A study on the clinical characteristics of overweight children in kindergartens in South China

A study on the clinical characteristics of overweight children in kindergartens in South China | 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 A study on the clinical characteristics of overweight children in kindergartens in South China Ping lin Hsieh, Dong-yu Zeng, Min-xi Xiao, Er-hei Dai, Yun Pan, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8546522/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Introduction Our goal was to explore the differences in clinical characteristics, including laboratory biochemical parameters, age, and sex, between overweight and nonoverweight children. Method In this study, 140 participants were 3- to 6-year-old children. Data were obtained from Chinese children at South China Normal University Affiliated Kindergarten from March 10 to 12, 2025. Weight, height, laboratory biochemical parameters, and liver elastography were measured. We divided the participants into 3 different groups according to their BMI, age, and sex. Results Compared with that in the nonoverweight group, HDL-C was significantly lower in the overweight group. The laboratory biochemical parameters TC, TG, and LDL-C were significantly greater in females than in males. The weight and height at baseline were significantly greater in the 5–6-year-old group than in the 3–4-year-old group, and HGB, GGT, TP, TC, and CREA were significantly greater in the laboratory biochemical parameters in the 5–6-year-old group than in the 3–4-year-old group. Conclusion We found that among preschool children aged 3-6 in Guangzhou, China, the prevalence of overweight was 22%, and the detection rate of NAFLD (assessed by CAP ≥225 dB/m) was 2.9%, and revealed significant differences in metabolic indicators and liver health parameters among children of different weight, sex, and age groups. In conclusion, this study emphasizes significant differences in metabolic indicators and liver health parameters during the preschool period and adopting targeted early health screening and prevention strategies addressing overweight, sex, and age-specific factors to curb the further development of childhood obesity and its related metabolic complications. Future research should incorporate longitudinal designs and expand the sample size to more comprehensively elucidate the dynamic developmental trajectory of liver and metabolic health in children. Introduction Overweight and obesity have emerged as critical global health challenges. Recent estimates indicate that approximately 10% of children worldwide are currently overweight or obese, including 22 million children under five years of age 1 . China has experienced a particularly dramatic increase, with pediatric overweight rates increasing to 15% by 2011—more than double the prevalence recorded in 1991 2 . Research has identified five primary determinants of childhood overweight and obesity in the Chinese context: (1) environmental influences, (2) individual dietary patterns and physical activity levels, (3) the interaction between genetic predispositions and environmental factors, (4) endocrine and metabolic disorders, and (5) the internal physiological environment 3 . Nonalcoholic fatty liver disease (NAFLD) encompasses a spectrum of liver disorders, ranging from simple steatosis to nonalcoholic steatohepatitis (NASH), fibrosis, and ultimately cirrhosis. It is clinically defined as macrovesicular hepatic steatosis, characterized by excessive fat accumulation exceeding 5% of hepatocytes in individuals without significant alcohol consumption 4 . Emerging evidence has demonstrated that NAFLD is strongly associated with metabolic abnormalities, particularly dyslipidemia and insulin resistance 5 , 6 . Without intervention, the condition may progress to more severe stages, including hepatic fibrosis and cirrhosis 7 , so screening for NAFLD is needed 8 . This study is a cross-sectional study. In this study, we observed body mass index (BMI), laboratory biochemical parameters and liver elastography data to determine the differences among the groups. Our goal was to explore the differences in clinical characteristics, including laboratory biochemical parameters, age, and sex, between overweight and nonoverweight children. These results highlight the clinical importance of early metabolic and hepatic screening in pediatric populations. Method Data were obtained from Chinese children at South China Normal University Affiliated Kindergarten from March 10 to 12, 2025. In this study, after 7 people without CAP and 2 people without physical examination data were excluded, a total of 140 children were included and used in the final analysis. This study was approved by the Medical Ethics Committee of The First Affiliated Hospital of Jinan University. For the minor participants, parents or legal guardians were informed, and consent was obtained. We ensured that all ethical guidelines for children were strictly followed. Weight and height were measured. Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters. A BMI over 16.5 is considered overweight 9 . In accordance with the WHO growth standards, they evaluated children before 5 years of age and between 5 years of age and 19 years of age 10 , and we also separated them in the same way as in our research. Liver elastography was performed via FibroScan® (Echosens) to measure the controlled attenuation parameter (CAP) and liver stiffness measurement (LSM). CAP quantified the increased attenuation of ultrasound waves as they passed through steatotic liver tissue compared with healthy liver tissue. LSM measures the speed of a shear wave generated on the surface of the skin. The velocity of the shear wave was calculated by measuring the time it took for the wave to propagate to a specific depth within the liver. For the procedure, the fasting participant remained in a supine position, with the probe placed in the intercostal space over the right hepatic lobe. A minimum of 10 consecutive valid measurements were taken at the same location, and the median value was recorded as the final result 11 . The liver of the cap was 225 or more and was considered steatotic 12 . Laboratory biochemical parameters, including liver function tests, routine blood tests, lipoprotein panels, creatinine (CREA), and glucose (Glu), were measured by Daan Gene Co., Ltd. Liver function tests included alanine aminotransferase (ALT), gamma-glutamyl transpeptidase (GGT), total protein (TP), albumin (ALB), and alkaline phosphatase (ALP) levels. Routine blood tests included white blood cell (WBC) count, absolute neutrophil count (NEUT), absolute lymphocyte count (LYMPH), red blood cell (RBC) count, hemoglobin (HGB), and platelet count (PLT). The lipoprotein panel included total cholesterol (TC) measured via cholesterol oxidase, triglyceride (TG) measured via triglyceride colorimetric assay, high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) measured via direct LDL-cholesterol measurement. Creatinine (CREA) was measured via an enzymatic creatinine assay. Glucose (Glu) was measured via the hexokinase method for blood glucose determination. The data were analyzed via SPSS software ver. 25.0 from SPSS Inc. in Chicago, Illinois, USA. The data are reported as the means (standard deviations). Independent t tests were used to compare differences between groups. Significance was defined as a p value less than 0.05. Results overweight and non overweight A total of 140 subjects participated in this study, comprising 110 individuals in the nonoverweight group and 30 in the overweight group. No adverse events were reported during the trial. The baseline characteristics of the study population are presented in Table 1 . Weight and BMI were significantly greater in the overweight group than in the nonoverweight group (p < 0.05), whereas height was slightly lower but not significantly different. The CAP values tended to be greater in the overweight group, and the E values were lower, although these differences did not reach statistical significance. Notably, four participants (three males and one female) presented CAP values ≥ 225 dB/m (ranging from 225–247), accounting for 2.9% of the total cohort. Table 1 Characteristic baseline of nonobese and obese group Patient characteristic Total (n = 140) non overweight (n = 110) (78%) overweight (n = 30) (22%) P value Age 4.53(0.97) 6(4.56) 4.40(1.00) 0.41 Sex 0.57 Male 87(62%) 67(60.9%) 20(66.7%) Female 53(38%) 43(39.1%) 10(33.3%) Weight (kg) 19.17(3.32) 18.35(2.51) 22.16(4.17) 0.00 Height (m) 1.11(0.07) 1.10(0.07) 1.12(0.08) 0.43 BMI 15.58(1.48) 15.01(0.87) 17.67(1.38) 0.00 CAP (dB/m) 165.65(28.17) 165.31(28.14) 166.90(28.68) 0.79 E(Kpa) 4.16(0.96) 4.24(0.97) 3.87(0.86) 0.06 Liver function tests and routine blood tests were performed in this study. Although most indices were lower in the overweight group than in the nonoverweight group, except for GGT, which was greater, none of these differences reached statistical significance (p > 0.05). Analysis of the lipid panel revealed lower levels of TC, HDL-C, LDL-C, and Glu in the overweight group, along with higher CREA and comparable TG levels. However, only HDL-C was significantly lower in the overweight group (p value = 0.002). Table 2 Laboratory biochemical parameters of nonobese and obese group Patient characteristic Total (n = 140) non overweight (n = 110) (78%) overweight (n = 30) (22%) P value WBC (10^9/L) 6.99(1.85) 7.03(1.90) 6.83(1.57) 0.60 NEUT (10^9/L) 3.22(1.48) 3.24(1.55) 3.12(1.21) 0.69 LYMPH (10^9/L) 3.11(0.92) 3.11(0.90) 3.09(1.01) 0.91 RBC (10^12/L) 4.59(0.39) 4.60(0.40) 4.56(0.35) 0.62 HGB(g/L) 124.36(8.24) 124.39(8.55) 124.23(7.15) 0.93 PLT(10^9/L) 304.11(63.86) 309.22(63.55) 285.40(62.51) 0.07 ALT(U/L) 14.31(4.30) 14.33(4.63) 14.27(2.85) 0.95 GGT(U/L) 13.56(2.43) 13.52(2.41) 13.73(2.55) 0.67 TP(g/L) 66.75(3.24) 66.93(2.95) 66.11(4.15) 0.32 ALB(g/L) 44.67(2.10) 44.72(2.11) 44.51(2.10) 0.63 ALP(U/L) 228.87(66.79) 229.38(71.02) 227.00(49.16) 0.86 TC (mmol) 4.36(0.81) 4.41(0.80) 4.15(0.81) 0.11 TG (mmol) 0.78(0.42) 0.78(0.43) 0.78(0.34) 1.00 HDL-C(mmol) 1.54(0.29) 1.58(0.29) 1.43(0.28) 0.02 LDL-C(mmol) 2.21(0.65) 2.23(0.63) 2.13(0.72) 0.48 CREA(µmol/L) 34.70(4.51) 34.40(4.58) 35.80(4.13) 0.13 Glu(mmol) 4.99(0.82) 5.00(0.83) 4.92(0.78) 0.60 Sex-Specific Analysis The participants were stratified by sex, with 53 individuals in the female group and 87 in the male group. No significant differences were observed between sexes in terms of age, weight, height, or BMI (p > 0.05). While both the CAP and E values were numerically greater in females than in males, these differences did not reach statistical significance. Table 3 Characteristic baseline of male and female Patient characteristic Total (n = 140) male (n = 87) (62.1%) female (n = 53) (37.9%) P value Age 4.53(0.97) 4.52(0.987) 4.55(0.952) 0.86 Weight (kg) 