OBJECTIVES

In Asia, younger individuals (below age 45) are diagnosed to have type 2 diabetes with increased rates of obesity defined by lower BMI yet with greater visceral adiposity (waist circumference and waisthip ratios). The prevalence data on type 1 diabetes is not well established, considered to be low, but is seen to be increasing as well. This changing phenotype therefore, presents a clinical dilemma in terms of correctly classifying diabetes and deciding on the consequent appropriate treatment. Distinguishing type 1 from type 2 diabetes has become more difficult with type 2 diabetes dramatically increasing in young adults and children. This study aims to define the characteristics of diabetes among young adults in the Philippines to provide a basis for appropriate management amidst changes in diabetes phenotypes seen globally.

METHODS

In this cross-sectional analytic study, we characterized the demographic, metabolic, and autoimmune features of diabetes among young adult Filipinos aged 18 to 45 years old consulting at a tertiary referral center in Manila, Philippines. Baseline serum A1c, FBS, 75-g oral glucose tolerance test, insulin, serum C-peptide, insulin autoantibodies, leptin, adiponectin, lipid profile, and thyroid function tests were obtained from the participants and analyzed. The homeostasis model assessment (HOMA) was used to estimate the insulin sensitivity.

RESULTS

A total of 348 patients with diabetes were included, with females comprising two-thirds of the participants. The mean age at diagnosis of diabetes was 35.9±7.22 years. The mean BMI was 28.12 kg/m2, with median waist to hip ratio (WHR) of 0·93. Metabolic syndrome was found in 60% of participants and 67.82% were obese by body mass index. The mean A1c was 9.07±2.52%. Good glucose control (A1c less than 7.0%) was seen in 23% of participants  while nearly half (48%) had HbA1c which was >9.0%. The median levels of fasting insulin and C-peptide were 12.62 (range 1.33–90.42) mIU/L and 0.78 ng/mL (range 0–16.2), respectively. 

Included participants were diagnosed with diabetes within a year and as such, majority did not have any micro- or macrovascular complications. The most common diabetes complication was sensory neuropathy detected by monofilament testing, which was found in 28% of participants, followed by non-proliferative diabetic retinopathy in 13%. A history of previous diabetic ketoacidosis was found in 10 patients (2.87%). Glutamic acid decarboxylase (GAD) and insulin auto-antibodies were found in 3.2% and 19.3% of participants, respectively. Approximately half (51.73%) of the participants were insulin resistant by HOMA-IR.

CONCLUSION

In contrast with Caucasians and other Asians, diabetes among young Filipino adults is associated with lower BMI but with a similarly high visceral adiposity as shown by an elevated WHR. Metabolic syndrome with insulin resistance as defined by a variety of indices is predominant. Type 1 diabetes with autoantibodies occur in only a small fraction of this population. Data derived from this work can provide a framework for cluster analysis towards personalized management specific to this population.

Human ; Acids ; Adiponectin ; Adiposity ; Adult ; Aged ; Antibodies ; Asia ; Asian ; Asian Continental Ancestry Group ; Autoantibodies ; Body Mass Index ; C-peptide ; Carboxy-lyases ; Child ; Cluster Analysis ; Demography ; Diabetes Complications ; Diabetes Mellitus ; Diabetes Mellitus, Type 1 ; Diabetes Mellitus, Type 2 ; Diabetic Ketoacidosis ; Diabetic Retinopathy ; Diagnosis ; Fasting ; Female ; Glucose ; Glucose Tolerance Test ; Glutamate Decarboxylase ; Glutamic Acid ; Insulin ; Insulin Resistance ; Ketosis ; Leptin ; Lipids ; Metabolic Syndrome ; Obesity ; Patients ; Peptides ; Phenotype ; Philippines ; Population ; Prevalence ; Serum ; Therapeutics ; Thyroid Gland ; Thyroid Function Tests ; Young Adult

