST) ( P < 0.001), and serum creatinine (CRE) ( P = 0.038) than Han mothers from lowland areas. While, mean corpuscular volume (MCV) ( P < 0.001), WBC count ( P = 0.018), neutrophil-to-lymphocyte ratio (NLR) ( P = 0.026), and platelet count (PLT) ( P = 0.022) were significantly lower in Tibetan mothers (Table 3 ). Table 3 Comparative Analysis of demographic and clinical information in our studied Non-Hospitalized Tibetan in high-altitude and Han Chinese Neonates in low-altitude. Tibetan ( n = 36) N(%)/Mean (SD) Han ( n = 74) N(%)/Mean (SD) Mean Difference 95% CI P General Information on Neonates Sex of neonate (Case) NS Male 17 (47.2%) 34 (45.9%) Female 19 (52.8%) 40 (54.1%) Birthweight (g) 3103.42 (385.92) 3222.03 (552.36) -118.61 -321.80, 84.58 NS TcB (Day 1 After Birth) (mg/dl) 2.40 (3.68) 2.57 (2.49) -0.17 -2.23, 1.89 NS TcB (Day 2 After Birth) (mg/dl) 4.76 (1.518) 6.72 (2.15) -1.95 -2.75, -1.15 < 0.001 TcB (Day 3 After Birth) (mg/dl) 8.69 (1.86) 9.68 (1.92) -0.99 -1.77, -0.21 0.014 TcB (Day 4 After Birth) (mg/dl) 10.93 (2.43) 11.20 (2.17) -0.27 -1.22, 0.68 NS TcB (Day 5 After Birth) (mg/dl) 11.64 (2.49) 11.89 (1.80) -0.25 -1.41, 0.92 NS Neonatal jaundice (Case) NS Yes (TcB ≥ 12.9mg/dl) 11 (40.7%) 15 (30%) No (TcB < 12.9mg/dl) 16 (59.3%) 35 (70%) Maternal general information and laboratory test Age 29.08 (4.91) 30.18 (4.01) -1.09 -2.83, 0.65 NS Gestational age (day) 274.78 (6.89) 273.35 (5.24) 1.43 -0.93, 3.79 NS Systolic blood pressure 121.92 (12.33) 117.38 (10.10) 4.54 0.16, 8.92 0.042 Diastolic blood pressure 77.17 (9.22) 77.42 (7.83) -0.25 -3.60, 3.09 NS Red blood cell count (RBC) 4.05 (0.45) 4.00 (0.40) 0.05 -0.12, 0.22 NS Hemoglobin (Hb) 120.47 (12.65) 120.71 (11.44) -0.24 -5.10, 4.61 NS Mean corpuscular volume (MCV) 85.48 (7.12) 91.33 (5.26) -5.85 -8.28, -3.43 < 0.001 White blood cell count (WBC) 8.08 (2.51) 9.46 (2.88) -1.38 -2.51, -0.24 0.018 Neutrophils/lymphocytes (NLR) 4.18 (1.79) 4.97 (1.64) -0.79 -1.48, -0.10 0.026 Platelet count (PLT) 185.79 (59.82) 213.00 (55.18) -27.21 -50.48, -3.93 0.022 ALT 18.00 (10.23) 12.16 (12.45) 5.84 0.99, 10.69 0.019 AST 25.44 (10.62) 15.88 (7.57) 9.56 6.02, 13.11 < 0.001 Total bilirubin (TBIL) 8.64 (4.02) 7.97 (3.14) 0.67 -0.74, 2.09 NS Serum total protein (TP) 60.19 (4.37) 62.69 (13.44) -2.50 -7.19, 2.19 NS Albumin 34.45 (3.78) 34.90 (6.30) -0.44 -2.76, 1.87 NS Serum creatinine (CRE) 54.30 (8.16) 50.24 (9.69) 4.06 0.22, 7.90 0.038 Serum uric acid (UA) 308.24 (56.47) 285.59 (82.64) 22.66 -8.73, 54.05 NS Abbreviations: CI, confidence interval; d, days; SD, standard deviation; NS, P > 0.1; UGT1A1 analysis We analyzed the mutation frequencies of common UGT1A1 gene variants in our studied neonates. Similar to Han newborns, three types of UGT1A1 mutations were detected in the studied Tibetan population: the c.211G > A (G211A, UGT1A1*16) mutation in the exon region, the c.-3279T > G (T-3279G, UGT1A1*60) mutation in the enhancer region, and the (TA)n polymorphism in the promoter region (Fig. 1 ). None of these polymorphisms significantly deviated from Hardy-Weinberg equilibrium (HWE). In our studied newborns, 41.4% of Tibetan newborns carried a mutation in the exon region of UGT1A1. Specifically, the homozygous mutation rate for c.211G > A was 6.9% (2/29), and the heterozygous mutation rate was 34.5% (10/29). Among Han newborns, the homozygous mutation rate at this site was 5.9% (2/34), and the heterozygous mutation rate was 23.5% (8/34). For the enhancer region mutation c.-3279T > G, the homozygous and heterozygous mutation rates in Tibetan newborns were 3.6% (1/28) and 28.6% (8/28), respectively. In contrast, Han newborns exhibited a higher total mutation rate at this site (66.7%), including a homozygous mutation rate of 18.2% (6/33) and a heterozygous mutation rate of 48.5% (16/33). Regarding the (TA)n polymorphism, only two genotypes - (TA)6/6 and (TA)6/7 (UGT1A1*28) - were detected in this study. The mutation frequency of (TA)n polymorphism in Tibetan newborns was 24.1% (7/29), while in Han newborns it was 20% (5/25). There were no statistically significant differences between Tibetan and Han newborns in the frequencies of the c.211G > A mutation or the (TA)n promoter polymorphism. However, the c.-3279T > G polymorphism showed a significant difference between the two ethnic groups, with the mutation rate in the Han population being 2.62 times higher than in the Tibetan population ( P = 0.023) (Table 4 ). Table 4 UGT1A1 c.-3275T > G, (TA)n, and c.211G > A Variants in Tibetan vs. Han Neonates. Tibetan Han Tibetan vs. Han N (%) P H−W a N (%) P H−W a Mode b OR (95% CI ) P c.211G > A 0.64 1.00 Dominant 0.54 G/G 17 (58.6%) 24 (70.6%) 1.00 G/A 10 (34.5%) 8 (23.5%) 1.42 (0.47, 4.30) A/A 2 (6.9%) 2 (5.9%) c.-3279T > G 1.00 1.00 Dominant 0.0095 T/T 19 (67.9%) 11 (33.3%) 1.00 T/G 8 (28.6%) 16 (48.5%) 0.23 (0.07, 0.73) G/G 1 (3.6%) 6 (18.2%) (TA) n 1.00 1.00 - 0.60 TA 6 /TA 6 22 (75.9%) 20 (80.0%) 1.00 TA 6 /TA 7 7 (24.1%) 5 (20.0%) 1.43 (0.37, 5.52) TA7/TA 7 - - - a Hardy-Weinberg Equilibrium test p-value. b In the linear regression analysis, neonates with wild UGT1A1 genotype (i.e. G/G) were set as the reference group under the dominant genetic model assumption, while both those wildtypes and the heterozygous UGT1A1 variant carrier (i.e. G/G + G/A) were set as the reference group under the recessive model. UGT1A1 Variants and Neonatal Hyperbilirubinemia Logistic regression analysis showed that after controlling for potential confounding factors such as sex, age, and ethnicity, the c.211G > A mutation in the UGT1A1 exon was significantly associated with an increased risk of hyperbilirubinemia in both Tibetan ( P = 0.027) and Han ( P = 0.0081) newborns. No significant association was found between the (TA)n polymorphism or the c.-3279T > G mutation and the increased risk of hyperbilirubinemia (Table 5 ). Table 5 Association between UGT1A1 Variants and Neonatal Hyperbilirubinemia under Different Genetic Model Assumptions. Tibetan Han Total Mode a ORadj b (95%CI) P Mode a ORadj b (95%CI) P Mode a ORadj b (95%CI) P c.211 G > A Dominant 0.03 Recessive 0.09 Recessive 0.0031 G/G 1.00 1.00 1.00 G/A 11.98 (0.99, 145.48) A/A NA (0.00, NA) NA (0.00, NA) (TA)n Dominant 0.37 - 0.70 - 0.95 TA6/TA6 1.00 1.00 1.00 TA6/TA7 2.67 (0.30, 23.71) 0.59 (0.04, 9.12) 1.05 (0.25, 4.44) TA7/TA7 - - - c.-3279T > G Overdominant 0.10 Dominant 0.31 Overdominant 0.34 T/T 1.00 1.00 1.00 G/G 0.29 (0.02, 3.55) T/G 4.86 (0.66, 35.77) 1.89 (0.51, 7.06) a In the linear regression analysis, neonates with wild UGT1A1 genotype (i.e. G/G) were set as the reference group under the dominant genetic model assumption, while both those wildtypes and the heterozygous UGT1A1 variant carrier (i.e. G/G + G/A) were set as the reference group under the recessive model. b Adjusted for gender, birth week, and race. Discussion The occurrence of neonatal jaundice is influenced by multiple factors and demonstrates marked ethnic and regional differences. Understanding its clinical characteristics and underlying causes is crucial for early diagnosis and intervention. Recent studies have highlighted regional and ethnic variations in the incidence of neonatal hyperbilirubinemia and their association with genetic factors [17–20]. This study systematically characterizes neonatal jaundice in Tibetan newborns from high-altitude regions of China. For the first time, we explore the genetic basis of jaundice in this population by comparing hematological parameters and UGT1A1 gene polymorphisms - a key gene in bilirubin metabolism - between Tibetan newborns and Han newborns from lowland areas. Our findings revealed no significant differences between the two groups in the incidence of neonatal jaundice or the carrier rate of the c.211G > A variant, a mutation prevalent in East Asia. However, the frequency of the c.-3275T > G variant in the UGT1A1 enhancer region was significantly lower in Tibetan newborns than in Han newborns. At high altitudes, increased RBC production is a common adaptation to hypoxia [21]. This adaptation may elevate maternal blood viscosity, reducing uterine artery (UtA) blood flow and impairing placental perfusion, potentially leading to fetal hypoxia [22]. In our cohort, mothers of jaundiced Tibetan newborns had significantly higher RBC counts than those of non-jaundiced newborns. This may be an important risk factor for jaundice in high-altitude settings and could serve as a clinical indicator. After adjusting for confounding variables, we observed a negative correlation between neonatal white blood cell count and jaundice occurrence, further suggesting that common causes of jaundice - such as ABO/Rh blood group incompatibility and infectious sepsis - may not be the primary etiologies for neonates in this region [23,24]. Overall, our study revealed that the hematological characteristics of jaundiced neonates in this region differ from those of typical neonatal jaundice cases in other lower-altitude populations. We also found that Tibetan newborns in the high-altitude region had significantly lower TcB levels than Han newborns in the low-altitude region on days 2 and 3 post-birth, though no differences were seen on the other three days ( day 1, day 4, and day 5). Bilirubin is primarily produced through heme degradation from senescent RBCs. Due to their structural immaturity, neonatal RBCs are more prone to oxidative stress, which can trigger eryptosis, a form of programmed red blood cell death analogous to apoptosis, ultimately leading to increased bilirubin production [25]. Hypoxia may enhance the antioxidant capacity of RBCs [26], and chronic high-altitude exposure in Tibetans may contribute to more stable RBCs and slower hemolysis. In addition, significant physiological differences were observed between Tibetan and Han mothers, likely reflecting the systemic effects of long-term high-altitude living. Tibetan mothers exhibited elevated systolic blood pressure, a cardiovascular adaptation to chronic hypoxia aimed at improving tissue oxygenation [27–29]. Pregnancy itself induces circulatory stress [30], which may be intensified by hypoxic conditions. Dietary factors, such as higher caffeine intake, may also play a role [31]. Studies conducted in Peru (at 3400 meters) and in western Saudi Arabia (at 2177 meters) similarly found reduced white blood cell and neutrophil counts in high-altitude pregnant women, although platelet trends differed from ours [32,33]. Hypoxia-induced erythropoietin (EPO) secretion increases RBC production while reducing immature megakaryocytes, potentially lowering platelet counts [34]. These hematological responses remain complex and multifactorial. Tibetans’ long-term adaptation to hypoxia and their unique diet may lead to differential gene expression and metabolic profiles, which in turn may affect bilirubin metabolism. The c.