19.17(3.32) 19.46 (3.39) 18.69 (3.18) 0.19 Height (m) 1.11(0.07) 1.11 (0.07) 1.1 (0.07) 0.32 BMI 15.58(1.48) 15.69 (1.53) 15.4 (1.39) 0.27 CAP (dB/m) 165.65(28.17) 165.31 (28.55) 166.21 (27.79) 0.86 E(Kpa) 4.16(0.96) 4.13 (0.98) 4.22 (0.92) 0.62 No statistically significant differences were observed in liver function tests or complete blood count parameters between the sexes. However, females presented numerically lower values for ALT, ALP, NEUT, and HGB, although these differences did not reach statistical significance (p > 0.05). Significant sex-based disparities were noted in the lipid panel: TC (P value = 0.00), TG (P value = 0.049), and LDL-C (P value = 0.00) were significantly elevated in females compared with males. While HDL-C levels were lower in females and creatinine (CREA) and fasting glucose (Glu) tended to be lower, these differences were not statistically significant. Table 4 Laboratory biochemical parameters of male and female Patient characteristic Total (n = 140) male (n = 87) (62.1%) female (n = 53) (37.9%) P value WBC (10^9/L) 6.99(1.85) 6.97 (1.72) 7.02 (2.05) 0.87 NEUT (10^9/L) 44.73(11.28) 44.92 (11.12) 44.42 (11.63) 0.80 LYMPH (10^9/L) 3.11(0.92) 3.1 (0.91) 3.12 (0.94) 0.88 RBC (10^12/L) 4.59(0.39) 4.57 (0.31) 4.63 (0.49) 0.42 HGB(g/L) 124.36(8.24) 124.4 (7.55) 124.28 (9.35) 0.93 PLT (10^9/L) 304.11(63.86) 302.59 (61.57) 306.62 (67.98) 0.72 ALT(U/L) 14.31(4.30) 14.45 (3.59) 14.09 (5.29) 0.64 GGT(U/L) 13.56(2.43) 13.59 (2.58) 13.53 (2.2) 0.89 TP(g/L) 66.75(3.24) 66.34 (3.39) 67.43 (2.89) 0.05 ALB(g/L) 44.67(2.10) 44.42 (2.13) 45.08 (2.01) 0.07 ALP(U/L) 228.87(66.79) 230.57 (77.79) 226.08 (43.61) 0.70 TC (mmol) 4.36(0.81) 4.18 (0.62) 4.64 (0.99) 0.00 TG (mmol) 0.78(0.42) 0.72 (0.26) 0.87 (0.58) 0.049 HDL-C(mmol) 1.54(0.29) 1.54 (0.26) 1.55 (0.34) 0.81 LDL-C(mmol) 2.21(0.65) 2.07 (0.52) 2.44 (0.77) 0.00 CREA(µmol/L) 34.70(4.51) 35.15 (4.85) 33.96 (3.81) 0.13 Glu(mmol) 4.99(0.82) 5.06 (0.74) 4.86 (0.93) 0.15 3–4-year-old and 5–6-year-old The participants were categorized into two age groups: 3–4 years, with 66 individuals, and 5–6 years, with 74 individuals. Compared with the younger group, the older group presented significantly greater weight and height ( p 0.05 ). Liver assessment revealed that the 5–6-year-old group had numerically lower CAP values and higher E values than did the 3–4-year-old group, although these differences did not reach statistical significance ( p > 0.05 ). Table 5 Characteristic baseline of 3-4-year-old and 5-6-year-old group Patient characteristic Total (n = 140) 3–4-year-old (n = 66 ) (47.1%) 5–6-year-old (n = 74) (52.9%) P value Sex 0.49 Male 87(62.1%) 43(65.2%) 44(59.5%) Female 53(37.9%) 23(34.8%) 30(49.5%) Weight (kg) 19.17(3.32) 17.57(2.33) 20.59(3.44) < 0.01 Height (m) 1.11(0.07) 1.06(0.06) 1.15(0.05) < 0.01 BMI 15.58(1.48) 15.46(1.90) 15.40(1.53) 0.22 CAP (dB/m) 165.65(28.17) 167.65(31.59) 163.86(24.80) 0.43 E(Kpa) 4.16(0.96) 4.14(0.94) 4.18(0.97) 0.83 The 5–6-year-old group presented significantly elevated levels of GGT (p value = 0.01), TP (p value < 0.01), and HGB (p value = 0.01). Conversely, ALT, WBC, NEUT, and LYMPH were lower. In the lipid panel, TC (p value = 0.049) was significantly greater in the 5–6-year-old group. HDL-C and LDL-C levels were also higher and TG levels were lower in the 5–6-year-old group than in the control group, but the results were not significantly different. CREA (p value = 0.03) was significantly greater in the 5–6-year-old group, and Glu was significantly lower. Table 6 Laboratory biochemical parameters of 3-4-year-old and 5-6-year-old group Patient characteristic Total (n = 140) 3–4 years old (n = 66 ) (47.1%) 5–6 years old (n = 74) (52.9%) P value WBC(10^9/L) 6.99(1.85) 7.19(1.91) 6.81(1.79) 0.23 NEUT#(10^9/L) 3.22(1.48) 3.31(1.49) 3.13(1.48) 0.49 LYMPH#(10^9/L) 3.11(0.92) 3.24(0.97) 2.99(0.87) 0.11 RBC(10^12/L) 4.59(0.39) 4.57(0.42) 4.61(0.36) 0.58 HGB(g/L) 124.36(8.24) 122.33(8.06) 126.16(8.03) 0.01 PLT(10^9/L) 304.11(63.86) 300.24(65.46) 307.57(62.65) 0.50 ALT(U/L) 14.31(4.30) 14.36(4.12) 14.27(4.48) 0.90 GGT(U/L) 13.56(2.43) 12.83(2.22) 14.22(2.45) 0.01 TP(g/L) 66.75(3.24) 65.52(3.37) 67.85(2.71) < 0.01 ALB(g/L) 44.67(2.10) 44.40(2.17) 44.91(2.02) 0.15 ALP(U/L) 228.87(66.79) 228.77(85.73) 228.96(44.14) 0.99 TC (mmol) 4.36(0.81) 4.21(0.71) 4.49(0.86) 0.04 TG (mmol) 0.78(0.42) 0.79(0.52) 0.76(0.29) 0.68 HDL-C(mmol) 1.54(0.29) 1.52(0.28) 1.57(0.30) 0.32 LDL-C(mmol) 2.21(0.65) 2.10(0.59) 2.30(0.69) 0.07 CREA(µmol/L) 34.70(4.51) 33.85(4.32) 35.46(4.58) 0.03 Glu(mmol) 4.99(0.82) 5.05(0.74) 4.92(0.89) 0.35 Discussion This study included 140 participants aged 3 to 6 years. The children were categorized on the basis of three criteria: body mass index (BMI), age, and sex. Comparative analyses revealed that HDL-C levels were significantly lower in the overweight group than in the nonoverweight group. Sex-based differences revealed that females had significantly higher levels of TC, TG, and LDL-C than males did in terms of laboratory biochemical parameters. Age-related comparisons demonstrated that compared with 3–4-year-olds, 5–6-year-olds had significantly greater weight and height at baseline. Additionally, laboratory parameters, including HGB, GGT, TP, TC, and CREA, were significantly greater in the 5–6-year-old group than in the younger age group. Previous studies reported that the prevalence of overweight and obesity among preschool-aged children in the United States was 26%, with overweight defined as a BMI between the 85th and 95th percentiles for age and sex and obesity defined as a BMI above the 95th percentile 13 . In contrast, a meta-analysis of Chinese studies revealed a combined overweight and obesity detection rate of 19% in preschool children (11% overweight and 8% obese) 14 . In our study, overweight children accounted for 22% of all participants, whereas central adipose obesity (CAP) was observed in 2.9% of the participants, indicating a prevalence higher than that reported in local Chinese studies but lower than that reported in the U.S. Excessive consumption of sugar-sweetened beverages has been identified as a contributing factor to obesity in both U.S. 15 and Chinese 16 studies, suggesting shared etiological factors across these populations. The discrepancy between our findings and those of previous Chinese studies may be attributed to regional variations, as our study focused exclusively on Guangzhou. Furthermore, research utilizing body fat percentage measurements has revealed distinct geographical clustering in children's physical constitution, with obesity hotspots predominantly in Northeast China and a lower prevalence in southern regions 17 . A previous study revealed that the prevalence of overweight and obesity in preschool-aged children in the USA was 26%, according to the definition of overweight as a BMI between the 85th and 95th percentiles for age and sex and above the 95th percentile for obesity. In another meta-analysis, the overweight and obesity detection rate of preschool children in China in this study was 19% (the overweight detection rate was 11%, and the obesity detection rate was 8%). In our study, overweight children accounted for 22% of all participants, and CAP accounted for 2.9% of all participants, which was higher than that reported in the USA and higher than that reported in a local study. Although obesity is influenced by the interplay of genetic, epigenetic, metagenomic, and environmental factors 18 , genetic predisposition may be the most significant contributor in this study. Given that the kindergarten employed a unified management strategy, environmental influences were relatively consistent across participants, minimizing the variability in external factors. Polygenic obesity is caused by numerous SNPs, the pattern of heritability is similar to that of other diseases and complex traits, and it is the most common disease in childhood 19 . Variation in 2 genes, MC4R and FTO, has been recognized, and insulin-induced gene 2 is still being studied 20 . Genome-wide association studies (GWASs) have revealed that more than 52 genetic loci are associated with obesity susceptibility 21 . Our findings demonstrate that even under uniform environmental conditions, phenotypic variability persists, suggesting a strong genetic component. However, further research is needed to elucidate the specific genes driving these observed differences. HDL-C was significantly different across BMI groups only in our study. This finding aligns with previous research indicating that reduced HDL-C levels are a characteristic feature of NAFLD 22 . According to Chinese pediatric standards, the critical HDL-C threshold for children ranges from 1.03 mmol/L to 1.16 mmol/L, with levels below 1.03 mmol/L classified as abnormally low 23 . In our cohort, the nonobese group had a mean HDL-C level of 1.58 (0.29) mmol/L, which was significantly greater than that of the obese group (1.43 (0.28) mmol/L, p value < 0.05). Comparatively, a study of preschool children in Mexico City reported HDL-C levels of 86.3 (3.1) mg/dL (2.23 (0.08) mmol/L) in obese children and 103.1 (7.6) mg/dL (2.66 (0.18) mmol/L) in controls 24 , whereas a Shenzhen-based study reported a mean HDL-C level of 1.99 (0.39) mmol/L 25 . Notably, HDL-C values in these foreign studies were approximately twice as high as those reported in Chinese studies, suggesting potential ethnic or regional disparities. Genetic factors are likely influential, as they are known to significantly modulate HDL-C concentrations 26 . Guidelines for adults recommend maintaining HDL-C levels above 1.0 mmol/L 26 , closely mirroring the pediatric threshold of 1.03 mmol/L 23 . This consistency implies that the physiological requirement for HDL-C remains relatively stable across all age groups in China. In our study, all indices of blood lipid levels and total cholesterol (TC), triglyceride (TG), and low-density lipoprotein (LDL) levels were greater in females than in males, and all indices except high-density lipoprotein cholesterol (HDL-C) were significantly different. In a previous study, each index in males at P50 was approximately 150, 85, and 45 mg/dL (3.885, 2.202, and 1.166 mmol/L) for TC, HDL, and LDL, respectively, at P50 was approximately 150, 85, and 50 mg/dL for HDL, LDL (3.885, 2.202, and 1.295 mmol/L) at 5 and 6 years of age, and each index at P50 in females was approximately 150, 90, and 45 mg/dL (3.885, 2.331, and 1.166 mmol/L) for TC, HDL, and LDL at 3 and 4 years of age and 155, 90, and 50 mg/dL (4.015, 2.331, and 1.295 mmol/L) for TC, HDL, and LDL at 5 and 6 years of age 27 , which supported our findings. Our study revealed significantly higher levels of HGB, GGT, TP, TC, and CREA in the older children (5–6 years) than in the younger children (3–4 years). These findings align with the literature showing a positive correlation between GGT levels and age in pediatric populations 