BACKGROUND: It is crucial to understand the seasonal variation of Metabolic Syndrome (MetS) for the detection and management of MetS. Previous studies have demonstrated the seasonal variations in MetS prevalence and its markers, but their methods are not robust. To clarify the concrete seasonal variations in the MetS prevalence and its markers, we utilized a powerful method called Seasonal Trend Decomposition Procedure based on LOESS (STL) and a big dataset of health checkups. METHODS: A total of 1,819,214 records of health checkups (759,839 records for men and 1,059,375 records for women) between April 2012 and December 2017 were included in this study. We examined the seasonal variations in the MetS prevalence and its markers using 5 years and 9 months health checkup data and STL analysis. MetS markers consisted of waist circumference (WC), systolic blood pressure (SBP), diastolic blood pressure (DBP), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), fasting plasma glucose (FPG). RESULTS: We found that the MetS prevalence was high in winter and somewhat high in August. Among men, MetS prevalence was 2.64 ± 0.42 (mean ± SD) % higher in the highest month (January) than in the lowest month (June). Among women, MetS prevalence was 0.53 ± 0.24% higher in the highest month (January) than in the lowest month (June). Additionally, SBP, DBP, and HDL-C exhibited simple variations, being higher in winter and lower in summer, while WC, TG, and FPG displayed more complex variations. CONCLUSIONS This finding, complex seasonal variations of MetS prevalence, WC, TG, and FPG, could not be derived from previous studies using just the mean values in spring, summer, autumn and winter or the cosinor analysis. More attention should be paid to factors affecting seasonal variations of central obesity, dyslipidemia and insulin resistance.
Male ; Female ; Humans ; Metabolic Syndrome/epidemiology* ; Seasons ; Prevalence ; Climate ; Insulin Resistance ; Triglycerides

Introduction: The purpose of this study was to determine the possible differences in genetic polymorphisms and serum levels of chromogranin A (CgA), according to age and sex, in subjects with and without metabolic syndrome (MetS). Methodology: The genotyping and serum level of CgA and biochemical parameters were measured by the T-ARMS-PCR and PCR-RFLP and ELISA and spectrophotometer methods, respectively. Results: A comparison of males with and without MetS showed significantly lower high-density lipoprotein-cholesterol (HDL-C) levels than those of females. At ages 30-70 years, both sexes showed significant differences in triglycerides (TG), fasting blood sugar (FBS), CgA levels and waist circumference (WC) when compared to the two groups. Both sexes with MetS indicated significant differences in systolic blood pressure (SBP) at ages 40-70 years, while at ages 40-59 years, there was a significant difference in HDL-C level in males. There was a significant correlation between serum levels of FBS, TG, SBP and WC (in both sexes), and CgA in subjects with MetS. Significant correlation was found between HDL-C level and diastolic blood pressure (DBP), and CgA level in males and females, respectively. CgA genotype frequency (T-415C and C+87T polymorphisms) showed no significant differences between males and females with and without MetS, while there was only a significant difference in frequency of the genotypes T-415C when compared to males with and without MetS. Conclusion The CgA appears to be strongly associated with MetS components in both sexes. Variation in CgA gene expression may affect the T–415C polymorphism in males. This may mean that the structure of CgA genetics differs in different ethnic groups. Differences in the serum level and expression of CgA gene may show valuable study results that it may be expected a relationship between these variables and the MetS.
Sex ; Chromogranin A ; Genotype ; Metabolic Syndrome