-3275T > G variant is located in the phenobarbital-responsive enhancer module (gtPBREM) of the UGT1A1 gene. This enhancer contains binding elements for the constitutive androstane receptor (CAR), pregnane X receptor (PXR), and glucocorticoid receptor (GR) [35], and is known to enhance phenobarbital sensitivity in patients with Gilbert syndrome (GS, OMIM #143500). This mutation can reduce UGT1A1 transcription and diminish regulatory effects from CAR [36], PXR, GR, and the aryl hydrocarbon receptor (AhR) [37,38], especially when coexisting with promoter variants. Hypoxia is known to suppress UGT1A1 expression through PXR and CAR pathways [39]. Beyond bilirubin metabolism, PXR and CAR also regulate energy homeostasis, including glucose and lipid metabolism [40,41]. Therefore, the lower frequency of c.-3275T > G in Tibetans may reflect adaptive selection under hypoxic conditions, balancing bilirubin risk and metabolic function. PXR has also been implicated in suppressing gluconeogenesis and lipid oxidation, potentially increasing type 2 diabetes risk [42–44]. An increased density of PXR/CAR binding sites in UGT1A1 may alter PXR/CAR availability for other pathways, providing additional adaptive advantages in energy metabolism. Studies from India [45] and Germany [46] have reported associations between c.-3275T > G and hyperbilirubinemia, while other studies from Poland and Pakistan found no such relationship [47,48]. Our findings align with the latter, showing no significant link between this mutation and neonatal jaundice in either Han or Tibetan newborns. This highlights the need for further investigation. Additionally, no differences were found between the two populations regarding the c.211G > A mutation or the (TA)n polymorphism. The c.211G > A variant is the most common genetic cause of Gilbert syndrome in East Asia [49,50], impairing UGT1A1 enzymatic activity and increasing bilirubin levels. In our study, both Han and Tibetan carriers were more likely to develop hyperbilirubinemia compared to wild-type individuals, consistent with previous reports [51–53]. While the (TA)n polymorphism is a major cause of unconjugated hyperbilirubinemia in Caucasian and African populations [49,51], most studies in East Asia have not found a strong association. Some even suggest a protective effect of the (TA)7 variant in breastfed infants [16,51]. These findings emphasize the need to consider both genetic and environmental contexts when assessing jaundice risk, particularly in high-altitude regions. In conclusion, this study provides a preliminary assessment of UGT1A1 polymorphisms and their relationship to neonatal jaundice in Tibetan and Han newborns. While our findings suggest certain genetic differences, the limited sample size may restrict the generalizability of results. Future studies should involve larger, more diverse cohorts to further clarify the role of UGT1A1 variants in neonatal jaundice and inform targeted prevention strategies for ethnic minority populations in China. Conclusion In summary, this study comprehensively analyzed the epidemiological features and related factors of neonatal jaundice in Tibetans, and several clinically significant hematological indicators were identified. This would provide new insights for early diagnosis and treatment in high-altitude regions. Furthermore, our preliminary investigation further elucidated the molecular mechanisms underlying jaundice in Tibetan newborns, revealing differences in UGT1A1 gene polymorphisms between Tibetan and Han newborns, which may be associated with Tibetan populations' adaptive changes to high-altitude environments. These findings offer a theoretical basis for individualized prevention and treatment of neonatal jaundice in Tibetan populations. Future research should involve larger sample sizes and more in-depth studies in this region to better guide clinical practice. Abbreviations GS Gilbert syndrome gtPBREM phenobarbital-responsive enhancer module of UGT1A1 PXR pregnane X receptor TcB transcutaneous bilirubin TSB total serum bilirubin UGT UDP-glucuronosyltransferase UtA uterine artery Declarations Acknowledgements None Authors’ contributions Hui Yang and Quan Gong conceptualized and designed the study. Zhen Qu, Zhen Guo, Jun Chen, and Qin De were responsible for collecting umbilical cord blood samples from Tibetan newborns and providing electronic case report data. Qingsong Zeng, Meng Liao, Li Wang, and Jufang Tang were responsible for collecting umbilical cord blood samples from Han newborns and providing electronic case report data. Quan Gong, Jun Chen, and Xianwang Wang participated in the study management and supervision. Zhi Guo and Hui Yang participated in the sample collection and contributed to the statistical analysis of the data and the drafting of the initial manuscript. Funding National Natural Science Foundation of China (Yang Hui), Grant number：81801509. Shannan Municipal Science and Technology Program Project, Grant number: SNSBJKJJHXM2023012. Yangtze University Medical and Health Aid Tibet Project, Grant number：2023YZ01 Jingzhou City Science and Technology Plan Project, Grant number：2024HD18. Preterm Screening and Mechanisms of Low Birth Weight in Fetuses in the Tibetan Plateau Region: Hubei Provincial Department of Education Project, Grant number：BXLBX0355. Data Availability Statement The nucleotide sequence data generated in this study have been deposited in the GenBank database under accession numbers PV897292-PV897477 (https://www.ncbi.nlm.nih.gov/nucleotide/). Conflicts of Interest Statement The authors have no relevant financial or non-financial interests to disclose. Ethics approval This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Yangtze University, the Medical Ethics Committee of Jingzhou First People's Hospital(KY202351), and the Medical Ethics Committee of Shannan Maternal and Child Health Hospital. Consent to participate Written informed consent was obtained from all parents. References Thomas M, Greaves RF, Tingay DG, Loh TP, Ignjatovic V, Newall F, et al. Current and emerging technologies for the timely screening and diagnosis of neonatal jaundice. Crit Rev Clin Lab Sci. 2022;59(5):332-52. Lee B, Piersante T, Calkins KL. Neonatal hyperbilirubinemia. Pediatr Ann. 2022;51(6):e219-27. Hegyi T, Kleinfeld A. Neonatal hyperbilirubinemia and the role of unbound bilirubin. J Matern Fetal Neonatal Med. 2022;35(25):9201-7. Erdeve O. 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Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 14 Sep, 2025 Reviewers agreed at journal 03 Sep, 2025 Reviewers invited by journal 15 Jul, 2025 Editor assigned by journal 10 Jul, 2025 Editor invited by journal 09 Jul, 2025 Submission checks completed at journal 09 Jul, 2025 First submitted to journal 09 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6947581","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":485587480,"identity":"48622689-3905-4d5d-980e-00a19b62cafa","order_by":0,"name":"Zhi Guo","email":"","orcid":"","institution":"Yangtze University","correspondingAuthor":false,"prefix":"","firstName":"Zhi","middleName":"","lastName":"Guo","suffix":""},{"id":485587481,"identity":"9ecc4d39-1fee-43de-a6c5-2f02bc96d73b","order_by":1,"name":"Zhen Qu","email":"","orcid":"","institution":"Shannan Women and Children’s Health Care 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University","correspondingAuthor":false,"prefix":"","firstName":"Quan","middleName":"","lastName":"Gong","suffix":""}],"badges":[],"createdAt":"2025-06-22 04:08:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6947581/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6947581/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87046253,"identity":"525b8014-dc40-44b7-acf2-101b90232732","added_by":"auto","created_at":"2025-07-18 14:36:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":670845,"visible":true,"origin":"","legend":"\u003cp\u003eMutations of \u003cem\u003eUGT1A1\u003c/em\u003ewere found in our study cohort. \u003cstrong\u003e(a)\u003c/strong\u003e c. 211 G \u0026gt;A heterozygote (Gly71Arg) \u003cstrong\u003e(b)\u003c/strong\u003e c. 211 G \u0026gt;A homozygote (Gly71Arg) \u003cstrong\u003e(c)\u003c/strong\u003ec.-3279T\u0026gt;G heterozygote \u003cstrong\u003e(d)\u003c/strong\u003e c.-3279T\u0026gt;G homozygote \u003cstrong\u003e(e)\u003c/strong\u003e(TA)6/(TA)6. (F) (TA)6/(TA)7\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6947581/v1/792c988cc413f83fee57e4a7.png"},{"id":87048944,"identity":"ce442416-0970-4aee-8e14-6daa620ee8ba","added_by":"auto","created_at":"2025-07-18 14:52:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1958198,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6947581/v1/90066397-6ef3-4c31-be98-5a14412c2c61.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of UGT1A1 Polymorphism and Clinical Risk Factors for Neonatal Jaundice in Chinese Tibetan Newborns: Comparison with Han Newborns","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNeonatal jaundice is one of the most common clinical symptoms in the neonatal period, primarily caused by the accumulation of unconjugated bilirubin in the body. Although most cases of neonatal jaundice are physiological and resolve spontaneously within a few days after birth, excessively high bilirubin levels or unrecognized pathological jaundice may lead to bilirubin encephalopathy and even permanent neurological damage [1–3]. The etiology of jaundice is complex, and even within the same country or region, differences in geographical environment and genetic background can influence its occurrence [4].