PATIENTS AND METHODS: Between January 2017 and December 2018, a total of 120 patients (101 males, 19 females; mean age: 35.1±3.0 years; range, 18 to 72 years) treated with IMN for closed DFFs were retrospectively analyzed. Data including age, sex, location, weight, height, comorbidities such as diabetes mellitus, hypertension or kidney injury, date of injury, mechanism of injury, type of femoral fractures (AO classification), date of surgery, duration of surgery, IMN length and diameter used, date of radiological fracture union and complications of surgery such as nonunion, delayed union, and infections were recorded.

RESULTS: Of the patients, 63 had obesity and 57 did not have obesity. There was a statistically significant difference in fracture configuration among patients with obesity; they sustained type B (p=0.001) and type C (p=0.024), the most severe fracture configuration. The nonunion rate was 45%. Obesity had a significant relationship with fracture nonunion with patients with obesity having the highest number of nonunion rates (n=40, 74.1%) compared to those without obesity (n=14, 25.9%) (p=0.001). Fracture union was observed within the first 180 days in 78.9% of patients without obesity, while it developed in the same time interval in only 38.1% of patients with obesity (p=0.001).

OBJECTIVE: To investigate the accuracy of anthropometric indices as a screening tool for predicting MetS among apparently healthy individuals in Karachi, Pakistan.

METHODS: A community-based cross-sectional survey was conducted in Karachi, Pakistan, from February 2022 to August 2022. A total of 1,065 apparently healthy individuals aged 25 years and above were included. MetS was diagnosed using International Diabetes Federation guidelines. Anthropometric indices were defined based on body mass index (BMI), neck circumference (NC), mid-upper arm circumference (MUAC), waist circumference (WC), waist to height ratio (WHtR), conicity index, reciprocal ponderal index (RPI), body shape index (BSI), and visceral adiposity index (VAI). The analysis involved the utilization of Pearson's correlation test and independent t-test to examine inferential statistics. The receiver operating characteristic (ROC) analysis was also applied to evaluate the predictive capacities of various anthropometric indices regarding metabolic risk factors. Moreover, the area under the curve (AUC) was computed, and the chosen anthropometric indices' optimal cutoff values were determined.

RESULTS: All anthropometric indices, except for RPI in males and BSI in females, were significantly higher in MetS than those without MetS. VAI [AUC 0.820 (95% CI 0.78-0.86)], WC [AUC 0.751 (95% CI 0.72-0.79)], WHtR [AUC 0.732 (95% CI 0.69-0.77)], and BMI [AUC 0.708 (95% CI 0.66-0.75)] had significantly higher AUC for predicting MetS in males, whereas VAI [AUC 0.693 (95% CI 0.64-0.75)], WHtR [AUC 0.649 (95% CI 0.59-0.70)], WC [AUC 0.646 (95% CI 0.59-0.61)], BMI [AUC 0.641 (95% CI 0.59-0.69)], and MUAC [AUC 0.626 (95% CI 0.57-0.68)] had significantly higher AUC for predicting MetS in females. The AUC of NC for males was 0.656 (95% CI 0.61-0.70), while that for females was 0.580 (95% CI 0.52-0.64). The optimal cutoff points for all anthropometric indices exhibited a high degree of sensitivity and specificity in predicting the onset of MetS.

DESIGN: Using a descriptive qualitative research approach informed by Levesque's framework of access to healthcare, we conducted phone interviews in the Malaysian language, which were audio-recorded and transcribed verbatim. Data were analysed inductively using a reflexive thematic analysis approach.

SETTING: Primary care clinics in five states in Peninsular Malaysia.

PARTICIPANTS: Adult patients with obesity receiving face-to-face care for obesity from healthcare providers in Peninsular Malaysia.

RESULTS: We interviewed 22 participants aged 24-62, with the majority being female (77%), Malay (95%), married (73%) and with tertiary education (82%). Most participants attended obesity management services at public primary care clinics. We identified five themes: (1) moving from perceiving the need to seeking obesity care is a non-linear process for patients, (2) providers' words can inspire patients to change, (3) patients' needs and preferences are not adequately addressed in current obesity care, (4) over-focusing on weight by patients and healthcare providers can lead to self-blame and loss of hope for patients and (5) obesity healthcare can have consequences beyond weight loss.

METHODS: Data from four population-based National Health and Morbidity Surveys conducted in 1996, 2006, 2010, and 2015 were pooled. Hierarchical Age-Period-Cohort (HAPC) analysis explored the trajectories of BMI and waist circumference across the life course and birth cohorts by sex and ethnicity. These models assumed no period effect.

RESULTS: Generally, BMI and waist circumference trajectories increased across age and birth cohorts. These trajectories varied by sex and ethnicity. Females have more profound increasing BMI and waist circumference trajectories than their male counterparts as they age and as cohort recency increases. Chinese have less profound BMI and waist circumference increases across the life course and birth cohorts than other ethnic groups.