28 . The age-related increase in GGT observed in our cohort further supports this established relationship. Our results showing higher TC concentrations in older children are consistent with those of previous Chinese studies 29 . These studies reported that females exhibit a gradual increase in TC with age, whereas males show a transient decrease at age 5 but maintain higher mean TC levels in the 5–6-year-old group than in the 3–4-year-old group. Notably, our study provides new evidence of this age-dependent pattern in both GGT and TC levels, specifically within the 3–6-year age range. The consistent observation of elevated biomarkers in older preschool-aged children suggests developmental changes in metabolic and physiological processes during this critical growth period. These findings highlight the importance of considering age-specific reference ranges for clinical biomarkers in early childhood. Further research is needed to elucidate the underlying mechanisms driving these age-related variations in pediatric biochemical parameters. Our liver elastography results showed distinct patterns from those of previous studies regarding hepatic steatosis and fibrosis markers in preschool-aged children. While existing research has revealed no correlation between CAP and LSM but has identified a positive association between LSM and age 30 and has demonstrated that obese individuals typically present elevated CAP values that are positively correlated with ALT levels 31 , our study revealed several noteworthy differences. First, we observed lower CAP values in older children (5–6 years) than in their younger counterparts (3–4 years). Second, although overweight children presented higher CAP values, as expected, we found higher ALT levels in nonoverweight children, indicating that there was no positive correlation between these two indices in our cohort. These discrepancies may stem primarily from the narrow 3–6-year age range of our study population, suggesting that the relationships between hepatic steatosis markers and liver enzymes may follow different patterns in early childhood than established adult correlations do. The developing liver physiology during these formative preschool years likely undergoes dynamic changes that differentially affect these biomarkers, highlighting the need for age-specific reference values and further investigations across broader pediatric age ranges to fully elucidate these relationships. This study has two major limitations that should be addressed in future research. First, the cross-sectional design lacks longitudinal follow-up data, limiting our ability to assess long-term effects. Second, the sample may not fully represent South China, as the data were collected from only one kindergarten. Future studies should incorporate longitudinal tracking and expand recruitment to multiple kindergartens to increase generalizability. Conclusion We found that among preschool children aged 3–6 in Guangzhou, China, the prevalence of overweight was 22%, and the detection rate of NAFLD (assessed by CAP ≥ 225 dB/m) was 2.9%, and revealed significant differences in metabolic indicators and liver health parameters among children of different weight, sex, and age groups. In Overweight and Metabolic Abnormalities, Overweight children exhibited significantly lower High-Density Lipoprotein Cholesterol levels. This suggests that even in early childhood, being overweight is already associated with an unfavorable lipid profile, increasing the risk for future metabolic diseases. In sex-based differences, regarding blood lipids, female children had significantly higher levels of Total Cholesterol, Triglycerides, and Low-Density Lipoprotein Cholesterol than males. This indicates that sex is an important factor influencing lipid levels in preschool children and should be considered in early health screening and interventions. In age-related physiological changes, compared to 3-4-year-olds, children aged 5–6 years had significantly greater weight and height, as well as higher levels of several biochemical parameters, including Hemoglobin, Gamma-Glutamyl Transpeptidase, Total Protein, Total Cholesterol, and Creatinine. This reflects the normal physiological maturation process during a critical period of child growth and development, underscoring the importance of using age-specific reference values. In early signals of Liver Health, although liver elastography parameters did not show statistically significant differences between groups, the observed trend of higher CAP values in overweight children, coupled with the fact that a small number of children already met the criteria for hepatic steatosis, highlights the clinical importance of early liver and metabolic screening in the pediatric population. In conclusion, this study emphasizes significant differences in metabolic indicators and liver health parameters during the preschool period and adopting targeted early health screening and prevention strategies addressing overweight, sex, and age-specific factors to curb the further development of childhood obesity and its related metabolic complications. Future research should incorporate longitudinal designs and expand the sample size to more comprehensively elucidate the dynamic developmental trajectory of liver and metabolic health in children. Declarations Ethics approval and consent to participate (include Helsinki statement under it) Consent for publication Our research followed Helsinki statement, and the parents of children have provided a written informed consent to publication of this article. Consent for publication Not applicable Funding The Medical Science Research Project of Hebei Province (No. 20260984) Author Contribution Ping-lin Hsieh, Dong-yu Zeng, Min-xi Xiao, Er-hei Dai, Yun Pan, Liu Yang, Li-li Zhang, Min-ran Li, and Yan Li searched the literature and conceived the study. Min-ran Li and Yan Li designed the study and performed the experiments. Min-ran Li and Ping-lin Hsieh interpreted the results and drafted the report. Ping-lin Hsieh, Dong-yu, Zeng, Min-xi Xiao, and Yan Li performed the experiments and collected the data. Ping-lin Hsieh, Dong-yu Zeng, Min-xi Xiao, Er-hei Dai, Yun Pan, Liu Yang, Li-li Zhang, Min-ran Li, and Yan Li analyzed the data and wrote the manuscript. Data Availability All data supporting the findings of this study are available within the paper and its Supplementary Information. References Lanigan J, Barber S, Singhal A. Prevention of obesity in preschool children. Proceedings of the Nutrition Society. 2010;69(2):204–210. 10.1017/S0029665110000029 Tang A, Ji M, Zhang Y, et al. Dietary Behaviors and Caregiver Perceptions of Overweight and Obesity among Chinese Preschool Children. Int J Environ Res Public Health. 2018;15(4). 10.3390/ijerph15040716 . (In eng). Chen JJ, Jiang YR. [Standardized diagnosis and assessment of overweight or obesity in children]. Zhonghua Er Ke Za Zhi. 2024;62(5):494–6. 10.3760/cma.j.cn112140-20240130-00084 . (In chi). Liyanagedera S, Williams RP, Veraldi S, Nobili V, Mann JP. The pharmacological management of NAFLD in children and adolescents. Expert Rev Clin Pharmacol. 2017;10(11):1225–37. 10.1080/17512433.2017.1365599 . Anwar SD, Foster C, Ashraf A. Lipid Disorders and Metabolic-Associated Fatty Liver Disease. Endocrinol Metab Clin North Am. 2023;52(3):445–57. 10.1016/j.ecl.2023.01.003 . (In eng). Solis-Herrera C, Triplitt C, Cersosimo E, DeFronzo RA. Pathogenesis of Type 2 Diabetes Mellitus. In: Feingold KR, Ahmed SF, Anawalt B, et al. editors. Endotext. South Dartmouth (MA): MDText.com, Inc. Copyright © 2000–2025. MDText.com, Inc.; 2000. Bozic MA, Subbarao G, Molleston JP. Pediatric Nonalcoholic Fatty Liver Disease. Nutr Clin Pract. 2013;28(4):448–58. https://doi.org/10.1177/0884533613489153 . Chan SL, Sun H-C, Xu Y et al. The Lancet Commission on addressing the global hepatocellular carcinoma burden: comprehensive strategies from prevention to treatment. The Lancet. 10.1016/S0140-6736(25)01042-6 Li HAJ. Chengye %A Zong, Xinnan %A Zhang, Yaqin. Growth Curves of Body Mass Index for Chinese Children and Adolescents Aged 0–18 Years. Chin J Pediatr 2009;47(7) ( https://qikan.cqvip.com/Qikan/Article/Detail?id=31718265 ). Child growth standards. ( https://www.who.int/zh/news-room/questions-and-answers/item/child-growth-standards ). Yang L, Lin Y, Zhu YF, Zhu YY, Liang ZM, Wu GS. Controlled attenuation parameter in the diagnosis of different liver steatosis groups in children with obesity. Pediatr Obes. 2022;17(6):e12893. 10.1111/ijpo.12893 . (In eng). Ferraioli G. Quantitative assessment of liver steatosis using ultrasound controlled attenuation parameter (Echosens). J Med Ultrason (2001) 2021;48(4):489–495. (In eng). 10.1007/s10396-021-01106-1 Kerns J, Fisher M. Epidemiology, pathophysiology and etiology of obesity in children and adolescents. Curr Probl Pediatr Adolesc Health Care. 2020;50(9):100869. https://doi.org/10.1016/j.cppeds.2020.100869 . Feng Y, Wang F, Zhang S. Meta-analysis of the Detection Rate of Overweight and Obesity among Preschool Children in China. J Prev Med Inform. 2025;41(12):1681–90. 10.19971/j.cnki.1006-4028.240531 . Wang YC, Bleich SN, Gortmaker SL. Increasing Caloric Contribution From Sugar-Sweetened Beverages and 100% Fruit Juices Among US Children and Adolescents, 1988–2004. Pediatrics. 2008;121(6):e1604–14. 10.1542/peds.2007-2834 . Report on Nutrition and Chronic Disease Status of Chinese Residents. (2020). Acta Nutrimenta Sinica 2020;42(06):521. ( https://kns.cnki.net/kcms2/article/abstract?v=YQFK0XGjx42faqt38Q49v0AgUpD1Ows_rHT7IPuA4XgvgDvi-wHF1AyuRlTyWvq8mN0PxIuitMkIhTO60qypAfKd2DxkR49bxSBk5c-Ox8TzMK3yJfykWkyOmdzvzTmhyEWsx4FvTZy1b6UiaKdg1UxyBvAyZwOc_0fQ9OSv6LKiTGvT1iNhnfpGRfXsXroE&uniplatform=NZKPT&language=CHS ). Wang C, Wang M, Feng Q, et al. National Survey on the Prevalence and Geospatial Variation of Body Fat Percentage Among Preschoolers – 31 PLADs, China, 2020. China CDC Wkly. 2025;7(19):650–7. 10.46234/ccdcw2025.107 . (In eng). Thaker VV, GENETIC, AND EPIGENETIC CAUSES OF OBESITY. Adolesc Med State Art Rev. 2017;28(2):379–405. (In eng). Panera N, Mandato C, Crudele A, Bertrando S, Vajro P, Alisi A. Genetics, epigenetics and transgenerational transmission of obesity in children. Front Endocrinol (Lausanne). 2022;13:1006008. 10.3389/fendo.2022.1006008 . (In eng). Concepción-Zavaleta MJ, Quiroz-Aldave JE, Durand-Vásquez MC, et al. A comprehensive review of genetic causes of obesity. World J Pediatr. 2024;20(1):26–39. 10.1007/s12519-023-00757-z . Loos RJF. Genetic determinants of common obesity and their value in prediction. Best Pract Res Clin Endocrinol Metab. 2012;26(2):211–26. https://doi.org/10.1016/j.beem.2011.11.003 . Deprince A, Haas JT, Staels B. Dysregulated lipid metabolism links NAFLD to cardiovascular disease. Mol Metab. 2020;42:101092. 