Objectives: We determined the clinical characteristics and prevalence of metabolic syndrome among adult Filipinos with overt hypothyroidism. Methodology: This is a cross-sectional study of 151 adults. Patients were recruited by sequential enrollment. Anthropometric and blood pressure measurements were performed followed by blood extraction for metabolic parameters and thyroid function tests. Clinical and laboratory characteristics were compared between patients with and without metabolic syndrome. Results: The prevalence of metabolic syndrome is 40.4% (95%CI: 32.5%, 48.7%). Patients with metabolic syndrome have a waist circumference of 88.4 ± 7.7 cm in females and 93.3 ± 9.0 cm in males. The median fasting blood glucose was 111.4 (52.2) mg/dL, median systolic blood pressure of 120 (30) mm Hg and diastolic blood pressure of 80 (20) mmHg, median serum triglycerides of 174.3 (114.2) mg/dL, median HDL-C of 42.3 (19.2) mg/dL and a proportion of patients with diabetes (23.0%) and hypertension (44.3%), respectively. The presence of increased waist circumference is the most prevalent component seen among hypothyroid patients. There were no differences in terms of age, sex, etiology of hypothyroidism and anti-TPO levels in those with and without metabolic syndrome. Conclusion The prevalence of metabolic syndrome in adult Filipinos with hypothyroidism is high. Emphasis must be placed on early screening using waist circumference and metabolic parameters among hypothyroid patients who are at high risk of developing metabolic syndrome.
Dyslipidemias ; Hypothyroidism ; Metabolic Syndrome ; Prevalence

Background and Objective: Dyslipidemia in hypothyroidism results from the effects of thyroid hormones on lipid metabolism. These, in combination with hypothyroidism-induced hemodynamic changes, are risk factors for cardiometabolic diseases. We determined the prevalence of metabolic syndrome (MS) among adult Filipinos with hypothyroidism and compared clinical and laboratory characteristics of those with versus without MS. Methods: This is a retrospective study of 105 patients with biochemically confirmed hypothyroidism. A review of records obtained anthropometric measurements, blood pressure, fasting blood glucose, lipid profile, and thyroid hormones. Clinical and laboratory characteristics were then compared between MS and those without. Significant differences were determined by two-way ANOVA, while heterogeneity of categorical variables was determined by chi-square or Fisher exact test. All data analyses were performed using Stata version 17.0 with a significance level of p<0.05. Results: The prevalence of MS is 36.19% (95%CI: 27.04%,46.15%). Body mass index (BMI) peaks at obese class I among those with MS. There is a significantly higher proportion of patients diagnosed to have diabetes (28.95% vs. 7.46%; p=0.003) and hypertension (52.63% vs. 14.93%; p<0.001) in the MS group. No significant differences were noted between groups regarding age, sex, etiology of hypothyroidism, blood pressure, fasting glucose, lipid profile, and thyroid hormone levels. Conclusion Our study showed that the prevalence of MS in adult Filipinos with hypothyroidism is increased at 36.19%. Only BMI, presence of diabetes, and hypertension were shown to be significantly higher. Emphasis must be placed on early screening among hypothyroid patients at high risk of developing MS. A prospective study using waist circumference and clinical and metabolic parameters is needed to validate these findings.
dyslipidemia ; hypothyroidism ; metabolic syndrome ; prevalence

Obesity and overweight are considered health risks for non-communicable diseases. Most clinical practice guidelines suggest lifestyle modification as the primary management and as an adjunct to pharmacologic treatment. Using the SMART-EST goaloriented action plan and lifestyle medicine may improve weight reduction outcomes. The case presented had clinical criteria (3/5) for metabolic syndrome, diagnosed previously as obese stage II, asthma moderately uncontrolled, prediabetes, and mild dyslipidemia. Baseline weight and waist-to-hip ratio were taken and interpreted as obese stage II with a very severe risk for comorbidity. The diagnostics used were fasting plasma glucose, lipid profile, HbA1c, and 2-D echocardiography on the eight months of intervention only. Using the SMART-EST action plan plus Lifestyle medicine approaches as non-pharmacologic management for metabolic syndrome. There was an 11.4 % reduction in weight, improved fasting glucose, lipid profile, and 2-D echocardiography within the eight months of intervention.
Metabolic syndrome

