Study design, setting and population
This was a cross-sectional study that took place from February to May 2023 at the diabetic unit and the consultation unit of the haemodialysis centre of the Bamenda Regional Hospital, a tertiary Hospital in Bamenda, Cameroon. T2DM patients aged 60 years and above attending the out-patient department mentioned above for their routine visit were consecutively recruited during their routine diabetes clinic consultations. We included a total of 146 T2DM patients in this study.
Data collection
Sociodemographic (age, gender, education levels, occupation, marital status) and clinical (heart failure, hepatocellular insufficiency, smoking, alcohol, coronary artery disease, stroke, neoplasia) characteristics of participants were collected during a face-to-face interview using a pre-tested data collection form. After the interview, physical parameters measurement (weight, height, blood pressure, mid upper arm circumference and calf circumference) were also performed for each participant of the study.
Sample collection and biological assays
Blood sample (5 ml) were collected by venepuncture in a plain tube. The tube was centrifuge at 3000 RPM for 10 minutes and the sera obtained were used for albumin assay using Randox Compact Chemistry Analyser (RX Monaco plus).
Assessment of nutritional status
We used “The Mini Nutritional Assessment Short Form (MNA-SF)” and “theGlobal Leadership Initiative on Malnutrition Diagnostic (GLIM)” criteria for the assessment of nutritional status of the participant of this study.
The Mini Nutritional Assessment Short Form
The Mini Nutritional Assessment-Short form (MNA-SF) is an initial screening test for nutritional risk, and is consider as a gold standard for detection of undernutrition in geriatric. It is one of the questionnaires recommended as a screening tool for malnutrition risk by the GLIM experts. The form contains six questions regarding decrease in food intake (1), weight loss (2), mobility (3), psychological distress or acute disease (4), neuropsychological problems (5), and body mass index (6). Using this criterion, a maximum of expected score is 14. a score of 12 or more indicate normal nutritional status, a score between 8 and 11 define patients at risk of undernutrition while a score below 8 indicate undernourished patients.
the Global Leadership Initiative on Malnutrition Diagnostic
The GLIM criteria were developed in 2019 by the European Society for Clinical Nutrition and Metabolism (ESPEN), the American Society for Parenteral and Enteral Nutrition (ASPEN) and the Federation of Parenteral and Enteral Nutrition Society of Asia (PENSA). It is consisted of Three phenotypic criteria and two etiologic criteria.
Phenotypic criteria:
(1) Weight loss was acknowledged in persons >5% within past 6 months, or >10% beyond 6 months.
(2) Low body mass index: <20 kg/m2 if the patient is < 70 years or <22 kg/m2 if < 70 years.
(3) Low muscle mass (LMM). Muscle mass was assessed based on the mid upper arm circumference. Mid upper arm circumference below 23 cm were considered as low muscle mass and those between 23 cm and above were considered as normal.
Etiologic criteria
(1) Reduced food intake was acknowledged in persons who declared at least moderate decrease in the number of meals or amount of food in the past three months;
(2) Disease burden/inflammatory condition was acknowledged if at least one chronic disease associated with chronic or periodic inflammation (e.g., chronic obstructive pulmonary disease, chronic heart failure, or chronic kidney disease) or elevated C-reactive protein levels (>10 mg/L) were identified in the subjects’ medical records.
Any participant with at least one phenotypic and one etiologic criterion was considered to be undernourished.
Ethical considerations
Ethical clearance was obtained from the Institutional Review Board of the Faculty of Health Sciences, University of Bamenda, Cameroon (2023/0798H/UBa/IRB). Before enrollment in the study, each participant provided a signed written informed consent.
Data processing and analysis
Data were analyzed using the Statistical Package for Social Science (SPSS) version 26.0 (SPSS lnc, Chicago, IL, USA). The qualitative variables were expressed as numbers and percentages. Quantitative variables were expressed as mean with standard deviation or median with inter quartile range depending on their distribution. The comparison between qualitative variables was done using Chi-square test. Odds ratios were used to measure the association between qualitative variables. Data were considered statistically significant for a p-value < 0.05 with a 95% confidence interval.