10.1016/j.molmet.2020.101092 . (In eng). Rare Disease Group SCoP, Chinese Medical Association, Cardiovascular Group SCoP, Chinese Medical Association, Child Health Care Group SCoP, Chinese Medical Association. Expert Consensus on Diagnosis and Treatment of Pediatric Dyslipidemia (2022). Chin J Pediatr 2022;60(7) ( https://cstj.cqvip.com/Qikan/Article/Detail?id=7107667594 ). Carmona-Montesinos E, Ruiz-Fragoso Z, Ponce-Hinojosa G, Rivas-Arancibia S, CHANGES IN C-REACTIVE, PROTEIN AND BIOCHEMICAL PROFILE IN PRESCHOOL CHILDREN WITH OBESITY. Nutr Hosp. 2015;32(4):1548–53. 10.3305/nh.2015.32.4.9569 . (In eng). Zheng J, Chen Y, Han X, Wang Q, Xi Y, Wang Y. A Study on Blood Lipid Levels and Influencing Factors among Preschool Children in Shenzhen. Chin J Child Health Care 2008(01):74–6. ( https://kns.cnki.net/kcms2/article/abstract?v=YQFK0XGjx432SPU9 DgfOl5FlsytNgZJ8KG-asOhj2pVpoOgEqhsrLdiF7SOTZw6KBpJCo-nzOOtLn8bijXSCCjWyTPWM-Ka6YH0uBc_cYi_qhYrk-XcE-zSMb7YgsvEeg-t__tfdoyZ5ku_ifpAWhxXRMtG4cZTrtt0cxNm7pwAQezUBF3a5Ww==&uniplatform=NZKPT&language=CHS). Guidelines JECftRoCBLM. Chinese Blood Lipid Management Guidelines. (2023). Chinese Circulation Journal 2023;38(3):237–271. Interator H, Lebenthal Y, Hoshen M, et al. Distinct Lipoprotein Curves in Normal Weight, Overweight, and Obese Children and Adolescents. J Pediatr Gastroenterol Nutr. 2017;65(6):673–80. https://doi.org/10.1097/MPG.0000000000001674 . Bussler S, Vogel M, Pietzner D, et al. New pediatric percentiles of liver enzyme serum levels (alanine aminotransferase, aspartate aminotransferase, γ-glutamyltransferase): Effects of age, sex, body mass index, and pubertal stage. Hepatology. 2018;68(4):1319–30. 10.1002/hep.29542 . (In eng). Skinner AC, Steiner MJ, Chung AE, Perrin EM. Cholesterol curves to identify population norms by age and sex in healthy weight children. Clin Pediatr (Phila). 2012;51(3):233–7. 10.1177/0009922811430344 . (In eng). Tokuhara D, Cho Y, Shintaku H. Transient Elastography-Based Liver Stiffness Age-Dependently Increases in Children. PLoS ONE. 2016;11(11):e0166683. 10.1371/journal.pone.0166683 . (In eng). Cho Y, Tokuhara D, Morikawa H, et al. Transient Elastography-Based Liver Profiles in a Hospital-Based Pediatric Population in Japan. PLoS ONE. 2015;10(9):e0137239. 10.1371/journal.pone.0137239 . (In eng). Additional Declarations No competing interests reported. Supplementary Files all1.xlsx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 06 Mar, 2026 Reviews received at journal 02 Mar, 2026 Reviewers agreed at journal 02 Mar, 2026 Reviews received at journal 27 Feb, 2026 Reviewers agreed at journal 26 Feb, 2026 Reviewers invited by journal 19 Feb, 2026 Editor invited by journal 06 Feb, 2026 Editor assigned by journal 20 Jan, 2026 Submission checks completed at journal 19 Jan, 2026 First submitted to journal 19 Jan, 2026 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. 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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-8546522","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":594807012,"identity":"6e983d39-2444-4f61-a747-c3a4e060f670","order_by":0,"name":"Ping lin Hsieh","email":"","orcid":"","institution":"First Affiliated Hospital of Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Ping","middleName":"lin","lastName":"Hsieh","suffix":""},{"id":594807013,"identity":"15d7d68c-6d5d-40af-8391-6161dd311741","order_by":1,"name":"Dong-yu Zeng","email":"","orcid":"","institution":"First Affiliated Hospital of Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Dong-yu","middleName":"","lastName":"Zeng","suffix":""},{"id":594807014,"identity":"dc9edce5-8cb6-4d18-bd31-59898217860a","order_by":2,"name":"Min-xi Xiao","email":"","orcid":"","institution":"First Affiliated Hospital of Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Min-xi","middleName":"","lastName":"Xiao","suffix":""},{"id":594807015,"identity":"47078542-4e93-482b-96f3-0ec2e7b6c3d9","order_by":3,"name":"Er-hei Dai","email":"","orcid":"","institution":"Fifth Hospital of Shijiazhuang","correspondingAuthor":false,"prefix":"","firstName":"Er-hei","middleName":"","lastName":"Dai","suffix":""},{"id":594807016,"identity":"ea160b90-e3d9-4a6c-b3ee-3e247a2d91da","order_by":4,"name":"Yun Pan","email":"","orcid":"","institution":"First Affiliated Hospital of Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Yun","middleName":"","lastName":"Pan","suffix":""},{"id":594807017,"identity":"065c3bb2-8e51-493f-abae-8f51c6e84d13","order_by":5,"name":"Liu Yang","email":"","orcid":"","institution":"First Affiliated Hospital of Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Liu","middleName":"","lastName":"Yang","suffix":""},{"id":594807018,"identity":"fa142085-ff21-4cc9-9e4f-b55eb9821275","order_by":6,"name":"Li-li Zhang","email":"","orcid":"","institution":"Shijiazhuang Second Hospital","correspondingAuthor":false,"prefix":"","firstName":"Li-li","middleName":"","lastName":"Zhang","suffix":""},{"id":594807019,"identity":"17483f05-7eaa-4c30-8a7e-20e18efd71d4","order_by":7,"name":"Min-ran Li","email":"","orcid":"","institution":"First Affiliated Hospital of Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Min-ran","middleName":"","lastName":"Li","suffix":""},{"id":594807020,"identity":"6252f4ba-7dbc-4473-8377-a82c277a2d60","order_by":8,"name":"Yan Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYBACxvbm4z8SftjU9xOthbnnWILEw540xpkNxGphn+FjIPmA7TDjhgPEauGdwWBgkMCTxmx8PHkDw4+KbYS1SM5uSEhIsLBhMzvzrICx58xtwloM5xw4cABoC4/ZjRwDZsY2IrTY30hsbEhgOyxhPINYLYwzkpkZgFoMDCSI1tJzjI0hsSctQQLol4NE+YWxvf8b448fNgn87ckbH/yoIEILEkgwOECSerAWUnWMglEwCkbBCAEAR6ZBhnOMg7UAAAAASUVORK5CYII=","orcid":"","institution":"General Practice Department, Hospital of South China Normal University","correspondingAuthor":true,"prefix":"","firstName":"Yan","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2026-01-08 03:38:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8546522/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8546522/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103505335,"identity":"df3cb312-db67-4efe-bc9a-5bb2fec124cb","added_by":"auto","created_at":"2026-02-26 13:30:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":821013,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8546522/v1/79a489e4-3ba1-44a6-90b0-d85d5703ee84.pdf"},{"id":103216428,"identity":"331a6c00-8ab3-4b44-bb06-e8b4ebc87990","added_by":"auto","created_at":"2026-02-23 09:35:29","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":39994,"visible":true,"origin":"","legend":"","description":"","filename":"all1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8546522/v1/1b1d4e98c43acdaa65a164d0.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A study on the clinical characteristics of overweight children in kindergartens in South China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOverweight and obesity have emerged as critical global health challenges. Recent estimates indicate that approximately 10% of children worldwide are currently overweight or obese, including 22\u0026nbsp;million children under five years of age \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. China has experienced a particularly dramatic increase, with pediatric overweight rates increasing to 15% by 2011—more than double the prevalence recorded in 1991 \u003csup\u003e2\u003c/sup\u003e. Research has identified five primary determinants of childhood overweight and obesity in the Chinese context: (1) environmental influences, (2) individual dietary patterns and physical activity levels, (3) the interaction between genetic predispositions and environmental factors, (4) endocrine and metabolic disorders, and (5) the internal physiological environment\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNonalcoholic fatty liver disease (NAFLD) encompasses a spectrum of liver disorders, ranging from simple steatosis to nonalcoholic steatohepatitis (NASH), fibrosis, and ultimately cirrhosis. It is clinically defined as macrovesicular hepatic steatosis, characterized by excessive fat accumulation exceeding 5% of hepatocytes in individuals without significant alcohol consumption\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Emerging evidence has demonstrated that NAFLD is strongly associated with metabolic abnormalities, particularly dyslipidemia and insulin resistance\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Without intervention, the condition may progress to more severe stages, including hepatic fibrosis and cirrhosis \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, so screening for NAFLD is needed \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study is a cross-sectional study. In this study, we observed body mass index (BMI), laboratory biochemical parameters and liver elastography data to determine the differences among the groups. Our goal was to explore the differences in clinical characteristics, including laboratory biochemical parameters, age, and sex, between overweight and nonoverweight children. These results highlight the clinical importance of early metabolic and hepatic screening in pediatric populations.\u003c/p\u003e "},{"header":"Method","content":"\u003cp\u003eData were obtained from Chinese children at South China Normal University Affiliated Kindergarten from March 10 to 12, 2025. In this study, after 7 people without CAP and 2 people without physical examination data were excluded, a total of 140 children were included and used in the final analysis. This study was approved by the Medical Ethics Committee of The First Affiliated Hospital of Jinan University. For the minor participants, parents or legal guardians were informed, and consent was obtained. We ensured that all ethical guidelines for children were strictly followed.\u003c/p\u003e\u003cp\u003eWeight and height were measured. Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters. A BMI over 16.5 is considered overweight\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. In accordance with the WHO growth standards, they evaluated children before 5 years of age and between 5 years of age and 19 years of age\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, and we also separated them in the same way as in our research.