\u003c/p\u003e\u003cp\u003eThe Tibet Autonomous Region of China, located on a plateau with an average altitude exceeding 3,500 meters, is characterized by hypoxia and high ultraviolet radiation. This unique geographical and climatic environment has distinct effects on maternal and infant health among the Tibetan population. The low pressure and hypoxic conditions at high altitudes may alter red blood cell metabolism and survival, making erythrocytes more prone to hemolysis and excessive bilirubin release [5]. However, Tibetans who have lived for generations in this hypoxic environment may possess unique regulatory mechanisms and metabolic traits that indirectly affect the risk factors and incidence of neonatal jaundice. Previous studies have shown that compared to Han Chinese living on the plateau, Tibetans exhibit a lower incidence of chronic mountain sickness, reduced immune stress, lighter hepatic and renal burdens, lower hemoglobin concentrations, and notably higher resting ventilation rates [6]. These differences are likely driven by genetic or physiological factors inherent to Tibetans that enhance their adaptation to high-altitude hypoxia [7]. Nevertheless, epidemiological data on neonatal jaundice in Tibetans remain scarce, and systematic analyses of case characteristics, incidence, risk factors, and intervention strategies are lacking. Hematological examinations are commonly used to understand metabolic status. This study aims to collect clinical and hematological data of Tibetan newborns and their mothers from one local Hospital in Tibetan Plateau Region (altitude 3,000–4,000 meters) to characterize the clinical features, laboratory parameters, and influencing factors of neonatal jaundice in Tibet, providing a scientific basis for early identification, diagnosis, and health management.\u003c/p\u003e\u003cp\u003eUDP-glucuronosyltransferase 1A1 (UGT1A1) plays a central role in bilirubin conjugation and excretion. Genetic polymorphisms of the UGT1A1 gene significantly affect enzyme activity and thus the efficiency of bilirubin conjugation [8], which is considered a key genetic factor contributing to neonatal jaundice. China is a multiethnic country with 56 officially recognized ethnic groups, each with distinct genetic backgrounds. Studies have shown significant differences in UGT1A1 polymorphisms among various ethnic populations [9]. However, data on UGT1A1 polymorphisms in Chinese ethnic minorities such as Tibetans, Uyghurs, and Mongolians remain limited. Our previous research on Han and Uyghur newborns revealed significant differences in the (TA)n repeat polymorphism in the UGT1A1 promoter region [10]. This study focuses specifically on the Tibetan population. Increasing evidence suggests that Tibetans, who have inhabited the Qinghai-Tibet Plateau for thousands of years, have developed unique genetic adaptations distinguishing them from other Chinese ethnic groups [11]. These adaptations likely involve metabolism-related genes, including UGT1A1.\u003c/p\u003e\u003cp\u003eUnderstanding UGT1A1 polymorphisms in Tibetan newborns will help clarify the genetic risk factors for hyperbilirubinemia in this population and fill the gap in comparative studies among ethnic groups. This knowledge will provide genetic and theoretical support for the early precise diagnosis and individualized prevention of neonatal jaundice in Tibetans. Accordingly, this study aims to compare UGT1A1 gene polymorphisms between Han and Tibetan newborns, explore their association with jaundice risk, and reveal ethnic-specific patterns in bilirubin metabolism, offering new insights to improve neonatal health.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003e\u003cb\u003eStudy subjects and sample collection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTibetan newborns were recruited from the Maternal and Child Health Hospital of Shannan City, located on the Tibetan Plateau in China (altitude 3,000–4,000 meters), between January 2024 and April 2025. Relevant clinical data were collected via the hospital’s case management system, including date of birth, sex, birth weight, mode of delivery, gestational age, Apgar scores, postnatal transcutaneous bilirubin (TcB) levels, hematological test results, and maternal information such as age, pre- and post-delivery weight, and laboratory test results. All data were reviewed by designated physicians. This study retrospectively analyzed clinical records of hospitalized neonates. Ethnicity was confirmed based on parent-reported demographic information (name and ethnicity). Newborns were included only if both parents were Tibetan, the mother had no major illness or genetic disease, and the neonate was a full-term singleton without significant birth defects or serious illness. Based on existing studies, most newborns develop jaundice within 2–3 days after birth, with bilirubin levels typically peaking around day 5 and then declining [12]. Therefore, TcB values from days 1 to 5 after birth were recorded. This study was approved by the Ethics Committees of Yangtze University and the participating hospital.\u003c/p\u003e\u003cp\u003eTo further explore the causes of neonatal jaundice in Tibet, potential confounding factors were excluded. Using more stringent criteria, we continuously collected clinical data from Tibetan newborns born between January and June 2024 at the Shannan Maternal and Child Health Hospital, and Han Chinese newborns born in the low-altitude region of Jingzhou, China (The two affiliated hospitals of Yangtze University). With informed consent from the mothers, umbilical cord blood samples were collected and stored at -80°C for subsequent genetic analysis. Inclusion criteria were: gestational age \u0026gt; 37 weeks, birth weight \u0026gt; 2.5 kg, no major birth defects or severe diseases, Apgar score of 10, and no maternal complications such as diabetes or hypertension. Based on the recorded bilirubin levels (TcB and/or total serum bilirubin, TSB), the neonates were divided into a case group and a control group. The case group included newborns whose TcB/TSB values were at or above the 95th percentile on the specific time-based TcB/TSB percentile charts established by the Chinese Multicenter Study Group for Neonatal Hyperbilirubinemia [13,14].\u003c/p\u003e\u003cp\u003e\u003cb\u003eUGT1A1 analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGenomic DNA was extracted from the collected EDTA-anticoagulated umbilical cord blood samples using the Tiangen DNA extraction kit. Polymerase Chain Reaction (PCR) amplification and direct sequencing were performed using five pairs of primers targeting the enhancer, promoter, all five exons, and exon-intron boundaries of the UGT1A1 gene, as detailed in our previous study[15,16].\u003c/p\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eDifferences in clinical and hematological data between Tibetan neonates with and without jaundice were compared. Categorical variables were analyzed using the Chi-square test or Fisher’s exact test, as appropriate. For continuous variables, independent samples t-tests were used for normally distributed data, while the Mann-Whitney U test was applied for non-normally distributed data. Neonatal jaundice status was treated as the dependent variable in a binary logistic regression analysis using the backward-LR method to identify relevant influencing factors. Gestational age, sex, and birth weight were retained in the model throughout to control for potential confounders.\u003c/p\u003e\u003cp\u003eCommon UGT1A1 mutations were summarized, and differences in mutation frequencies between the two ethnic groups were compared. Multivariate logistic regression analyses were then conducted to evaluate the association between specific UGT1A1 variants and the risk of neonatal jaundice, adjusting for potential confounders such as sex, gestational age, and birth weight. Variables with \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.100 were retained in the model, and UGT1A1 genotypes were forced into the model. For each UGT1A1 locus, association analyses were conducted under codominant, dominant, and recessive genetic models.\u003c/p\u003e\u003cp\u003eAll statistical analyses were performed using SPSS version 16.0, and \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eClinical data were collected from 121 Tibetan pregnant women and their newborns. Meanwhile, a total of 43 umbilical cord blood samples were collected from Tibetan neonates in high-altitude regions of China. Among the collected 43 samples, 13 were excluded based on the predefined criteria, leaving 30 samples for final analysis. Additionally, clinical data from 80 Han pregnant women and their newborns, as well as 30 cases of umbilical cord blood samples from Han neonates in the plain region of China, were collected as controls.\u003c/p\u003e\u003cp\u003e\u003cb\u003eClinical features of jaundice in Tibetan neonates\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBased on TSB and/or TcB levels, the 121 Tibetan neonates were divided into a jaundice group (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;68) and a non-jaundice group (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;53). A systematic comparison was conducted between the two groups regarding basic maternal and neonatal information, including maternal age, gestational age, blood pressure, altitude of residence, neonatal birth weight, and various hematological indicators. Preliminary results showed that mothers in the jaundice group had significantly higher mean red blood cell (RBC) counts (4.314\u0026thinsp;\u0026plusmn;\u0026thinsp;0.574 vs. 4.018\u0026thinsp;\u0026plusmn;\u0026thinsp;0.461, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) and white blood cell (WBC) counts (9.778\u0026thinsp;\u0026plusmn;\u0026thinsp;3.258 vs. 8.550\u0026thinsp;\u0026plusmn;\u0026thinsp;2.908, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036) than those in the non-jaundice group, suggesting a possible hypoxic adaptation or immune activation state in the jaundice group mothers. In contrast, maternal fasting blood glucose levels during the second trimester were significantly lower in the jaundice group than in the non-jaundice group (4.516\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6515 vs. 5.013\u0026thinsp;\u0026plusmn;\u0026thinsp;1.388, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032), indicating possible energy insufficiency during pregnancy among mothers of the jaundice group. In addition, from day 2 to day 5 after birth, TcB values were significantly higher in the jaundice group than in the non-jaundice group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting that early TcB monitoring may have predictive value for the development of jaundice. No significant differences were found between the two groups regarding neonatal sex, mode of delivery (cesarean section and/or vaginal delivery), or breastfeeding status ( Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTibetan Neonates with Jaundice Compared to Non-Jaundiced Neonates: Clinical Information and Hematological Data.