OBJECTIVES: To study the predictive value of serum macrophage M1/M2 markers for the risk of cardiac lesions in obese children. METHODS: A total of 60 obese children (mild-to-moderate obesity, n=32; severe obesity, n=28) and 50 healthy controls who visited the Second Affiliated Hospital of Nanchang University from June 2024 to December 2024 were included. The baseline characteristics and the levels of laboratory indicators, echocardiographic parameters, and macrophage markers (MCP-1, Arg-1, CD206, and CD86) were compared among the three groups. The correlation between macrophage marker levels and echocardiographic parameters and the influencing factors of cardiac lesions in obese children were analyzed. The receiver operating characteristic curve analysis was used to evaluate the predictive performance of each influencing factor for cardiac lesions in obese children. RESULTS: Multiple echocardiographic parameters differed significantly among the mild-to-moderate obesity, severe obesity, and control groups (P<0.01). Significant differences were also observed in MCP-1 and Arg-1 levels, CD206 positivity rate, and the CD86/CD206 ratio among the three groups (P<0.05). In obese children, MCP-1 and Arg-1 levels, as well as CD86 and CD206 positivity rates, were correlated with echocardiographic parameters (P<0.05). Univariate logistic regression identified MCP-1, Arg-1, the CD86/CD206 ratio, and the CD206 positivity rate as factors associated with cardiac lesions (P<0.05). The combined prediction model based on these markers yielded an area under the receiver operating characteristic curve of 0.887 (P<0.01). CONCLUSIONS Combined measurement of macrophage markers can predict the risk of early cardiac lesions in obese children.
Humans ; Male ; Female ; Child ; Biomarkers/blood* ; Macrophages ; Obesity/blood* ; Chemokine CCL2/blood* ; ROC Curve ; Adolescent ; Child, Preschool ; Heart Diseases/diagnosis* ; Echocardiography

OBJECTIVE: To investigate the clinical characteristics of overlapping syndromes of low muscle mass in Chinese patients with rheumatoid arthritis (RA) and their impact on physical function. METHODS: Consecutive patients with RA were recruited from September 2019 to April 2024 at Department of Rheumatology and Immunology, Sun Yat-Sen Memorial Hospital. Clinical data including disease acti-vity, physical function and radiographic assessment were collected. All patients also finished measurement of body composition, grip strength, and gait speed, and overlapping syndromes of low muscle mass as well as malnutrition, sarcopenia, sarcopenic obesity, and cachexia were evaluated. The Stanford health assessment questionnaire- disability index (HAQ-DI) was used to evaluate physical function. Logistic regression was used to analyze the related factors of physical dysfunction. RESULTS: A total of 1 016 RA patients were recruited. Their mean age was (52.4±12.5) years, and 82.5% were female. There were 557 cases (54.8%) with overlapping syndromes of low muscle mass and all of them were malnutrition. On this basis, 326 cases (32.1%) exhibited sarcopenia, 124 (12.2%) sarcopenic obesity, and 33 (3.2%) cachexia. There were 584 (57.4%) of RA patients having physical dysfunction, with varying degrees of severity 421 (41.4%) mild, 124 (12.2%) moderate, and 39 (3.8%) severe. Compared with patients without overlapping syndromes of low muscle mass (n=459) or with malnutrition only (n=231), RA patients with both malnutrition and sarcopenia (n=326) had significantly higher core disease activity indicators and higher rate of physical dysfunction (69.6% vs. 42.0% vs. 56.6%). However, compared with patients without overlapping syndromes of low muscle mass, patients with malnutrition only had lower HAQ-DI score (median 0.0 vs. 0.1) and lower rate of physical dysfunction (42.0% vs. 56.6%). Multivariate Logistic regression analysis showed that simultaneously overlapping malnutrition and sarcopenia were associated factors of physical dysfunction (OR=2.021, 95%CI: 1.067-3.828), but malnutrition only was not. CONCLUSION Simultaneously overlapping malnutrition and sarcopenia can deteriorate disease activity and physical dysfunction in RA patients. The screening and evaluation of overlapping syndromes of low muscle mass, especially sarcopenia should be emphasized in patients with RA.
Humans ; Arthritis, Rheumatoid/physiopathology* ; Female ; Male ; Sarcopenia/complications* ; Middle Aged ; Cachexia/diagnosis* ; Malnutrition/etiology* ; Obesity/physiopathology* ; Body Composition ; Syndrome ; Hand Strength ; Adult ; Muscle, Skeletal/physiopathology* ; Surveys and Questionnaires