\u003c/p\u003e\u003cp\u003eLiver elastography was performed via FibroScan® (Echosens) to measure the controlled attenuation parameter (CAP) and liver stiffness measurement (LSM). CAP quantified the increased attenuation of ultrasound waves as they passed through steatotic liver tissue compared with healthy liver tissue. LSM measures the speed of a shear wave generated on the surface of the skin. The velocity of the shear wave was calculated by measuring the time it took for the wave to propagate to a specific depth within the liver. For the procedure, the fasting participant remained in a supine position, with the probe placed in the intercostal space over the right hepatic lobe. A minimum of 10 consecutive valid measurements were taken at the same location, and the median value was recorded as the final result \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. The liver of the cap was 225 or more and was considered steatotic \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eLaboratory biochemical parameters, including liver function tests, routine blood tests, lipoprotein panels, creatinine (CREA), and glucose (Glu), were measured by Daan Gene Co., Ltd. Liver function tests included alanine aminotransferase (ALT), gamma-glutamyl transpeptidase (GGT), total protein (TP), albumin (ALB), and alkaline phosphatase (ALP) levels. Routine blood tests included white blood cell (WBC) count, absolute neutrophil count (NEUT), absolute lymphocyte count (LYMPH), red blood cell (RBC) count, hemoglobin (HGB), and platelet count (PLT). The lipoprotein panel included total cholesterol (TC) measured via cholesterol oxidase, triglyceride (TG) measured via triglyceride colorimetric assay, high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) measured via direct LDL-cholesterol measurement. Creatinine (CREA) was measured via an enzymatic creatinine assay. Glucose (Glu) was measured via the hexokinase method for blood glucose determination.\u003c/p\u003e\u003cp\u003eThe data were analyzed via SPSS software ver. 25.0 from SPSS Inc. in Chicago, Illinois, USA. The data are reported as the means (standard deviations). Independent t tests were used to compare differences between groups. Significance was defined as a p value less than 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eoverweight and non overweight\u003c/p\u003e \u003cp\u003eA total of 140 subjects participated in this study, comprising 110 individuals in the nonoverweight group and 30 in the overweight group. No adverse events were reported during the trial. The baseline characteristics of the study population are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eWeight and BMI were significantly greater in the overweight group than in the nonoverweight group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas height was slightly lower but not significantly different. The CAP values tended to be greater in the overweight group, and the E values were lower, although these differences did not reach statistical significance. Notably, four participants (three males and one female) presented CAP values\u0026thinsp;\u0026ge;\u0026thinsp;225 dB/m (ranging from 225\u0026ndash;247), accounting for 2.9% of the total cohort.\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\u003eCharacteristic baseline of nonobese and obese group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient characteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;140)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003enon overweight\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;110) (78%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eoverweight (n\u0026thinsp;=\u0026thinsp;30) (22%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.53(0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6(4.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.40(1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.57\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\u003e87(62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67(60.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20(66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003e53(38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43(39.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10(33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.17(3.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.35(2.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.16(4.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11(0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.10(0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.12(0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.58(1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.01(0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.67(1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAP (dB/m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165.65(28.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e165.31(28.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e166.90(28.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE(Kpa)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.16(0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.24(0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.87(0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.06\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\u003eLiver function tests and routine blood tests were performed in this study. Although most indices were lower in the overweight group than in the nonoverweight group, except for GGT, which was greater, none of these differences reached statistical significance (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eAnalysis of the lipid panel revealed lower levels of TC, HDL-C, LDL-C, and Glu in the overweight group, along with higher CREA and comparable TG levels. However, only HDL-C was significantly lower in the overweight group (p value\u0026thinsp;=\u0026thinsp;0.002).\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\u003e Laboratory biochemical parameters of nonobese and obese group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient characteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;140)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003enon overweight\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;110) (78%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eoverweight (n\u0026thinsp;=\u0026thinsp;30) (22%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (10^9/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.99(1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.03(1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.83(1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEUT (10^9/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.22(1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.24(1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.12(1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLYMPH (10^9/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.11(0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.11(0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.09(1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBC (10^12/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.59(0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.60(0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.56(0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGB(g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e124.36(8.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e124.39(8.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e124.23(7.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT(10^9/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e304.11(63.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e309.22(63.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e285.40(62.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.31(4.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.33(4.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.27(2.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGT(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.56(2.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.52(2.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.73(2.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP(g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.75(3.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66.93(2.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.11(4.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB(g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44.67(2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.72(2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.51(2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALP(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e228.87(66.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e229.38(71.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e227.00(49.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mmol)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.36(0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.41(0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.15(0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.78(0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.78(0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.78(0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C(mmol)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.54(0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.58(0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.43(0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C(mmol)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.21(0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.23(0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.13(0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCREA(\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.70(4.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.40(4.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.80(4.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlu(mmol)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.99(0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.00(0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.92(0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSex-Specific Analysis\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe participants were stratified by sex, with 53 individuals in the female group and 87 in the male group. No significant differences were observed between sexes in terms of age, weight, height, or BMI (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). While both the CAP and E values were numerically greater in females than in males, these differences did not reach statistical significance.\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\u003eCharacteristic baseline of male and female\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient characteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;140)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003emale\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;87) (62.1%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;53) (37.9%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.53(0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.52(0.987)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.55(0.952)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.17(3.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.46 (3.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.69 (3.