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

UNLABELLED: AbstractObjective: To analysis the clinical data of patients after single-center hematopoietic stem cell transplantation (HSCT) and construct a predictive model for metabolic syndrome (MS) diagnosis. METHODS: Ninety-three hematology patients who underwent HSCT at the First Hospital of Lanzhou University from July 2015 to September 2022 were selected to collect basic data, transplantation status and postoperative data, the clinical characteristics of patients with and without MS after transplantation were compared and analyzed. Logistic regression model was used to analyze the influence fators of MS after transplantation, and a predictive model of HSCT-MS diagnosis was constructed under the influence of independent influence factors. The model was evaluated using the ceceiver operating characteristic curve (ROC curve). RESULTS: Metabolic syndrome occurred in 36 of 93 HSCT patients and did not occur in 57. Compared with non-HSCT-MS group, HSCT-MS had significantly higher fasting blood glucose (FBG) levels before transplantation, shorter course before transplantation, and higher bilirubin levels after transplantation (P<0.05). The statistically significant clinical indicators were subjected to multi-factor logistic regression analysis, and the results showed that pre-transplant high FBG, pre-transplant short disease course and post-transplant high bilirubin were independent influence factors for HSCT-MS. The standard error of predicting the occurrence of HSCT-MS based on the clinical model was 0.048, the area under the curve AUC=0.776, 95% CI :0.683-0.869, the optimal threshold was 0.58 based on the Jorden index at maximum, the sensitivity was 0.694, and the specificity was 0.772, which has certain accuracy. CONCLUSION A clinical prediction model for HSCT-MS based on logistic regression analysis is constructed through the analysis of clinical data, which has certain clinical value.
Humans ; Metabolic Syndrome ; Prognosis ; Models, Statistical ; Hematopoietic Stem Cell Transplantation ; ROC Curve ; Retrospective Studies

Objective: To understand the depression status and its influencing factors in elderly patients with MS in China and to explore the correlation between various components of elderly MS and depression. Methods: This study is based on the "Prevention and Intervention of Key Diseases in Elderly" project. We used a multi-stage stratified cluster random sampling method to complete 16 199 elderly aged 60 years and above in 16 counties (districts) in Liaoning, Henan, and Guangdong Provinces in 2019, excluding 1 001 missing variables. Finally, 15 198 valid samples were included for analysis. The respondents' MS disease was obtained through questionnaires and physical examinations, and the respondents' depression status within the past half month was assessed using the PHQ-9 Depression Screening Scale. The correlation between elderly MS and its components and depression and its influencing factors were analyzed by logistic regression. Results: A total of 15 198 elderly aged 60 years and above were included in this study, with the prevalence of MS at 10.84% and the detection rate of depressive symptoms in MS patients at 25.49%. The detection rates of depressive symptoms in patients with 0, 1, 2, 3, and 4 MS abnormal group scores were 14.56%, 15.17%, 18.01%, 25.21%, and 26.65%, respectively. The number of abnormal components of MS was positively correlated with the detection rate of depressive symptoms, and the difference between groups was statistically significant (P<0.05). The risk of depression symptoms in patients with MS, overweight/obesity, hypertension, diabetes, and dyslipidemia was 1.73 times (OR=1.73, 95%CI:1.51-1.97), 1.13 times (OR=1.13, 95%CI:1.03-1.24), 1.25 times (OR=1.25, 95%CI:1.14-1.38), 1.41 times (OR=1.41, 95%CI:1.24-1.60), 1.81 times (OR=1.81,95%CI:1.61-2.04), respectively, more than those without the disease. Multivariate logistic regression analysis showed that the detection rate of depressive symptoms in patients with sleep disorders was higher than that with normal sleep (OR=4.89, 95%CI: 3.79-6.32). The detection rate of depressive symptoms in patients with cognitive dysfunction was 2.12 times higher than that in the average population (OR=2.12, 95%CI: 1.56-2.89). The detection rate of depressive symptoms in patients with impaired instrumental activities of daily living (IADL) was 2.31 times (OR=2.31, 95%CI: 1.64-3.26) higher than that in the average population. Tea drinking (OR=0.73, 95%CI: 0.54-0.98) and physical exercise (OR=0.67, 95%CI: 0.49-0.90) seemed to be protective factors for depression in elderly MS patients (P<0.05). Conclusions: Older patients with MS and its component abnormalities have a higher risk of depression than the average population. Sleep disorders, cognitive impairment, and IADL impairment are important influencing factors for depression in elderly MS patients, while tea drinking and physical exercise may help to reduce the risk of the disease.
Aged ; Humans ; Metabolic Syndrome/epidemiology* ; Activities of Daily Living/psychology* ; Depression/epidemiology* ; China/epidemiology* ; Tea ; Risk Factors