BACKGROUND: Tri-ponderal mass index (TMI) has been reported to be a more accurate estimate of body fat than body mass index (BMI). This study aims to compare the effectiveness of TMI and BMI in identifying hypertension, dyslipidemia, impaired fasting glucose (IFG), abdominal obesity, and clustered cardio-metabolic risk factors (CMRFs) in 3- to 17-year-old children. METHODS: A total of 1587 children aged 3 to 17 years were included. Logistic regression was used to evaluate correlations between BMI and TMI. Area under the curves (AUCs) were used to compare discriminative capability among indicators. BMI was converted to BMI- z scores, and accuracy was compared by false-positive rate, false-negative rate, and total misclassification rate. RESULTS: Among children aged 3 to 17 years, the mean TMI was 13.57 ± 2.50 kg/m 3 for boys and 13.3 ± 2.33 kg/m 3 for girls. Odds ratios (ORs) of TMI for hypertension, dyslipidemia, abdominal obesity, and clustered CMRFs ranged from 1.13 to 3.15, higher than BMI, whose ORs ranged from 1.08 to 2.98. AUCs showed similar ability of TMI (AUC: 0.83) and BMI (AUC: 0.85) in identifying clustered CMRFs. For abdominal obesity and hypertension, the AUC of TMI was 0.92 and 0.64, respectively, which was significantly better than that of BMI, 0.85 and 0.61. AUCs of TMI for dyslipidemia and IFG were 0.58 and 0.49. When 85th and 95th of TMI were set as thresholds, total misclassification rates of TMI for clustered CMRFs ranged from 6.5% to 16.4%, which was not significantly different from that of BMI- z scores standardized according to World Health Organization criteria. CONCLUSIONS TMI was found to have equal or even better effectiveness in comparison with BMI in identifying hypertension, abdominal obesity, and clustered CMRFs TMI was more stable than BMI in 3- to 17-year-old children, while it failed to identify dyslipidemia and IFG. It is worth considering the use of TMI for screening CMRFs in children and adolescents.
Adolescent ; Child ; Child, Preschool ; Female ; Humans ; Male ; Body Mass Index ; Dyslipidemias ; East Asian People ; Hypertension ; Obesity, Abdominal ; Pediatric Obesity/diagnosis* ; Cardiometabolic Risk Factors

OBJECTIVES: To study the relationship between skeletal muscle mass index (SMI) and metabolic phenotypes of obesity in adolescents, and to provide a basis for the prevention and control of adolescent obesity and related metabolic diseases. METHODS: A total of 1 352 adolescents aged 12 to 18 years were randomly selected by stratified cluster sampling in Yinchuan City from October 2017 to September 2020, and they were surveyed using questionnaires, physical measurements, body composition measurements, and laboratory tests. According to the diagnostic criteria for metabolic abnormalities and the definition of obesity based on the body mass index, the subjects were divided into four metabolic phenotypes: metabolically healthy normal weight, metabolically healthy obesity, metabolically unhealthy normal weight, and metabolically unhealthy obesity. The association between SMI and the metabolic phenotypes was analyzed using multivariate logistic regression. RESULTS: The SMI level in the metabolically unhealthy normal weight, metabolically healthy obesity, and metabolically unhealthy obesity groups was lower than that in the metabolically healthy normal weight group (P<0.001). Multivariate logistic regression analysis showed that after adjusting for gender and age, a higher SMI level was a protective factors for adolescents to develop metabolic unhealthy normal weight, metabolically healthy obesity, and metabolically unhealthy obesity phenotypes (OR=0.74, 0.60, and 0.54, respectively; P<0.001). CONCLUSIONS Increasing SMI can reduce the risk of the development of metabolic unhealthy/obesity.
Adolescent ; Humans ; Body Mass Index ; Metabolic Syndrome/metabolism* ; Muscle, Skeletal/metabolism* ; Obesity, Metabolically Benign/diagnosis* ; Pediatric Obesity ; Phenotype ; Risk Factors ; Child

