This cross-sectional survey aimed to assess the relationship between obesity and microalbuminuria in children and adolescents with diabetes mellitus attending a tertiary care outpatient clinic. The study was performed at the Changing Diabetes in Children (CDiC) Outpatient Department of BIRDEM-2 Hospital in Bangladesh over a 1-year period from July 2018 to June 2019.
A total of 512 children and adolescents with diabetes mellitus were enrolled. Diabetes mellitus was diagnosed according to the World Health Organization (WHO), International Society for Paediatric and Adolescent Diabetes (ISPAD) and Mohan's criteria as reported by the authors. Within the cohort, 61.9% had type 1 diabetes mellitus and 38.1% had type 2 diabetes mellitus.
Height, weight and waist circumference were recorded for each participant, and body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared (kg/m²). BMI categories were assigned using the Center for Disease Control and Prevention (CDC) growth charts and percentile cutoffs: <5th percentile as underweight, 5th–85th percentile as normal weight, 85th–<95th percentile as overweight, and ≥95th percentile as obese. The authors used these categories to examine associations between BMI status and albuminuria.
Urinary microalbumin and spot urine creatinine were measured by immunoturbidimetry using a 2000 DCA plus analyzer. The albumin‑creatinine ratio (ACR) was calculated for each sample. Microalbuminuria was defined operationally when two consecutive spot urine samples tested positive for microalbumin. Macroalbuminuria status was also reported in the cohort.
Statistical analysis was performed using SPSS for Windows, version 20.0. Associations between categorical variables were evaluated using the Chi‑square test as described in the abstract. The authors reported p values for assessing statistical significance of BMI category associations with microalbuminuria.
Among the 512 participants, the distribution of diabetes types was 61.9% type 1 and 38.1% type 2. The prevalence of albuminuria in the study population was reported as follows: microalbuminuria in 88.7% and macroalbuminuria in 3.6% of participants. These prevalence figures are those presented in the abstract.
The analysis revealed that overweight and obese participants had higher rates of microalbuminuria compared with other BMI categories. This association between higher BMI (overweight/obesity) and microalbuminuria was statistically significant (p < 0.001) according to the authors. The abstract states that overweight and obesity were "high in microalbuminuria than other parameters of BMI," indicating a stronger representation of these BMI categories among those with microalbuminuria.
The authors conclude that early diagnosis and active screening of diabetic children and adolescents who are overweight or obese may allow regression of microalbuminuria and prevent further progression of renal involvement. The abstract emphasizes the importance of screening obese pediatric patients with diabetes for early evidence of albuminuria.
The abstract provides methods, measurements and major results but does not report several details in the source text: it does not provide age distribution, duration of diabetes, glycemic control metrics (for example HbA1c), exact ACR cutoff values used to define positivity, or the numeric breakdown of BMI categories across the cohort. It also does not report longitudinal follow‑up data or interventions demonstrating regression of microalbuminuria. Where such details are not reported in the abstract, they are not invented here.
Overall, this single‑center cross‑sectional study of 512 pediatric patients attending a tertiary diabetes clinic in Bangladesh documents a high reported prevalence of microalbuminuria and a significant association between overweight/obesity and microalbuminuria. The authors recommend early screening of obese children and adolescents with diabetes to detect and potentially reverse early renal involvement, although the abstract does not present follow‑up or interventional outcome data to support effectiveness of specific measures.