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.11(0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.11 (0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.1 (0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.58(1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.69 (1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.4 (1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAP (dB/m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e165.65(28.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e165.31 (28.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e166.21 (27.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE(Kpa)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.16(0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.13 (0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.22 (0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.62\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\u003eNo statistically significant differences were observed in liver function tests or complete blood count parameters between the sexes. However, females presented numerically lower values for ALT, ALP, NEUT, and HGB, although these differences did not reach statistical significance (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eSignificant sex-based disparities were noted in the lipid panel: TC (P value\u0026thinsp;=\u0026thinsp;0.00), TG (P value\u0026thinsp;=\u0026thinsp;0.049), and LDL-C (P value\u0026thinsp;=\u0026thinsp;0.00) were significantly elevated in females compared with males. While HDL-C levels were lower in females and creatinine (CREA) and fasting glucose (Glu) tended to be lower, these differences were not statistically significant.\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\u003e Laboratory biochemical parameters of male and female\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient characteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;140)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003emale\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;87) (62.1%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;53) (37.9%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (10^9/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.99(1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.97 (1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.02 (2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEUT (10^9/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44.73(11.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.92 (11.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.42 (11.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLYMPH (10^9/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.11(0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.1 (0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.12 (0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBC (10^12/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.59(0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.57 (0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.63 (0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGB(g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e124.36(8.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e124.4 (7.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e124.28 (9.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT (10^9/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e304.11(63.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e302.59 (61.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e306.62 (67.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.31(4.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.45 (3.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.09 (5.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGT(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.56(2.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.59 (2.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.53 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP(g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.75(3.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66.34 (3.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67.43 (2.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB(g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44.67(2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.42 (2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.08 (2.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALP(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e228.87(66.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e230.57 (77.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e226.08 (43.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mmol)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.36(0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.18 (0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.64 (0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.78(0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.72 (0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.87 (0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C(mmol)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.54(0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.54 (0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.55 (0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C(mmol)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.21(0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.07 (0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.44 (0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCREA(\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.70(4.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.15 (4.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.96 (3.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlu(mmol)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.99(0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.06 (0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.86 (0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e3\u0026ndash;4-year-old and 5\u0026ndash;6-year-old\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe participants were categorized into two age groups: 3\u0026ndash;4 years, with 66 individuals, and 5\u0026ndash;6 years, with 74 individuals. Compared with the younger group, the older group presented significantly greater weight and height (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e). However, no statistically significant difference in BMI was detected between the two age groups (\u003cem\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/em\u003e). Liver assessment revealed that the 5\u0026ndash;6-year-old group had numerically lower CAP values and higher E values than did the 3\u0026ndash;4-year-old group, although these differences did not reach statistical significance (\u003cem\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/em\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\u003eCharacteristic baseline of 3-4-year-old and 5-6-year-old group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient characteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;140)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u0026ndash;4-year-old\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;66\u0026thinsp;) (47.1%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026ndash;6-year-old\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;74) (52.9%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.49\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87(62.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43(65.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44(59.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53(37.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23(34.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30(49.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.17(3.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.57(2.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.59(3.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.11(0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.06(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.15(0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.58(1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.46(1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.40(1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAP (dB/m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e165.65(28.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e167.65(31.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e163.86(24.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE(Kpa)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.16(0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.14(0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.18(0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.83\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\u003eThe 5\u0026ndash;6-year-old group presented significantly elevated levels of GGT (p value\u0026thinsp;=\u0026thinsp;0.01), TP (p value\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and HGB (p value\u0026thinsp;=\u0026thinsp;0.01). Conversely, ALT, WBC, NEUT, and LYMPH were lower.\u003c/p\u003e \u003cp\u003eIn the lipid panel, TC (p value\u0026thinsp;=\u0026thinsp;0.049) was significantly greater in the 5\u0026ndash;6-year-old group. HDL-C and LDL-C levels were also higher and TG levels were lower in the 5\u0026ndash;6-year-old group than in the control group, but the results were not significantly different. CREA (p value\u0026thinsp;=\u0026thinsp;0.03) was significantly greater in the 5\u0026ndash;6-year-old group, and Glu was significantly lower.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e Laboratory biochemical parameters of 3-4-year-old and 5-6-year-old group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient characteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;140)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u0026ndash;4 years old\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;66\u0026thinsp;) (47.1%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026ndash;6 years old\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;74) (52.9%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC(10^9/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.99(1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.19(1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.81(1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEUT#(10^9/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.22(1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.31(1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.13(1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLYMPH#(10^9/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.11(0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.24(0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.99(0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBC(10^12/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.59(0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.57(0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.61(0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGB(g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e124.36(8.