OBJECTIVE

This study aimed to determine the association between Asia-Pacific classification of body mass index, waist circumference and waist hip ratio and clinical outcomes of COVID-19 moderate & severe patients at the height of the COVID-19 pandemic.

A This study involved adult patients diagnosed with COVID-19. 182 patients were analyzed and divided into 167 moderate and 15 severe COVID-19 patients. Primary outcomes (respiratory decompensation, septic shock, and mortality) of patients were compared among Asia Pacific BMI groups.

Among patients with moderate and severe COVID-19, 7 out of 10 patients were obese. Respiratory decompensation and sepsis were more frequently seen in obese patients. Obesity and waist circumference were significantly associated with the odds of respiratory decompensation (95% CI p=0.010 and p=0.002), however this association was not sustained upon adjustment for confounders. On univariate analysis, waist and hip circumferences were significantly associated with the odds of ICU admission (95% CI, p=.013 and p=.034), however after controlling for confounders, only hip ratio retained significant association. Among patients with severe COVID-19, there was insufficient evidence to support significant variations in distributions of outcomes of interest across Asia-Pacific BMI groups.

Our study emphasized that although respiratory decompensation and sepsis were more frequently seen in obese patients. progression of respiratory decompensation and mortality is not significantly associated with obesity as defined by the Asia Pacific BMI classification, warranting the need for larger prospective studies.

BACKGROUND: Chronic illnesses such as Type 2 diabetes mellitus (T2DM) and obesity have been implicated as risk factors in the development of cognitive impairment (CI), but despite this, definite association between the two conditions in increasing cognitive impairment risk is not well defined. OBJECTIVE: This study aims to examine the association between body mass index (BMI) and cognitive impairment (CI) in elderly patients with Type 2 diabetes mellitus. METHODS: This is a cross-sectional study conducted in the outpatient clinics of a private hospital in Manila which included elderly patients with Type 2 diabetes. BMI categories of the subjects were determined using the Asia-Pacific criteria and the Montreal Cognitive Assessment – Philippines (MOCA-P) was administered to subjects who fulfilled the inclusion criteria. Descriptive statistics were used to determine the prevalence of impaired cognition among subjects while risk ratio analysis was used to determine the correlation between BMI and CI. Correlation analysis and linear regression analysis were used to determine the presence of association between cognition (measured by MOCA-P scores) and BMI. For all analysis, a 95% level of significance was used. RESULTS: A total of 109 subjects from the outpatient clinics were included in the study. A high percentage of the study population (90.83%) had CI based on MOCA-P scores. Subjects that belonged to the extremes of BMI- underweight and obese class 2 had higher incidence of CI compared to the other groups. Underweight subjects had 1.103 (95% CI: 1.038 to 1.172) times likelihood of having impaired cognition (p-value 0.0016), while obese 2 subjects had 1.110 (95% CI: 1.040 to 1.184) times likelihood of having impaired cognition (p-value 0.0016). Regression analysis revealed that in subjects with diabetes of less than 10 years, cognition scores were negatively correlated to BMI (p-value 0.0454). Correlation analysis revealed that at the general population level, regardless of the external factors, increasing or decreasing BMI did not have significant effect on cognition scores. CONCLUSION Subjects who belonged to the extremes of BMI-underweight and obese class 2 – had higher incidence of CI compared to the other BMI groups. Among subjects with T2DM duration of less than 10 years, cognition scores tend to be negatively correlated to BMI.
diabetes mellitus, Type 2 ; cognitive impairment ; cognitive dysfunction ; Body Mass Index