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJaundice (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;68)\u003c/p\u003e\u003cp\u003eN(%)/Mean (SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-Jaundiced (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;53)\u003c/p\u003e\u003cp\u003eN(%)/Mean (SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean Difference\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eGeneral Information of Neonates\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex of neonates\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35 (51.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35 (66.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33 (48.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (34.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMode of delivery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCesarean section\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 (19.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (18.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVaginal delivery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55 (77.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (81.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFeeding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExclusive breast feeding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45 (86.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32 (97.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMixed feeding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (113.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (3.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBirthweight (g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3098.62 (399.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3159.92 (470.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e61.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-100.19, 222.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTcB (Day 1 After Birth) (mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.17 (2.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.87 (2.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.18, 0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTcB (Day 2 After Birth) (mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.15 (2.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.07 (2.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.90, -0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTcB (Day 3 After Birth) (mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.90 (2.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.22 (3.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.92, -0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTcB (Day 4 After Birth) (mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.37 (1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.57 (1.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.52, -1.071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTcB (Day 5 After Birth) (mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.32 (1.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.52 (1.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.57, -1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eMaternal general information and laboratory test\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30.34 (4.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.75 (4.376)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.29, 1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGestational age (day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e277.03 (8.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e277.28 (8.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.78, 3.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystolic blood pressure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e116.34 (15.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e117.72 (13.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.88, 6.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiastolic blood pressure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75.92 (10.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76.472 (10.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.23, 4.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRed blood cell count (RBC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.31 (0.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.02 (0.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.49, -0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin (Hb)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e133.33 (27.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e129.12 (24.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-4.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-14.04, 5.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean corpuscular volume (MCV)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90.45 (9.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90.24 (8.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.66, 3.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite blood cell count (WBC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.78 (3.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.55 (2.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.38, -0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophils/lymphocytes (NLR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.26 (2.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.57 (1.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.51, 0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet count (PLT)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e212.05 (54.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e207.19 (55.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-4.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-25.25, 15.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirst prenatal checkup FBG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.57 (0.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.64 (0.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.27, 0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecond-trimester FBG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.52 (0.652)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.01 (1.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.043, 0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecond-trimester 1hPBG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.29 (1.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.56 (1.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.40, 0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecond-trimester 2hPBG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.39 (0.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.53 (0.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.30, 0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.13 (10.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.82 (10.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-4.25, 3.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.18 (19.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.31 (19.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-9.07, 5.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal bilirubin (TBIL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81.02 (90.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72.34 (75.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-8.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-39.22, 21.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum total protein (TP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56.56 (7.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56.08 (8.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.46, 2.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbumin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35.28 (5.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.49 (4.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.68, 1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum creatinine (CRE)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53.05 (11.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52.87 (9.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-4.00, 3.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum uric acid (UA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e321.60 (69.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e312.22 (65.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-9.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-34.33, 15.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eNeonatal laboratory test results\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRed blood cell count (RBC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.87 (0.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.74 (0.