Database Quiroga et al

This database comprises detailed anthropometric, clinical, and bioimpedance-derived body composition data collected from adult individuals with type 2 diabetes mellitus. It includes a total of 89 variables organized across multiple domains: 1. Demographic and Sociocultural Data ID: Unique identifier for each participant. Height: Body height in centimeters. Age: Age in years. Area: Living environment (urban or rural). Ethnicity: Self-reported ethnic background (e.g., mestizo, indigenous). 2. Clinical Parameters Systolic and Diastolic Blood Pressure (mmHg) Oxygen Saturation (%) Heart Rate (bpm) Waist, Arm, and Calf Circumference (cm) Medical history: Family and personal history of diseases such as type 2 diabetes, hypertension, dyslipidemia, cancer, osteoporosis, thyroid disorders, cardiovascular, cerebrovascular, renal, and pulmonary conditions. Diagnosis Duration: Years since diabetes diagnosis. Treatment Type: Includes categories such as "only tablets", "insulin", "diet and exercise", "mixed", and "insulin therapy". Lifestyle Habits: Smoking, alcohol and drug use. Sleep hygiene (categorized by hours of sleep). Urinary habits and bowel movements (e.g., constipation, diarrhea). 3. Bioimpedance Body Composition Data (InBody S10) Total and Segmental Body Water (intracellular and extracellular) Fat Mass, Lean Mass, Fat-Free Mass (kg and %) Skeletal Muscle Mass Protein and Mineral Content Visceral Fat Area (cm²) Body Cell Mass (BCM) Arm Muscle Circumference (cm) Bone Mineral Content (kg) Segmental Phase Angles for arms, trunk, and legs General Phase Angle: As an indicator of cellular health and membrane integrity. ECW/TBW Ratios: Both total and segment-specific, indicating fluid distribution. This structured dataset is designed for multivariable analysis of the relationship between body composition indices and diabetes-related complications, such as neuropathy, retinopathy, nephropathy, and cardiovascular risk. It is suitable for clinical research, statistical modeling, and machine learning applications in metabolic health and risk stratification.