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

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

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

BACKGROUND/AIMS: There is limited data to compare the clinical characteristics and recurrence rates between left-sided primary epiploic appendagitis (PEA) versus left-sided acute colonic diverticulitis (ACD), and right-sided PEA versus right-sided ACD, respectively.METHODS: We retrospectively reviewed the medical records and radiologic images of the patients who presented with left-sided or right-sided acute abdominal pain and had computer tomography performed at the time of presentation showing radiological signs of PEA or ACD between January 2004 and December 2014. We compared the clinical characteristics of left PEA versus left ACD and right PEA versus right ACD, respectively.RESULTS: Fifty-six patients (left:right = 27:29) and 308 patients (left:right = 24:284) were diagnosed with symptomatic PEA and ACD, respectively. Left-sided PEA were statistically significantly younger (50.2 ± 15.4 years vs. 62.1 ± 15.8 years, P= 0.009), more obese (body mass index [BMI]: 26.3 ± 2.9 kg/m² vs. 22.3 ± 3.1 kg/m² , P< 0.001), and had more tendencies with normal or mildly elevated high-sensitivity C-reactive protein (hsCRP) (1.2 ± 1.3 mg/dL vs. 8.4 ± 7.9 mg/dL, P< 0.001) than patients with left-sided ACD. The discriminative function of age, BMI and CRP between left-sided PEA versus left-sided ACD was 0.71 (cutoff: age ≤ 59 years, sensitivity of 66.7%, specificity of 77.8%), 0.84 (cutoff: BMI > 24.5 kg/m² , sensitivity of 80.0%, specificity of 80.0%) and 0.80 (cutoff: CRP < 1.8 mg/dL, sensitivity of 72.2%, specificity of 85.7%).CONCLUSIONS: If patients with left lower quadrant abdominal pain are less than 60 years, obese (BMI > 24.5 kg/m² ) with or without normal to mild elevated CRP levels (CRP < 1.8 mg/dL), it might be necessary for clinicians to suspect the diagnosis of PEA rather than ACD.
Abdominal Pain ; C-Reactive Protein ; Diagnosis ; Diverticulitis ; Diverticulitis, Colonic ; Humans ; Medical Records ; Obesity ; Peas ; Recurrence ; Retrospective Studies ; Sensitivity and Specificity

PURPOSE: This study aims to provide preliminary data for weight management, and prevention and management of cardiovascular and cerebrovascular diseases. We examined the effect of changes in the weight of workers at a manufacturing company over three years on their metabolic syndrome and metabolic syndrome diagnosis components.METHODS: Necessary data were collected from the questionnaire and the results of the Korean National Health Screening of 2015 and 2017, which included 228 workers at a manufacturing company in G region. The collected data were analyzed using the SPSS/WIN 23.0 program. ANCOVA was used to examine the differences in the metabolic syndrome diagnosis components according to weight change. In addition, multiple logistic regression analysis was used to obtain the odds ratios of metabolic syndrome and metabolic syndrome analysis component, based on the weight changes in the normal weight group and the obesity group.RESULTS: Waist measure, systolic blood pressure, and blood pressure were found to have significant effects based on participants' weight change over three years. These factors increased with a larger increase in weight at a statistically significant level. This study analyzed the weight changes of the normal weight group and the obesity group considering the data from the National Health Screening of 2015, and found that the risk of metabolic syndrome increased at a statistically significant level as body weight increased; thus, the obesity group showed a higher risk in this regard. It was also found that waist measure, fasting blood sugar, and high-density low cholesterol increased at a statistically significant level as body weight increased.CONCLUSION: Health administrators need to recognize the importance of workers' weight management, select an intensive management group based on a time series analysis of weight changes, and develop and implement programs to manage the metabolic syndrome diagnosis components.
Administrative Personnel ; Blood Glucose ; Blood Pressure ; Body Weight ; Cerebrovascular Disorders ; Cholesterol ; Diagnosis ; Fasting ; Humans ; Logistic Models ; Mass Screening ; Obesity ; Odds Ratio

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