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.35, 0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin (Hb)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e177.75 (20.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e168.60 (22.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-9.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-18.40, 0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean corpuscular volume (MCV)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e104.03 (5.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e103.49 (6.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.08, 1.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite blood cell count (WBC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.94(4.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.75 (4.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.16, 2.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet count (PLT)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e240.44 (57.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e253.44 (51.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-10.60, 36.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.13 (6.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.42 (5.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.37, 2.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56.68 (21.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56.26 (23.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-10.01, 9.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndirect bilirubin (IBIL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e152.92 (65.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e136.76 (65.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-59.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-85.43, -33.18\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum total protein (TP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51.70 (4.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51.61 (3.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.79, 1.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbumin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35.70 (2.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.91 (2.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.79, 0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum creatinine (CRE)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56.21 (13.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53.84 (13.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-8.26, 3.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum uric acid (UA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e338.30 (100.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e310.70 (86.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-27.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-68.37, 13.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: CI, confidence interval; d, days; SD, standard deviation; NS, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.1;\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eMultivariate Analysis of Risk Factors for Neonatal Jaundice in Tibetan Newborns\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAfter controlling for potential confounding factors and imputing missing values with means, the final model selected using the backward-LR method of logistic regression showed that maternal red blood cell count (OR\u0026thinsp;=\u0026thinsp;9.657, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and white blood cell count (OR\u0026thinsp;=\u0026thinsp;1.234, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038) were significantly positively associated with neonatal jaundice, while maternal mid-pregnancy fasting glucose level (OR\u0026thinsp;=\u0026thinsp;0.359, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035) and neonatal white blood cell count (OR\u0026thinsp;=\u0026thinsp;0.863, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027) were significantly negatively associated with jaundice (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate Logistic Regression Analysis of Factors Influencing Neonatal Jaundice in the Tibetan Region.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWald \u003cem\u003eX\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eOR\u003c/em\u003e 95% \u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGestational age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.909\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.95, 1.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex of neonate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.467\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.42, 2.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBirthweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.709\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.00, 1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal red blood cell count (RBC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.355\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.478\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.52, 9.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal white blood cell count (WBC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.854\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.02, 1.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal second-trimester FBG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.487\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.430\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.14, 0.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeonatal white blood cell count (WBC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.908\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.76, 0.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eComparative Analysis of Clinical Data Between Tibetan and Han Pregnant Women and Newborns\u003c/b\u003e\u003c/p\u003e\u003cp\u003eConsidering the potential impact of disease on bilirubin levels, we conducted a comparative analysis of clinical data from 36 non-hospitalized full-term healthy Tibetan newborns and 74 non-hospitalized full-term healthy Han newborns and their mothers. No significant differences were found between the two groups in maternal age, gestational age, or newborn birth weight. We observed that similar to Han newborns, the TcB levels in Tibetan newborns peaked on the 5th day after birth. Also, on days 1, 4, and 5 after birth, TcB levels showed no statistically significant differences between Tibetan and Han newborns. However, on days 2 and 3, TcB levels of Tibetan newborns were significantly lower as compared with Han newborns (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In addition, Tibetan mothers living at high altitudes had significantly higher systolic blood pressure (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042), alanine aminotransferase (ALT) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019), aspartate aminotransferase (AST) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and serum creatinine (CRE) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038) than Han mothers from lowland areas. While, mean corpuscular volume (MCV) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), WBC count (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018), neutrophil-to-lymphocyte ratio (NLR) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026), and platelet count (PLT) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022) were significantly lower in Tibetan mothers (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparative Analysis of demographic and clinical information in our studied Non-Hospitalized Tibetan in high-altitude and Han Chinese Neonates in low-altitude.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTibetan (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;36)\u003c/p\u003e\u003cp\u003eN(%)/Mean (SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHan (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;74)\u003c/p\u003e\u003cp\u003eN(%)/Mean (SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean Difference\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eGeneral Information on Neonates\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex of neonate (Case)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 (47.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34 (45.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 (52.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40 (54.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBirthweight (g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3103.42 (385.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3222.03 (552.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-118.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-321.80, 84.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTcB (Day 1 After Birth) (mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.40 (3.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.57 (2.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.23, 1.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTcB (Day 2 After Birth) (mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.76 (1.518)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.72 (2.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.75, -1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTcB (Day 3 After Birth) (mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.69 (1.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.68 (1.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.77, -0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTcB (Day 4 After Birth) (mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.93 (2.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.20 (2.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.22, 0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTcB (Day 5 After Birth) (mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.64 (2.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.89 (1.