Background: The association of obesity with adverse COVID-19 outcomes is known, but unexplored in younger adults. Objective: To determine the association of obesity [body mass index (BMI) of ≥30] with severe COVID-19 outcomes in younger and older adults. Design: Retrospective cohort study. Participants: 391 patients with COVID-19 (226 younger adults aged 18-60 years, and 165 older adults aged >60 years). Setting: Southern Philippines Medical Center, Davao City, January 2021 to September 2021. Main outcome measures: Severe COVID-19 outcomes (high-flow oxygen administration, ICU admission, mechanical ventilation, death); odds ratio of severe outcomes in patients with BMI of ≥30. Main results: Of 391 patients (median age of 57 years), 286 had a BMI of <30, while 105 had a BMI of ≥30. Univariate regression analysis showed that a BMI of ≥30 was significantly associated with any severe COVID-19 outcomes (OR=2.68; 95% CI 1.68 to 4.27; p<0.001). This remained after adjusting for age, sex, hypertension, diabetes, and cardiovascular disease (adjusted OR=3.19; 95% CI 1.93 to 5.27; p<0.001). A BMI of ≥30 was also significantly associated with any severe outcomes among younger adults (adjusted OR=4.04; 95% CI 2.23 to 7.32; p<0.001), but not among older adults (adjusted OR=1.80; 95% CI 0.70 to 4.64; p=0.227). Conclusion In our study, among all adults, a BMI of ≥30 significantly increased the odds of experiencing any severe COVID-19 outcomes. This association was also observed in the younger adult subgroup, but not in the older adult subgroup.
SARS-CoV-2 ; Body Mass Index ; Immunity ; Critical Care

Obesity ; Body Mass Index ; Leptin ; Inflammation ; Adipose Tissue

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

Humans ; Cohort Studies ; Body Mass Index ; Percutaneous Coronary Intervention ; Coronary Artery Disease ; Inflammation ; Treatment Outcome ; Risk Factors

OBJECTIVE: This study aimed to assess the relationship between the body composition of children aged 3-5 years and breastfeeding status and duration. METHODS: The study was conducted using data from the National Nutrition and Health Systematic Survey for children 0-17 years of age in China (CNHSC), a nationwide cross-sectional study. Breastfeeding information and potential confounders were collected using standardized questionnaires administered through face-to-face interviews. The body composition of preschool children was measured using bioelectrical impedance analysis. A multivariate linear regression model was used to assess the relationship between breastfeeding duration and body composition after adjusting for potential confounders. RESULTS: In total, 2,008 participants were included in the study. Of these, 89.2% were ever breastfed and the median duration of breastfeeding was 12 months (IQR 7-15 months). Among children aged 3 years, the height-for-age Z-score (HAZ) for the ever breastfed group was lower than that for never breastfed group (0.12 vs. 0.42, P = 0.043). In addition, the weight-for-age Z-score (WAZ) of the ever breastfed group was lower than that of the never breastfed group (0.31 vs. 0.65, P = 0.026), and the WAZ was lower in children aged 4 years who breastfed between 12 and 23 months than in those who never breastfed. Compared to the formula-fed children, the fat-free mass of breastfed infants was higher for children aged 3 years (12.84 kg vs. 12.52 kg, P = 0.015) and lower for those aged 4 years (14.31 kg vs. 14.64 kg, P = 0.048), but no difference was detected for children aged 5 years (16.40 kg vs. 16.42 kg, P = 0.910) after adjusting for potential confounders. No significant difference was detected in the weight-for-height Z-score (WHZ), body mass index (BMI)-for-age Z-score (BAZ), fat-free mass index, and body fat indicators in the ever breastfed and never breastfed groups and among various breastfeeding duration groups for children aged 3-5 years. CONCLUSION No obvious associations were detected between breastfeeding duration, BMI, and fat mass indicators. Future prospective studies should explore the relationship between breastfeeding status and fat-free mass.
Infant ; Female ; Child, Preschool ; Humans ; Infant, Newborn ; Child ; Adolescent ; Breast Feeding ; Prospective Studies ; Cross-Sectional Studies ; Body Mass Index ; Body Composition