MATERIALS AND METHODS: This cross-sectional study was conducted on 124 breast cancer outpatients within the first year of diagnosis and yet to commence oncological treatment. Body composition parameters [body weight, body mass index (BMI), body fat percentage, fat mass over fat-free mass ratio (FM/FFM), muscle mass, and visceral fat] were obtained using a bioelectrical impedance analyzer. Body fat percentage was categorized into two groups which were normal (<35%) and high (≥35%). The E-DII was calculated from the validated 165-items Food Frequency Questionnaire (FFQ) and categorized into three groups or tertiles. Multiple logistic regression analysis was used to determine the association between the E-DII and body fat percentage.

RESULTS: Mean body weight, body fat percentage, FM/FFM, and visceral fat increased as E-DII increased from the lowest tertile (T1) to the most pro-inflammatory tertile (T3) (p for trend <0.05). E-DII was positively associated with body fat percentage (OR 2.952; 95% CI 1.154-7.556; p = 0.024) and remained significant after adjustment for cancer stage, age, physical activity, ethnicity, smoking history, and presence of comorbidities. Compared to T1, participants in T3 had a significantly lower consumption of fiber, vitamin A, beta-carotene, vitamin C, iron, thiamine, riboflavin, niacin, vitamin B6, folic acid, zinc, magnesium, and selenium, but a higher intake of total fat, saturated fat, and monounsaturated fatty acids.

MATERIALS AND METHODS: This cross-sectional study recruited women referred to physiotherapy to manage OA. The measurements included fatigue severity (fatigue severity scale); pain level (numerical rating scale); obesity indices (body mass index, fat %, waist circumference); functional performances (upper limb strength, lower limb strength, mobility, exercise capacity and quality of life). A simple linear regression analysis was used to determine which independent variable may be associated with fatigue severity.

RESULTS: Ninety-six women with unilateral KOA participated in this study (Mean age, 55.70, Standard Deviation, SD 6.90) years; Mean fatigue severity, 34.51, SD 14.03). The simple linear regression analysis showed that pain level (β=4.089, p<0.001), fat % (β=0.825, p<0.001) and QoL (β=0.304, p<0.001) were significantly associated with fatigue. After controlling for pain level, only fat % was significantly associated with fatigue (β=0.581, p=0.005).

METHODS: We conducted a gene-environment interaction (GxE) analysis including 8,255 cases and 11,900 controls from four pancreatic cancer genome-wide association study (GWAS) datasets (Pancreatic Cancer Cohort Consortium I-III and Pancreatic Cancer Case Control Consortium). Obesity (body mass index ≥30 kg/m2) and diabetes (duration ≥3 years) were the environmental variables of interest. Approximately 870,000 SNPs (minor allele frequency ≥0.005, genotyped in at least one dataset) were analyzed. Case-control (CC), case-only (CO), and joint-effect test methods were used for SNP-level GxE analysis. As a complementary approach, gene-based GxE analysis was also performed. Age, sex, study site, and principal components accounting for population substructure were included as covariates. Meta-analysis was applied to combine individual GWAS summary statistics.

RESULTS: No genome-wide significant interactions (departures from a log-additive odds model) with diabetes or obesity were detected at the SNP level by the CC or CO approaches. The joint-effect test detected numerous genome-wide significant GxE signals in the GWAS main effects top hit regions, but the significance diminished after adjusting for the GWAS top hits. In the gene-based analysis, a significant interaction of diabetes with variants in the FAM63A (family with sequence similarity 63 member A) gene (significance threshold P < 1.25 × 10-6) was observed in the meta-analysis (P GxE = 1.2 ×10-6, P Joint = 4.2 ×10-7).

CONCLUSIONS: This analysis did not find significant GxE interactions at the SNP level but found one significant interaction with diabetes at the gene level. A larger sample size might unveil additional genetic factors via GxE scans.

AIMS: We evaluated the performance of machine learning (ML) and non-patented scores for ruling out SF among NAFLD/MASLD patients.

METHODS: Twenty-one ML models were trained (N = 1153), tested (N = 283), and validated (N = 220) on clinical and biochemical parameters of histologically-proven NAFLD/MASLD patients (N = 1656) collected across 14 centres in 8 Asian countries. Their performance for detecting histological-SF (≥F2fibrosis) were evaluated with APRI, FIB4, NFS, BARD, and SAFE (NPV/F1-score as model-selection criteria).

RESULTS: Patients aged 47 years (median), 54.6% males, 73.7% with metabolic syndrome, and 32.9% with histological-SF were included in the study. Patients with SFvs.no-SF had higher age, aminotransferases, fasting plasma glucose, metabolic syndrome, uncontrolled diabetes, and NAFLD activity score (p  140) was next best in ruling out SF (NPV of 0.757, 0.724 and 0.827 in overall, test and validation set).