Overweight/obesity has become one of the major public health problems among children and adolescents all over the world. The current screening standards for overweight and obesity in children and adolescents are not unified. It is easy to make mistakes and inefficient to evaluate item by item or develop self-written packages. Taking the"Screening standards for overweight and obesity in Chinese school-age children and adolescents"as an example, this study introduced four methods and procedures for evaluating overweight and obesity among children and adolescents from the world and China and described their application methods in combination with specific cases. At the same time, the SPSS and SAS packages were compiled and the specific application steps were explained, so that users could correctly and quickly screen overweight and obesity among children and adolescents, and conduct horizontal comparisons of similar studies across different regions.
Adolescent ; Body Mass Index ; Child ; China ; Humans ; Overweight ; Pediatric Obesity/diagnosis* ; Prevalence

BACKGROUND AND OBJECTIVES: Hypertension is becoming one of the most common health conditions in children and adolescents due to increasing childhood obesity. We aimed to provide the auscultatory blood pressure (BP) normative reference values for Korean non-overweight children and adolescents. METHODS: BP measurements in children and adolescents aged 10 to 18 years were performed in the Korean National Health and Nutrition Examination Survey (KNHANES) from 1998 to 2016. BP was measured using a mercury sphygmomanometer. Sex-, age- and height-specific systolic BP (SBP) and diastolic BP (DBP) percentiles were calculated in the non-overweight children (n=10,442). We used the General Additive Model for Location Scale and Shape method to calculate BP percentiles. RESULTS: The 50th, 90th, 95th, and 99th percentiles of SBP and DBP tables and graphs of non-overweight children and adolescents aged 10 to 18 years were presented by age and height percentiles. We found that the SBP and DBP at the 95th percentile were well correlated with height. The BP tables presented by height contained BP values from 124 cm to 190 cm for boys and from 120 cm to 178 cm for girls. Boys had higher SBP and DBP. CONCLUSIONS: We provided the sex-, age- and height-specific auscultatory BP values using the KNHANES big data. These may be useful in diagnosis and treatment of hypertension in Korean children and adolescents.
Adolescent ; Auscultation ; Blood Pressure ; Child ; Diagnosis ; Female ; Humans ; Hypertension ; Korea ; Methods ; Nutrition Surveys ; Pediatric Obesity ; Reference Values ; Sphygmomanometers

Obesity is associated with various comorbidities, such as type II diabetes, hypertension, dyslipidemia, and cardiovascular disease. Gastrointestinal complications are also frequent and obesity is a direct cause of nonalcoholic fatty liver disease, and are risk factors for gastroesophageal reflux disease, pancreatitis, gallstone disease, diarrhea, dyssynergic defection, and various gastrointestinal cancers. Diagnosis is usually made by measuring body mass index (BMI). Although BMI is correlated with body fat mass, it may overestimate subjects with high muscle mass and underestimate subjects with low muscle mass. Co-measurement of waist circumference as a reflection of abdominal obesity for subjects with BMIs ranging from 25 to 35 kg/m2 has been recommended; however, it is still an anthropometric diagnosis that does not clearly discriminate subjects at risk for developing comorbidities. Biomarkers reflect the underlying biological mechanisms of obesity and can be used to characterize the obesity phenotype (i.e., at high risk for disease development) as well as a target for disease-causing factors. In this article, we describe the conventional diagnosis, biomarkers of obesity, and current challenges.
Adipose Tissue ; Biomarkers ; Body Mass Index ; Cardiovascular Diseases ; Comorbidity ; Diagnosis ; Diarrhea ; Dyslipidemias ; Gallstones ; Gastroesophageal Reflux ; Gastrointestinal Diseases ; Gastrointestinal Neoplasms ; Hypertension ; Non-alcoholic Fatty Liver Disease ; Obesity ; Obesity, Abdominal ; Pancreatitis ; Phenotype ; Risk Factors ; Waist Circumference