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e122.33(8.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e126.16(8.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT(10^9/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e304.11(63.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e300.24(65.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e307.57(62.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.31(4.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.36(4.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.27(4.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGT(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.56(2.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.83(2.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.22(2.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP(g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.75(3.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.52(3.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67.85(2.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB(g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44.67(2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.40(2.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.91(2.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALP(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e228.87(66.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e228.77(85.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e228.96(44.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mmol)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.36(0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.21(0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.49(0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.78(0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.79(0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.76(0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C(mmol)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.54(0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.52(0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.57(0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C(mmol)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.21(0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.10(0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.30(0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCREA(\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.70(4.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.85(4.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.46(4.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlu(mmol)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.99(0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.05(0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.92(0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study included 140 participants aged 3 to 6 years. The children were categorized on the basis of three criteria: body mass index (BMI), age, and sex. Comparative analyses revealed that HDL-C levels were significantly lower in the overweight group than in the nonoverweight group. Sex-based differences revealed that females had significantly higher levels of TC, TG, and LDL-C than males did in terms of laboratory biochemical parameters. Age-related comparisons demonstrated that compared with 3\u0026ndash;4-year-olds, 5\u0026ndash;6-year-olds had significantly greater weight and height at baseline. Additionally, laboratory parameters, including HGB, GGT, TP, TC, and CREA, were significantly greater in the 5\u0026ndash;6-year-old group than in the younger age group.\u003c/p\u003e \u003cp\u003ePrevious studies reported that the prevalence of overweight and obesity among preschool-aged children in the United States was 26%, with overweight defined as a BMI between the 85th and 95th percentiles for age and sex and obesity defined as a BMI above the 95th percentile\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. In contrast, a meta-analysis of Chinese studies revealed a combined overweight and obesity detection rate of 19% in preschool children (11% overweight and 8% obese)\u003csup\u003e14\u003c/sup\u003e. In our study, overweight children accounted for 22% of all participants, whereas central adipose obesity (CAP) was observed in 2.9% of the participants, indicating a prevalence higher than that reported in local Chinese studies but lower than that reported in the U.S. Excessive consumption of sugar-sweetened beverages has been identified as a contributing factor to obesity in both U.S. \u003csup\u003e15\u003c/sup\u003e and Chinese\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e studies, suggesting shared etiological factors across these populations. The discrepancy between our findings and those of previous Chinese studies may be attributed to regional variations, as our study focused exclusively on Guangzhou. Furthermore, research utilizing body fat percentage measurements has revealed distinct geographical clustering in children's physical constitution, with obesity hotspots predominantly in Northeast China and a lower prevalence in southern regions\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA previous study revealed that the prevalence of overweight and obesity in preschool-aged children in the USA was 26%, according to the definition of overweight as a BMI between the 85th and 95th percentiles for age and sex and above the 95th percentile for obesity. In another meta-analysis, the overweight and obesity detection rate of preschool children in China in this study was 19% (the overweight detection rate was 11%, and the obesity detection rate was 8%). In our study, overweight children accounted for 22% of all participants, and CAP accounted for 2.9% of all participants, which was higher than that reported in the USA and higher than that reported in a local study.\u003c/p\u003e \u003cp\u003eAlthough obesity is influenced by the interplay of genetic, epigenetic, metagenomic, and environmental factors\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, genetic predisposition may be the most significant contributor in this study. Given that the kindergarten employed a unified management strategy, environmental influences were relatively consistent across participants, minimizing the variability in external factors. Polygenic obesity is caused by numerous SNPs, the pattern of heritability is similar to that of other diseases and complex traits, and it is the most common disease in childhood \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Variation in 2 genes, MC4R and FTO, has been recognized, and insulin-induced gene 2 is still being studied \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Genome-wide association studies (GWASs) have revealed that more than 52 genetic loci are associated with obesity susceptibility \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Our findings demonstrate that even under uniform environmental conditions, phenotypic variability persists, suggesting a strong genetic component. However, further research is needed to elucidate the specific genes driving these observed differences.\u003c/p\u003e \u003cp\u003eHDL-C was significantly different across BMI groups only in our study. This finding aligns with previous research indicating that reduced HDL-C levels are a characteristic feature of NAFLD \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. According to Chinese pediatric standards, the critical HDL-C threshold for children ranges from 1.03 mmol/L to 1.16 mmol/L, with levels below 1.03 mmol/L classified as abnormally low\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. In our cohort, the nonobese group had a mean HDL-C level of 1.58 (0.29) mmol/L, which was significantly greater than that of the obese group (1.43 (0.28) mmol/L, p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Comparatively, a study of preschool children in Mexico City reported HDL-C levels of 86.3 (3.1) mg/dL (2.23 (0.08) mmol/L) in obese children and 103.1 (7.6) mg/dL (2.66 (0.18) mmol/L) in controls\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, whereas a Shenzhen-based study reported a mean HDL-C level of 1.99 (0.39) mmol/L\u003csup\u003e25\u003c/sup\u003e. Notably, HDL-C values in these foreign studies were approximately twice as high as those reported in Chinese studies, suggesting potential ethnic or regional disparities. Genetic factors are likely influential, as they are known to significantly modulate HDL-C concentrations\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Guidelines for adults recommend maintaining HDL-C levels above 1.0 mmol/L \u003csup\u003e26\u003c/sup\u003e, closely mirroring the pediatric threshold of 1.03 mmol/L \u003csup\u003e23\u003c/sup\u003e. This consistency implies that the physiological requirement for HDL-C remains relatively stable across all age groups in China.\u003c/p\u003e \u003cp\u003eIn our study, all indices of blood lipid levels and total cholesterol (TC), triglyceride (TG), and low-density lipoprotein (LDL) levels were greater in females than in males, and all indices except high-density lipoprotein cholesterol (HDL-C) were significantly different. In a previous study, each index in males at P50 was approximately 150, 85, and 45 mg/dL (3.885, 2.202, and 1.166 mmol/L) for TC, HDL, and LDL, respectively, at P50 was approximately 150, 85, and 50 mg/dL for HDL, LDL (3.885, 2.202, and 1.295 mmol/L) at 5 and 6 years of age, and each index at P50 in females was approximately 150, 90, and 45 mg/dL (3.885, 2.331, and 1.166 mmol/L) for TC, HDL, and LDL at 3 and 4 years of age and 155, 90, and 50 mg/dL (4.015, 2.331, and 1.295 mmol/L) for TC, HDL, and LDL at 5 and 6 years of age \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, which supported our findings.