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.41, 0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeonatal jaundice (Case)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes (TcB\u0026thinsp;\u0026ge;\u0026thinsp;12.9mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 (40.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo (TcB\u0026thinsp;\u0026lt;\u0026thinsp;12.9mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16 (59.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35 (70%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eMaternal general information and laboratory test\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.08 (4.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.18 (4.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.83, 0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGestational age (day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e274.78 (6.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e273.35 (5.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.93, 3.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystolic blood pressure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e121.92 (12.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e117.38 (10.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.16, 8.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiastolic blood pressure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e77.17 (9.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e77.42 (7.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.60, 3.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRed blood cell count (RBC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.05 (0.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.00 (0.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.12, 0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin (Hb)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e120.47 (12.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e120.71 (11.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-5.10, 4.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean corpuscular volume (MCV)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85.48 (7.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e91.33 (5.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-5.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-8.28, -3.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite blood cell count (WBC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.08 (2.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.46 (2.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.51, -0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophils/lymphocytes (NLR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.18 (1.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.97 (1.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.48, -0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet count (PLT)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e185.79 (59.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e213.00 (55.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-27.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-50.48, -3.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.00 (10.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.16 (12.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.99, 10.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.44 (10.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.88 (7.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.02, 13.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal bilirubin (TBIL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.64 (4.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.97 (3.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.74, 2.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum total protein (TP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60.19 (4.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62.69 (13.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-7.19, 2.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbumin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.45 (3.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.90 (6.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.76, 1.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum creatinine (CRE)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54.30 (8.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50.24 (9.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.22, 7.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum uric acid (UA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e308.24 (56.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e285.59 (82.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-8.73, 54.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: CI, confidence interval; d, days; SD, standard deviation; NS, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.1;\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eUGT1A1 analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe analyzed the mutation frequencies of common UGT1A1 gene variants in our studied neonates. Similar to Han newborns, three types of UGT1A1 mutations were detected in the studied Tibetan population: the c.211G\u0026thinsp;\u0026gt;\u0026thinsp;A (G211A, UGT1A1*16) mutation in the exon region, the c.-3279T\u0026thinsp;\u0026gt;\u0026thinsp;G (T-3279G, UGT1A1*60) mutation in the enhancer region, and the (TA)n polymorphism in the promoter region (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). None of these polymorphisms significantly deviated from Hardy-Weinberg equilibrium (HWE). In our studied newborns, 41.4% of Tibetan newborns carried a mutation in the exon region of UGT1A1. Specifically, the homozygous mutation rate for c.211G\u0026thinsp;\u0026gt;\u0026thinsp;A was 6.9% (2/29), and the heterozygous mutation rate was 34.5% (10/29). Among Han newborns, the homozygous mutation rate at this site was 5.9% (2/34), and the heterozygous mutation rate was 23.5% (8/34). For the enhancer region mutation c.-3279T\u0026thinsp;\u0026gt;\u0026thinsp;G, the homozygous and heterozygous mutation rates in Tibetan newborns were 3.6% (1/28) and 28.6% (8/28), respectively. In contrast, Han newborns exhibited a higher total mutation rate at this site (66.7%), including a homozygous mutation rate of 18.2% (6/33) and a heterozygous mutation rate of 48.5% (16/33). Regarding the (TA)n polymorphism, only two genotypes - (TA)6/6 and (TA)6/7 (UGT1A1*28) - were detected in this study. The mutation frequency of (TA)n polymorphism in Tibetan newborns was 24.1% (7/29), while in Han newborns it was 20% (5/25).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThere were no statistically significant differences between Tibetan and Han newborns in the frequencies of the c.211G\u0026thinsp;\u0026gt;\u0026thinsp;A mutation or the (TA)n promoter polymorphism. However, the c.-3279T\u0026thinsp;\u0026gt;\u0026thinsp;G polymorphism showed a significant difference between the two ethnic groups, with the mutation rate in the Han population being 2.62 times higher than in the Tibetan population (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUGT1A1 c.-3275T\u0026thinsp;\u0026gt;\u0026thinsp;G, (TA)n, and c.211G\u0026thinsp;\u0026gt;\u0026thinsp;A Variants in Tibetan vs. Han Neonates.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eTibetan\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eHan\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eTibetan vs. Han\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eN (%)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eH\u0026minus;W\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eN (%)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eH\u0026minus;W\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMode\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eOR\u003c/em\u003e (95% \u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ec.211G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDominant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG/G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 (58.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24 (70.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (34.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8 (23.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1.42 (0.47, 4.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (6.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (5.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ec.-3279T\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDominant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0095\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT/T\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 (67.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (33.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT/G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (28.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 (48.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.23 (0.07, 0.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG/G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (3.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 (18.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(TA)\u003csub\u003en\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTA\u003csub\u003e6\u003c/sub\u003e/TA\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (75.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20 (80.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTA\u003csub\u003e6\u003c/sub\u003e/TA\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (24.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (20.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.43 (0.37, 5.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTA7/TA\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003ea\u003c/sup\u003e Hardy-Weinberg Equilibrium test p-value.