OBJECTIVE: To analyze the association between different growth patterns and metabolic syndrome in children and adolescents aged 7 to 17 years, and to provide suggestions for the prevention and control of metabolic syndrome in Chinese children and adolescents. METHODS: Data were collected from the research project "Development and Application of Technology and Related Standards for Prevention and Control of Major Diseases among Students" of public health industry in 2012. This project is a cross-sectional study design. A total of 65 347 students from 93 primary and secondary schools in 7 provinces including Guangdong were selected by stratified cluster random sampling method. Given the budget, 25% of the students were randomly selected to collect blood samples. In this study, 10 176 primary and middle school students aged 7 to 17 years with complete physical measurements and blood biochemical indicators were selected as research objects. Chi-square test was used to compare the distribution differences of growth patterns under different demographic characteristics. Birth weight, waist circumference and blood biochemical indexes were expressed in the form of mean ± standard deviation, and the differences among different groups were compared by variance analysis. Binary Logistic regression model was used to analyze the relationship between different growth patterns and metabolic syndrome in children and adolescents aged 7 to 17 years. RESULTS: The prevalence of metabolic syndrome in children and adolescents was 6.56%, 7.18% in boys and 5.97% in girls. The risk of metabolic syndrome was higher in the catch-down growth group than in the normal growth group (OR=1.417, 95%CI: 1.19-1.69), and lower in the catch-up growth group(OR=0.66, 95%CI: 0.53-0.82). After adjusting for gender, age and so on, the risk of developing metabolic syndrome in the catch-down growth group was higher than that in the normal growth group (OR=1.25, 95%CI: 1.02-1.52), but there was no significant difference between the catch-up growth group and the normal growth group (OR=0.79, 95%CI: 0.62-1.01). Stratified analysis showed that the association between different growth patterns and metabolic syndrome was statistically significant in the 7-12 years group, urban population, and Han Chinese student population. CONCLUSION There is a correlation between different growth patterns and metabolic syndrome in children and adolescents. The risk of developing metabolic syndrome in children and adolescents with catch-down growth is higher than that in the normal growth group, which suggests that attention should be paid to the growth and development of children and adolescents, timely correction of delayed growth and prevention of adverse health outcomes.
Male ; Female ; Humans ; Child ; Adolescent ; Metabolic Syndrome/epidemiology* ; Cross-Sectional Studies ; Students ; Urban Population ; Asian People ; China/epidemiology* ; Prevalence

Background: Cardiovascular diseases are the leading causes of deaths among adults in the Philippines, and this is true also among older persons aged 60 years and above. Identification of risk factors and diseases that lead to cardiovascular mortality among the elderly is important to have an impact on longevity. Objective: This study aimed to determine the prevalence of metabolic syndrome and specific cardiovascular risk factors among older persons in the Philippines. Methods: A cross sectional design was used, with data taken from the results of the 8th Philippine National Nutrition Survey (NNS) Clinical and Health Survey done in 2013. Although there is a more recent survey, only the 2013 data is complete and available for secondary analyses. Results: There were 1,835 older persons who were participants in the 8th Philippine NNS who had complete clinical data. The prevalence of Metabolic Syndrome was 52.6% in this population with the distribution of the components as follows: 33.5% have elevated waist circumference; 59.25 had elevated BP >130/85 mm Hg; 30.1% had fasting blood glucose ≥100 mg/dL (includes prediabetes and diabetes); 63% have low HDL, and 39% have elevated triglycerides ≥150 mg/dL. The results for other cardiovascular diseases and risk factors are as follows: 44% had hypertension using the criterion of the JNC VII report (BP ≥140/90 mm Hg); 85% had LDL cholesterol ≥100 mg/dL; 31% are current alcohol drinkers; 22% are current smokers; 53.7% have low physical activity; and 82% have an unhealthy diet. Conclusions Fifty-two percent (52%) of older Filipinos have metabolic syndrome and have a high prevalence of cardiovascular risk factors, foremost of which are elevated LDL-cholesterol at 85%, hypertension at 44% based on JNC VII, approximately 10% with diabetes mellitus, but with a double burden of overweight/obesity and undernutrition. This data can help plan for public health approaches to improve quality of life and increase longevity of Filipinos.
Metabolic Syndrome ; Heart Disease Risk Factors