OBJECTIVE: To investigate the effect of body mass index (BMI) on the short-term effectiveness of high tibial osteotomy (HTO) in the treatment of varus knee arthritis. METHODS: The clinical data of 84 patients (84 knees) with varus knee arthritis treated with HTO between May 2016 and August 2020 were retrospectively analyzed. According to BMI, the patients were divided into normal group (32 patients in group A, BMI<25 kg/m ²), overweight group (27 patients in group B, BMI>30 kg/m ²), and obese group (25 patients in group C, BMI>30 kg/m ²). The BMI of groups A, B, and C were (23.35±0.89), (26.65±1.03), and (32.05±1.47) kg/m ², respectively. There was no significant difference ( P>0.05) in gender, age, surgical side, disease duration, and preoperative Hospital for Special Surgery (HSS) score, visual analogue scale (VAS) score, knee range of motion, and hip-knee-ankle angle (HKA) between groups. The operation time, intraoperative dominant blood loss, and the decrease of hemoglobin on the 3rd day after operation were recorded and compared between groups. The improvement of knee joint function and pain status were evaluated by knee joint HSS score, knee range of motion, and VAS score before and after operation, and measuring the HKA of patients on X-ray film. During the follow-up, the X-ray films of the knee joint were reexamined to observe the position of the internal fixator and the healing of osteotomy. RESULTS: All patients completed the operation successfully and were followed up 8-40 months (mean, 19.3 months). There was no significant difference in follow-up time, operation time, intraoperative dominant blood loss, and the decrease of hemoglobin on the 3rd day after operation between groups ( P>0.05). No operative complications such as severe vascular or nerve injury occurred. After operation, deep venous thrombosis of lower extremities occurred in 1 case in groups A and B respectively, and fat liquefaction of surgical incision occurred in 2 cases in group C. There was no significant difference in the incidence of perioperative complications between groups (3.1% vs. 3.7% vs. 8.0%) ( P=0.689). During the follow-up, there was no bone nonunion, plate fracture or loosening. At last follow-up, HSS score, VAS score, knee range of motion, and HKA significantly improved in the 3 groups when compared with those before operation ( P<0.05), but there was no significant difference in the differences of the above indexes between groups before and after operation ( P>0.05). CONCLUSION BMI does not affect the short-term effectiveness of HTO in the treatment of varus knee arthritis. HTO can be selected for overweight and obese patients after standard medical treatment is ineffective.
Humans ; Osteoarthritis, Knee/surgery* ; Body Mass Index ; Overweight ; Retrospective Studies ; Treatment Outcome ; Knee Joint/surgery* ; Obesity/complications* ; Osteotomy ; Blood Loss, Surgical

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

OBJECTIVES: To study the correlation of intestinal dominant flora with hyperuricemia, and to explore influencing factors of hyperuricemia. METHODS: Data of gut dominant microbiota were collected from subjects who underwent health check-up in Shulan (Hangzhou) Hospital from January 2018 to April 2020. Subjects with high uric acid and normal uric acid were matched by propensity score matching method according to age, gender and body mass index (BMI). This resulted in 178 pairs as hyperuricemia group and control group. The gut dominant microbiota between hyperuricemia and normal control group were compared. Pearson or Spearman correlation coefficient method was used to analyze the correlation between blood uric acid and intestinal dominant flora. Univariate and multivariate logistic regression were used to analyze the influencing factors of hyperuricemia. RESULTS: The abundance of Atopobium, Lactobacillus, Bacteroides, Enterococcus, Clostridium leptum, Fusobacterium prausnitzii, Bifidobacterium, Clostridium butyricum and the ratio of Bifidobacterium to Enterobacter (B/E) in the hyperuricemia group were significantly lower than those in the control group (all P<0.01). The correlation analysis showed that serum uric acid were negatively correlated with the abundance of Atopobium (r=－0.224, P<0.01), Bacteroides (r=－0.116, P<0.05), Clostridium leptum (r=－0.196, P<0.01), Fusobacterium prausnitzii (r=－0.244, P<0.01), Bifidobacterium (r=－0.237, P<0.01), Eubacterium rectale (r=－0.125, P<0.05), Clostridium butyricum (r=－0.176, P<0.01) and B/E value (r=－0.127, P<0.05). Multivariate logistic regression analysis showed that glutamyl transpeptidase was an independent risk factor for hyperuricemia (OR=1.007, 95%CI: 1.002-1.012, P<0.05), and the Atopobium was an independent protective factor for hyperuricemia (OR=0.714, 95%CI: 0.605-0.842, P<0.01). CONCLUSIONS There are alterations in abundance of gut dominant microbiota in patients with hyperuricemia, and Atopobium abundance appears as a protective factor for hyperuricemia.
Humans ; Uric Acid ; Hyperuricemia ; Body Mass Index ; Risk Factors ; Microbiota