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003eb\u003c/sup\u003e In the linear regression analysis, neonates with wild UGT1A1 genotype (i.e. G/G) were set as the reference group under the dominant genetic model assumption, while both those wildtypes and the heterozygous UGT1A1 variant carrier (i.e. G/G\u0026thinsp;+\u0026thinsp;G/A) were set as the reference group under the recessive model.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eUGT1A1 Variants and Neonatal Hyperbilirubinemia\u003c/b\u003e\u003c/p\u003e\u003cp\u003eLogistic regression analysis showed that after controlling for potential confounding factors such as sex, age, and ethnicity, the c.211G\u0026thinsp;\u0026gt;\u0026thinsp;A mutation in the UGT1A1 exon was significantly associated with an increased risk of hyperbilirubinemia in both Tibetan (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027) and Han (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0081) newborns. No significant association was found between the (TA)n polymorphism or the c.-3279T\u0026thinsp;\u0026gt;\u0026thinsp;G mutation and the increased risk of hyperbilirubinemia (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation between UGT1A1 Variants and Neonatal Hyperbilirubinemia under Different Genetic Model Assumptions.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eTibetan\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eHan\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMode\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eORadj\u003csup\u003eb\u003c/sup\u003e (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMode\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eORadj\u003csup\u003eb\u003c/sup\u003e (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMode\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eORadj\u003csup\u003eb\u003c/sup\u003e (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ec.211 G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDominant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRecessive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRecessive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.0031\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG/G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e11.98 (0.99, 145.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA (0.00, NA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNA (0.00, NA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(TA)n\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDominant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTA6/TA6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTA6/TA7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.67 (0.30, 23.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.59 (0.04, 9.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.05 (0.25, 4.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTA7/TA7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ec.-3279T\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverdominant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDominant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eOverdominant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT/T\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG/G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.29 (0.02, 3.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT/G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.86 (0.66, 35.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.89 (0.51, 7.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e In the linear regression analysis, neonates with wild UGT1A1 genotype (i.e. G/G) were set as the reference group under the dominant genetic model assumption, while both those wildtypes and the heterozygous UGT1A1 variant carrier (i.e. G/G\u0026thinsp;+\u0026thinsp;G/A) were set as the reference group under the recessive model.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e Adjusted for gender, birth week, and race.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe occurrence of neonatal jaundice is influenced by multiple factors and demonstrates marked ethnic and regional differences. Understanding its clinical characteristics and underlying causes is crucial for early diagnosis and intervention. Recent studies have highlighted regional and ethnic variations in the incidence of neonatal hyperbilirubinemia and their association with genetic factors [17\u0026ndash;20]. This study systematically characterizes neonatal jaundice in Tibetan newborns from high-altitude regions of China. For the first time, we explore the genetic basis of jaundice in this population by comparing hematological parameters and UGT1A1 gene polymorphisms - a key gene in bilirubin metabolism - between Tibetan newborns and Han newborns from lowland areas.\u003c/p\u003e\u003cp\u003eOur findings revealed no significant differences between the two groups in the incidence of neonatal jaundice or the carrier rate of the c.211G\u0026thinsp;\u0026gt;\u0026thinsp;A variant, a mutation prevalent in East Asia. However, the frequency of the c.-3275T\u0026thinsp;\u0026gt;\u0026thinsp;G variant in the UGT1A1 enhancer region was significantly lower in Tibetan newborns than in Han newborns.\u003c/p\u003e\u003cp\u003eAt high altitudes, increased RBC production is a common adaptation to hypoxia [21]. This adaptation may elevate maternal blood viscosity, reducing uterine artery (UtA) blood flow and impairing placental perfusion, potentially leading to fetal hypoxia [22]. In our cohort, mothers of jaundiced Tibetan newborns had significantly higher RBC counts than those of non-jaundiced newborns. This may be an important risk factor for jaundice in high-altitude settings and could serve as a clinical indicator. After adjusting for confounding variables, we observed a negative correlation between neonatal white blood cell count and jaundice occurrence, further suggesting that common causes of jaundice - such as ABO/Rh blood group incompatibility and infectious sepsis - may not be the primary etiologies for neonates in this region [23,24]. Overall, our study revealed that the hematological characteristics of jaundiced neonates in this region differ from those of typical neonatal jaundice cases in other lower-altitude populations.\u003c/p\u003e\u003cp\u003eWe also found that Tibetan newborns in the high-altitude region had significantly lower TcB levels than Han newborns in the low-altitude region on days 2 and 3 post-birth, though no differences were seen on the other three days ( day 1, day 4, and day 5). Bilirubin is primarily produced through heme degradation from senescent RBCs. Due to their structural immaturity, neonatal RBCs are more prone to oxidative stress, which can trigger eryptosis, a form of programmed red blood cell death analogous to apoptosis, ultimately leading to increased bilirubin production [25]. Hypoxia may enhance the antioxidant capacity of RBCs [26], and chronic high-altitude exposure in Tibetans may contribute to more stable RBCs and slower hemolysis.\u003c/p\u003e\u003cp\u003eIn addition, significant physiological differences were observed between Tibetan and Han mothers, likely reflecting the systemic effects of long-term high-altitude living. Tibetan mothers exhibited elevated systolic blood pressure, a cardiovascular adaptation to chronic hypoxia aimed at improving tissue oxygenation [27\u0026ndash;29]. Pregnancy itself induces circulatory stress [30], which may be intensified by hypoxic conditions. Dietary factors, such as higher caffeine intake, may also play a role [31]. Studies conducted in Peru (at 3400 meters) and in western Saudi Arabia (at 2177 meters) similarly found reduced white blood cell and neutrophil counts in high-altitude pregnant women, although platelet trends differed from ours [32,33]. Hypoxia-induced erythropoietin (EPO) secretion increases RBC production while reducing immature megakaryocytes, potentially lowering platelet counts [34]. These hematological responses remain complex and multifactorial.\u003c/p\u003e\u003cp\u003eTibetans\u0026rsquo; long-term adaptation to hypoxia and their unique diet may lead to differential gene expression and metabolic profiles, which in turn may affect bilirubin metabolism. The c.-3275T\u0026thinsp;\u0026gt;\u0026thinsp;G variant is located in the phenobarbital-responsive enhancer module (gtPBREM) of the UGT1A1 gene. This enhancer contains binding elements for the constitutive androstane receptor (CAR), pregnane X receptor (PXR), and glucocorticoid receptor (GR) [35], and is known to enhance phenobarbital sensitivity in patients with Gilbert syndrome (GS, OMIM #143500). This mutation can reduce UGT1A1 transcription and diminish regulatory effects from CAR [36], PXR, GR, and the aryl hydrocarbon receptor (AhR) [37,38], especially when coexisting with promoter variants. Hypoxia is known to suppress UGT1A1 expression through PXR and CAR pathways [39]. Beyond bilirubin metabolism, PXR and CAR also regulate energy homeostasis, including glucose and lipid metabolism [40,41]. Therefore, the lower frequency of c.-3275T\u0026thinsp;\u0026gt;\u0026thinsp;G in Tibetans may reflect adaptive selection under hypoxic conditions, balancing bilirubin risk and metabolic function. PXR has also been implicated in suppressing gluconeogenesis and lipid oxidation, potentially increasing type 2 diabetes risk [42\u0026ndash;44]. An increased density of PXR/CAR binding sites in UGT1A1 may alter PXR/CAR availability for other pathways, providing additional adaptive advantages in energy metabolism.\u003c/p\u003e\u003cp\u003eStudies from India [45] and Germany [46] have reported associations between c.-3275T\u0026thinsp;\u0026gt;\u0026thinsp;G and hyperbilirubinemia, while other studies from Poland and Pakistan found no such relationship [47,48]. Our findings align with the latter, showing no significant link between this mutation and neonatal jaundice in either Han or Tibetan newborns. This highlights the need for further investigation.