BACKGROUND: Osteoporosis is a systemic skeletal disease characterized by reduced bone mineral density and skeletal microstructural abnormalities. We investigated the correlation between serum uric acid (SUA) levels and bone mineral content (BMC) values as a surrogate marker of bone health. Additionally, we assessed whether the correlation, if any, was evident even after classifying the study population into two groups based on a diagnosis of obesity in these individuals.METHODS: This study included 936 women who visited the healthcare centers that participated in this study. Fasting blood samples were obtained from all women to measure SUA, BMC of the lumbar spine was measured using dual-energy X-ray absorptiometry. We evaluated the correlation between SUA and BMC in obese and non-obese groups.RESULTS: SUA concentration and BMC were positively correlated (β=0.257, P=0.001); however, this correlation was significant only in the obese group (β=0.203, P=0.001).CONCLUSION: SUA concentration and BMC are positively correlated in women diagnosed obesity.
Absorptiometry, Photon ; Biomarkers ; Body Mass Index ; Bone Density ; Delivery of Health Care ; Diagnosis ; Fasting ; Female ; Humans ; Obesity ; Osteoporosis ; Spine ; Uric Acid

In recent years, reports of diabetes mellitus (DM) cases that do not fit the traditional classification system have increased in prevalence. While insulin deficiency appears as type 1 DM (T1DM), the new type also has the clinical features of type 2 DM (T2DM); as such, this new type of DM is called ketosis-prone diabetes (KPD) and is correlated with findings of severe hyperglycemia and ketoacidosis. To provide a clear, clinical classification of DM, new classification systems are being studied. Among these, the Aβ system demonstrates the highest sensitivity and specificity in predicting clinical features and prognosis. We report 2 cases of KPD in Korean pediatric patients. The first patient was referred while in a state of diabetic ketoacidosis (DKA) and was considered to have T1DM. However, their blood glucose was well-controlled even with small doses of insulin, and the treatment was able to be changed to metformin therapy. The second patient seemed to be a typical case of T2DM because of his obesity and strong family history. However, blood glucose was not well-controlled with a regular diet, and ketosis occurred. After performing a glucagon stimulation test, both patients showed different clinical features that were finally diagnosed as type A-β+ KPD. The rapid and accurate diagnosis of KPD can reduce the duration of inappropriate insulin use and improve patients' quality of life. Further, the treatment of KPD children should be individualized according to each patient's lifestyle to preventing recurrent DKA.
Adolescent ; Blood Glucose ; Child ; Classification ; Diabetes Mellitus ; Diabetes Mellitus, Type 1 ; Diabetic Ketoacidosis ; Diagnosis ; Diet ; Glucagon ; Humans ; Hyperglycemia ; Insulin ; Ketosis ; Life Style ; Metformin ; Obesity ; Prevalence ; Prognosis ; Quality of Life ; Sensitivity and Specificity

OBJECTIVES: In South Korea, there are two nationwide health surveys conducted by the Korea Centers for Disease Control and Prevention: the Korea Community Health Survey (KCHS) and Korea National Health and Nutrition Examination Survey (KNHANES). The two surveys are directly comparable, as they have the same target population with some common items, and because both surveys are used in various analyses, identifying the similarities and disparities between the two surveys would promote their appropriate use. Therefore, this study aimed to compare the estimates of six variables in KCHS and eight variables in KNHANES over a six-year period and compare time series stability of region-specific and sex- and age-specific subgroup estimates.METHODS: Data from adults aged 19 years or older in the 2010-2015 KCHS and KNHANES were examined to analyze the differences of estimates and 95% confidence interval for self-rated health, current smoking rate, monthly drinking rate, hypertension diagnosis rate, diabetes diagnosis rate, obesity prevalence, hypertension prevalence, and diabetes prevalence. The variables were then clustered into subgroups by city as well as sex and age to assess the time series stability of the estimates based on mean square error.RESULTS: With the exception of self-rated health, the estimates taken based on questionnaires, namely current smoking rate, monthly drinking rate, hypertension diagnosis rate, and diabetes diagnosis rate, only differed by less than 1.0%p for both KCHS and KNHANES. However, for KNHANES, estimates taken from physical examination data, namely obesity prevalence, hypertension prevalence, and diabetes prevalence, differed by 1.9-8.4%p, which was greater than the gap in the estimates taken from questionnaires. KCHS had a greater time series stability for subgroup estimates than KNHANES.CONCLUSIONS: When using the data from KCHS and KNHANES, the data should be selected and used based on the purpose of analysis and policy and in consideration of the various differences between the two data.
Adult ; Centers for Disease Control and Prevention (U.S.) ; Diagnosis ; Drinking ; Health Services Needs and Demand ; Health Surveys ; Humans ; Hypertension ; Korea ; Nutrition Surveys ; Obesity ; Physical Examination ; Prevalence ; Smoke ; Smoking