METHODOLOGY: This was a cross-sectional study on 258 patients with T2DM duration of at least 10 years. Transient elastography (FibroScan®) was performed on all subjects. Advanced liver fibrosis was diagnosed based on LSM results. The FIB-4 index formula was used.

RESULTS: The prevalence of advanced liver fibrosis was 22.1%. Associated factors were body mass index (BMI), alanine transaminase (ALT), aspartate transaminase (AST), gamma-glutamyl transferase (GGT), triglyceride (TG) and high-density lipoprotein (HDL) cholesterol. Independent factors were BMI and GGT (p=0.003 and p<0.001). FIB-4 index has 30.0% sensitivity, 85.0% specificity, 38.7% positive predictive value, and 79.4% negative predictive value in detecting advanced liver fibrosis by LSM criteria.

Methodology: We used a retrospective, matched before and after study design to prevent biased levels of effort by students conducting the home visits over two years. Information was obtained through reports written by IMU students. Convenient sampling was used to select outpatients undergoing treatment 'as usual' from a health clinic and were subsequently matched as controls.

Results: There was a significant decrease in the mean HbA1c among 57 patients with diabetes who were CFCS subjects [from 8.4% (68 mmol/mol) to 7.3% (57 mmol/mol) p<0.001], while the mean HbA1c levels among 107 matched control subjects rose significantly from 7.9% (63 mmol/mol) to 8.3% (67 mmol/mol) (p=0.019) over a similar period. The two groups were controlled for most biological and socioeconomic variables except for comorbidities, diabetic complications and medication dose changes between groups.

Methodology: A 12-week prospective, non-controlled, interventional study in suboptimal-controlled T2DM patients with DFU was conducted. Antidiabetic medications were adjusted with the aim of at least 1% in relation to patient's individualised HbA1c target. The wound area was determined by using specific wound tracing. The daily wound area healing rate in cm2 per day was calculated as the difference between wound area at first visit and the subsequent visit divided by the number of days between the two visits.

Results: 19 patients were included in the study. There was a significant HbA1c reduction from 10.33 %+1.83% to 6.89%+1.4% (p<0.001) with no severe hypoglycaemia. The median daily wound area healing rate was 0.234 (0.025,0.453) cm2/day. There was a strong positive correlation between these two variables (r=0.752, p=0.01). After dividing the patients into four quartiles based on final HbA1c and comparing the first quartile vs fourth quartile, there was a significant difference in daily wound area healing rates (0.597 vs 0.044 cm2/day, p=0.012).

Methodology: We conducted a prospective study in patients with T2DM on twice-daily MHI with or without metformin therapy. Blinded continuous glucose monitoring was performed at baseline and following 6 weeks of Vildagliptin therapy.

Results: Twelve patients with mean (SD) age of 55.8 (13.1) years and duration of disease of 14.0 (6.6) years were recruited. The addition of Vildagliptin significantly reduced GV indices (mmol/L): SD from 2.73 (IQR 2.12-3.66) to 2.11 (1.76-2.55), p=0.015; mean amplitude of glycemic excursions (MAGE) 6.94(2.61) to 5.72 (1.87), p=0.018 and CV 34.05 (8.76) to 28.19 (5.36), p=0.010. In addition, % time in range (3.9-10 mmol/l) improved from 61.17 (20.50) to 79.67 (15.33)%, p=0.001; % time above range reduced from 32.92 (23.99) to 18.50 (15.62)%, p=0.016; with reduction in AUC for hyperglycemia from 1.24 (1.31) to 0.47 (0.71) mmol/day, p=0.015. Hypoglycemic events were infrequent and the reduction in time below range and AUC for hypoglycemia did not reach statistical significance.

Methodology: This sub-analysis included Filipino patients with T1DM or T2DM, aged 18 years and older, treated with insulin for more than 12 months, who completed the two-part self-assessment questionnaires (SAQ1 and SAQ2) and patient diaries that recorded hypoglycemia during retrospective (6 months/4 weeks before baseline) and prospective period (4 weeks after baseline) (ClinicalTrials.gov number: NCT02306681).

Results: A total of 671 patients were enrolled and completed the SAQ1 (62 patients with T1DM and 609 patients with T2DM). Almost all patients (100% in T1DM and 99.3% in T2DM) experienced at least 1 hypoglycemic event prospectively. The incidence of any hypoglycemia was also high in the prospective period compared to retrospective period (72.6 [95% CI: 64.8, 80.9] events PPY and 43.6 [95% CI: 37.8, 49.9] events PPY; p=0.001, respectively) in T1DM patients.

Methodology: We recruited 70 participants with the mean age of 10.1 ± 2.94 years with exogenous or simple form of obesity from June 2019 until September 2020. We analyzed their demography (age, gender, ethnicity, family background), measured their anthropometry (weight, height, BMI) and monitored monthly weight increment and finally analyzed their HOMA-IR at baseline and after 6 months of follow up.

Results: The mean time to gain 5 kg from baseline was 16 weeks (95% CI): (15.2, 16.7). Multivariate analysis showed only HOMA-IR after 6 months was a significant predictor affecting time to gain 5 kg; Adjusted HR: (95% CI) 1.617 (1.232, 2.123), (p=0.001).

Methodology: A total of 205 patients who fit eligibility criteria were included in the study. A questionnaire was completed, and blood was drawn to study vitamin B12 levels. Vitamin B12 deficiency was defined as serum B12 level of ≤300 pg/mL (221 pmol/L).

Results: The prevalence of vitamin B12 deficiency among metformin-treated patients with type 2 DM patients was 28.3% (n=58). The median vitamin B12 level was 419 (±257) pg/mL. The non-Malay population was at a higher risk for metformin-associated vitamin B12 deficiency [adjusted odds ratio (OR) 3.86, 95% CI: 1.836 to 8.104, p<0.001]. Duration of metformin use of more than five years showed increased risk for metformin-associated vitamin B12 deficiency (adjusted OR 2.06, 95% CI: 1.003 to 4.227, p=0.049).

METHODOLOGY: We recruited 175 subjects, aged 7 to 18 years old, referred for obesity. We studied their demography (age, gender, ethnicity, family background), performed clinical/auxological examinations [weight, height, body mass index (BMI), waist circumference (WC), blood pressure (BP)], and analyzed their biochemical risks associated with metabolic syndrome [fasting plasma glucose (FPG), fasting lipid profile (FLP), fasting insulin, liver function tests (LFT)]. MetS was identified according to the criteria proposed by the International Diabetes Federation (IDF) for pediatric obesity. Multiple logistic regression models were used to examine the associations between risk variables and MetS.

RESULTS: The prevalence of metabolic syndrome among children with obesity was 56% (95% CI: 48.6 to 63.4%), with a mean age of 11.3 ± 2.73 years. Multiple logistic regression analysis showed age [adjusted odds ratio (OR) 1.27, 95% CI: 1.15 to 1.45] and sedentary lifestyle (adjusted OR 3.57, 95% CI: 1.48 to 8.59) were the significant factors associated with metabolic syndrome among obese children.