\u003c/p\u003e\u003cp\u003eAdditionally, no differences were found between the two populations regarding the c.211G\u0026thinsp;\u0026gt;\u0026thinsp;A mutation or the (TA)n polymorphism. The c.211G\u0026thinsp;\u0026gt;\u0026thinsp;A variant is the most common genetic cause of Gilbert syndrome in East Asia [49,50], impairing UGT1A1 enzymatic activity and increasing bilirubin levels. In our study, both Han and Tibetan carriers were more likely to develop hyperbilirubinemia compared to wild-type individuals, consistent with previous reports [51\u0026ndash;53]. While the (TA)n polymorphism is a major cause of unconjugated hyperbilirubinemia in Caucasian and African populations [49,51], most studies in East Asia have not found a strong association. Some even suggest a protective effect of the (TA)7 variant in breastfed infants [16,51]. These findings emphasize the need to consider both genetic and environmental contexts when assessing jaundice risk, particularly in high-altitude regions.\u003c/p\u003e\u003cp\u003eIn conclusion, this study provides a preliminary assessment of \u003cem\u003eUGT1A1\u003c/em\u003e polymorphisms and their relationship to neonatal jaundice in Tibetan and Han newborns. While our findings suggest certain genetic differences, the limited sample size may restrict the generalizability of results. Future studies should involve larger, more diverse cohorts to further clarify the role of \u003cem\u003eUGT1A1\u003c/em\u003e variants in neonatal jaundice and inform targeted prevention strategies for ethnic minority populations in China.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, this study comprehensively analyzed the epidemiological features and related factors of neonatal jaundice in Tibetans, and several clinically significant hematological indicators were identified. This would provide new insights for early diagnosis and treatment in high-altitude regions. Furthermore, our preliminary investigation further elucidated the molecular mechanisms underlying jaundice in Tibetan newborns, revealing differences in UGT1A1 gene polymorphisms between Tibetan and Han newborns, which may be associated with Tibetan populations' adaptive changes to high-altitude environments. These findings offer a theoretical basis for individualized prevention and treatment of neonatal jaundice in Tibetan populations. Future research should involve larger sample sizes and more in-depth studies in this region to better guide clinical practice.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGilbert syndrome\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003egtPBREM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ephenobarbital-responsive enhancer module of \u003cem\u003eUGT1A1\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePXR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003epregnane X receptor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTcB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003etranscutaneous bilirubin\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTSB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003etotal serum bilirubin\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUGT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eUDP-glucuronosyltransferase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUtA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003euterine artery\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHui Yang and Quan Gong conceptualized and designed the study. Zhen Qu, Zhen Guo, Jun Chen, and Qin De were responsible for collecting umbilical cord blood samples from Tibetan newborns and providing electronic case report data. Qingsong Zeng, Meng Liao, Li Wang, and Jufang Tang were responsible for collecting umbilical cord blood samples from Han newborns and providing electronic case report data. Quan Gong, Jun Chen, and Xianwang Wang participated in the study management and supervision. Zhi Guo and Hui Yang participated in the sample collection and contributed to the statistical analysis of the data and the drafting of the initial manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNational Natural Science Foundation of China (Yang Hui), Grant number：81801509.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eShannan Municipal Science and Technology Program Project, Grant number: SNSBJKJJHXM2023012.\u003c/p\u003e\n\u003cp\u003eYangtze University Medical and Health Aid Tibet Project, Grant number：2023YZ01\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJingzhou City Science and Technology Plan Project, Grant number：2024HD18.\u003c/p\u003e\n\u003cp\u003ePreterm Screening and Mechanisms of Low Birth Weight in Fetuses in the Tibetan Plateau Region: Hubei Provincial Department of Education Project, Grant number：BXLBX0355.\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u003cstrong\u003eData Availability Statement\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe nucleotide sequence data generated in this study have been deposited in the GenBank database under accession numbers PV897292-PV897477 (https://www.ncbi.nlm.nih.gov/nucleotide/).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Yangtze University, the Medical Ethics Committee of Jingzhou First People\u0026apos;s Hospital(KY202351), and the Medical Ethics Committee of Shannan Maternal and Child Health Hospital.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all parents.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eThomas M, Greaves RF, Tingay DG, Loh TP, Ignjatovic V, Newall F, et al. Current and emerging technologies for the timely screening and diagnosis of neonatal jaundice. Crit Rev Clin Lab Sci. 2022;59(5):332-52. \u003c/li\u003e\n\u003cli\u003eLee B, Piersante T, Calkins KL. Neonatal hyperbilirubinemia. Pediatr Ann. 2022;51(6):e219-27. \u003c/li\u003e\n\u003cli\u003eHegyi T, Kleinfeld A. Neonatal hyperbilirubinemia and the role of unbound bilirubin. 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Pediatr Neonatol. 2020;61(5):506-12.\u003c/li\u003e\n\u003cli\u003eMehrad-Majd H, Haerian MS, Akhtari J, Ravanshad Y, Azarfar A, Mamouri G. Effects of Gly71Arg mutation in UGT1A1 gene on neonatal hyperbilirubinemia: a systematic review and meta-analysis. J Matern Fetal Neonatal Med. 2019;32(10):1575-85.\u003c/li\u003e\n\u003cli\u003eLiu W, Chang LW, Xie M, Li WB, Rong ZH, Wu L, et al. Correlation between UGT1A1 polymorphism and neonatal hyperbilirubinemia of neonates in Wuhan. J Huazhong Univ Sci Technolog Med Sci. 2017;37(5):740-3. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Hyperbilirubinemia, Neonate, Plateau Tibetans, UGT1A1","lastPublishedDoi":"10.21203/rs.3.rs-6947581/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6947581/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e Neonatal jaundice is a common health issue in infants. Tibetan newborns living at high altitudes may face greater risks due to chronic hypoxia and potential genetic factors. To better understand this issue, we conducted a study to analyze the clinical features of neonatal jaundice and investigate UDP-glucuronosyltransferase 1A1 (UGT1A1) gene mutations among Tibetan newborns living in the Tibetan Plateau Region of China.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e We collected umbilical cord blood samples, birth records, and maternal clinical data from healthy full-term Tibetan newborns born at Shannan Maternal and Child Health Hospital (3,000\u0026ndash;4,000 m altitude). As a comparison group, Han newborns (the predominant ethnic group in China) born and residing in low-altitude regions were also included. UGT1A1 polymorphisms were analyzed by PCR and sequencing.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e A total of 121 Tibetan and 80 Han newborns were included. Among Tibetan newborns, Higher maternal red blood cell (RBC) counts, white blood cell (WBC) counts, and lower mid-pregnancy glucose levels were associated with neonatal jaundice. Meanwhile, compared to Han newborns, Tibetan newborns exhibited lower transcutaneous bilirubin (TcB) levels on postnatal days 2 and 3(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), but no differences were observed on days 1, 4, or 5. Also, mothers of Tibetan newborns had higher blood pressure and liver/kidney function markers but lower MCV, WBC, NLR, and PLT (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) as compared with Han neonates. Furthermore, Homozygous UGT1A1 c.211G\u0026thinsp;\u0026gt;\u0026thinsp;A mutations were associated with increased jaundice risk in both groups, while the frequency of -c.-3279T\u0026thinsp;\u0026gt;\u0026thinsp;G mutation was lower in our studied Tibetan newborns (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023).\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e Tibetan neonates with UGT1A1 c.211G\u0026thinsp;\u0026gt;\u0026thinsp;A mutation or whose mothers have high RBC and WBC and low glucose levels may have a higher jaundice risk. The lower frequency of the c.-3279T\u0026thinsp;\u0026gt;\u0026thinsp;G mutation in Tibetans may indicate a genetic adaptation to high altitude, potentially reducing jaundice susceptibility.\u003c/p\u003e","manuscriptTitle":"Analysis of UGT1A1 Polymorphism and Clinical Risk Factors for Neonatal Jaundice in Chinese Tibetan Newborns: Comparison with Han Newborns","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-18 14:36:27","doi":"10.21203/rs.3.rs-6947581/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-09-14T18:07:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"37710826752309801597181928889584941404","date":"2025-09-03T19:06:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-15T07:34:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-10T12:22:21+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-09T10:21:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-09T09:14:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pediatrics","date":"2025-07-09T09:10:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c0a44c15-594c-4c56-84f0-673abdbd340f","owner":[],"postedDate":"July 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-07-18T14:36:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-18 14:36:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6947581","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6947581","identity":"rs-6947581","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}