\u003c/p\u003e \u003cp\u003eOur study revealed significantly higher levels of HGB, GGT, TP, TC, and CREA in the older children (5\u0026ndash;6 years) than in the younger children (3\u0026ndash;4 years). These findings align with the literature showing a positive correlation between GGT levels and age in pediatric populations\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The age-related increase in GGT observed in our cohort further supports this established relationship. Our results showing higher TC concentrations in older children are consistent with those of previous Chinese studies \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. These studies reported that females exhibit a gradual increase in TC with age, whereas males show a transient decrease at age 5 but maintain higher mean TC levels in the 5\u0026ndash;6-year-old group than in the 3\u0026ndash;4-year-old group. Notably, our study provides new evidence of this age-dependent pattern in both GGT and TC levels, specifically within the 3\u0026ndash;6-year age range. The consistent observation of elevated biomarkers in older preschool-aged children suggests developmental changes in metabolic and physiological processes during this critical growth period. These findings highlight the importance of considering age-specific reference ranges for clinical biomarkers in early childhood. Further research is needed to elucidate the underlying mechanisms driving these age-related variations in pediatric biochemical parameters.\u003c/p\u003e \u003cp\u003eOur liver elastography results showed distinct patterns from those of previous studies regarding hepatic steatosis and fibrosis markers in preschool-aged children. While existing research has revealed no correlation between CAP and LSM but has identified a positive association between LSM and age \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e and has demonstrated that obese individuals typically present elevated CAP values that are positively correlated with ALT levels \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, our study revealed several noteworthy differences. First, we observed lower CAP values in older children (5\u0026ndash;6 years) than in their younger counterparts (3\u0026ndash;4 years). Second, although overweight children presented higher CAP values, as expected, we found higher ALT levels in nonoverweight children, indicating that there was no positive correlation between these two indices in our cohort. These discrepancies may stem primarily from the narrow 3\u0026ndash;6-year age range of our study population, suggesting that the relationships between hepatic steatosis markers and liver enzymes may follow different patterns in early childhood than established adult correlations do. The developing liver physiology during these formative preschool years likely undergoes dynamic changes that differentially affect these biomarkers, highlighting the need for age-specific reference values and further investigations across broader pediatric age ranges to fully elucidate these relationships.\u003c/p\u003e \u003cp\u003eThis study has two major limitations that should be addressed in future research. First, the cross-sectional design lacks longitudinal follow-up data, limiting our ability to assess long-term effects. Second, the sample may not fully represent South China, as the data were collected from only one kindergarten. Future studies should incorporate longitudinal tracking and expand recruitment to multiple kindergartens to increase generalizability.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe found that among preschool children aged 3\u0026ndash;6 in Guangzhou, China, the prevalence of overweight was 22%, and the detection rate of NAFLD (assessed by CAP\u0026thinsp;\u0026ge;\u0026thinsp;225 dB/m) was 2.9%, and revealed significant differences in metabolic indicators and liver health parameters among children of different weight, sex, and age groups. In Overweight and Metabolic Abnormalities, Overweight children exhibited significantly lower High-Density Lipoprotein Cholesterol levels. This suggests that even in early childhood, being overweight is already associated with an unfavorable lipid profile, increasing the risk for future metabolic diseases. In sex-based differences, regarding blood lipids, female children had significantly higher levels of Total Cholesterol, Triglycerides, and Low-Density Lipoprotein Cholesterol than males. This indicates that sex is an important factor influencing lipid levels in preschool children and should be considered in early health screening and interventions. In age-related physiological changes, compared to 3-4-year-olds, children aged 5\u0026ndash;6 years had significantly greater weight and height, as well as higher levels of several biochemical parameters, including Hemoglobin, Gamma-Glutamyl Transpeptidase, Total Protein, Total Cholesterol, and Creatinine. This reflects the normal physiological maturation process during a critical period of child growth and development, underscoring the importance of using age-specific reference values. In early signals of Liver Health, although liver elastography parameters did not show statistically significant differences between groups, the observed trend of higher CAP values in overweight children, coupled with the fact that a small number of children already met the criteria for hepatic steatosis, highlights the clinical importance of early liver and metabolic screening in the pediatric population.\u003c/p\u003e \u003cp\u003eIn conclusion, this study emphasizes significant differences in metabolic indicators and liver health parameters during the preschool period and adopting targeted early health screening and prevention strategies addressing overweight, sex, and age-specific factors to curb the further development of childhood obesity and its related metabolic complications. Future research should incorporate longitudinal designs and expand the sample size to more comprehensively elucidate the dynamic developmental trajectory of liver and metabolic health in children.\u003c/p\u003e"},{"header":"Declarations","content":" \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003e(include Helsinki statement under it)\u003c/p\u003e \u003ch2\u003eConsent for publication\u003c/h2\u003e \u003cp\u003e Our research followed Helsinki statement, and the parents of children have provided a written informed consent to publication of this article.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe Medical Science Research Project of Hebei Province (No. 20260984)\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003ePing-lin Hsieh, Dong-yu Zeng, Min-xi Xiao, Er-hei Dai, Yun Pan, Liu Yang, Li-li Zhang, Min-ran Li, and Yan Li searched the literature and conceived the study. Min-ran Li and Yan Li designed the study and performed the experiments. Min-ran Li and Ping-lin Hsieh interpreted the results and drafted the report. Ping-lin Hsieh, Dong-yu, Zeng, Min-xi Xiao, and Yan Li performed the experiments and collected the data. Ping-lin Hsieh, Dong-yu Zeng, Min-xi Xiao, Er-hei Dai, Yun Pan, Liu Yang, Li-li Zhang, Min-ran Li, and Yan Li analyzed the data and wrote the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data supporting the findings of this study are available within the paper and its Supplementary Information.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLanigan J, Barber S, Singhal A. Prevention of obesity in preschool children. 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(In eng).\u003c/span\u003e\u003c/li\u003e\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":"","lastPublishedDoi":"10.21203/rs.3.rs-8546522/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8546522/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIntroduction\u003c/p\u003e\n\u003cp\u003eOur goal was to explore the differences in clinical characteristics, including laboratory biochemical parameters, age, and sex, between overweight and nonoverweight children.\u003c/p\u003e\n\u003cp\u003eMethod\u003c/p\u003e\n\u003cp\u003eIn this study, 140 participants were 3- to 6-year-old children. Data were obtained from Chinese children at South China Normal University Affiliated Kindergarten from March 10 to 12, 2025. Weight, height, laboratory biochemical parameters, and liver elastography were measured. We divided the participants into 3 different groups according to their BMI, age, and sex.\u003c/p\u003e\n\u003cp\u003eResults\u003c/p\u003e\n\u003cp\u003eCompared with that in the nonoverweight group, HDL-C was significantly lower in the overweight group. The laboratory biochemical parameters TC, TG, and LDL-C were significantly greater in females than in males. The weight and height at baseline were significantly greater in the 5–6-year-old group than in the 3–4-year-old group, and HGB, GGT, TP, TC, and CREA were significantly greater in the laboratory biochemical parameters in the 5–6-year-old group than in the 3–4-year-old group.\u003c/p\u003e\n\u003cp\u003eConclusion\u003c/p\u003e\n\u003cp\u003eWe found that among preschool children aged 3-6 in Guangzhou, China, the prevalence of overweight was 22%, and the detection rate of NAFLD (assessed by CAP ≥225 dB/m) was 2.9%, and revealed significant differences in metabolic indicators and liver health parameters among children of different weight, sex, and age groups. In conclusion, this study emphasizes significant differences in metabolic indicators and liver health parameters during the preschool period and adopting targeted early health screening and prevention strategies addressing overweight, sex, and age-specific factors to curb the further development of childhood obesity and its related metabolic complications. Future research should incorporate longitudinal designs and expand the sample size to more comprehensively elucidate the dynamic developmental trajectory of liver and metabolic health in children.\u003c/p\u003e","manuscriptTitle":"A study on the clinical characteristics of overweight children in kindergartens in South China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-23 09:35:24","doi":"10.21203/rs.3.rs-8546522/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-06T09:54:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-03T04:25:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"199315325604349785710367133497807635137","date":"2026-03-03T01:21:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-27T17:12:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"280733839840627336445100984136105632759","date":"2026-02-26T11:46:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-19T06:46:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-06T10:58:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-20T05:36:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-19T16:32:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pediatrics","date":"2026-01-19T16:18:59+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":"311162f6-f1ce-46de-94ca-96532fd9f80a","owner":[],"postedDate":"February 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-19T07:10:27+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-23 09:35:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8546522","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8546522","identity":"rs-8546522","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}