Back

Medicinal Potential of Milk: A Meta-Analysis of Bioactive Compounds, Health Benefits, Consumption Patterns, and Policy Implications for Tanzania and Beyond

Sonola, V. S.

2025-04-16 nutrition
10.1101/2025.04.14.25325749 medRxiv
Show abstract

BackgroundMilk from various animal species is increasingly recognized not only as a nutritional food but also as a functional therapeutic resource due to its rich bioactive compounds. However, disparities exist globally regarding awareness, consumption patterns, and industrial utilization of medicinal milk, particularly in Tanzania and Sub-Saharan Africa. ObjectiveThis meta-analysis synthesizes current literature on the medicinal potential of milk, focusing on the types of bioactive compounds, their therapeutic applications, consumption trends, regional awareness, and policy strategies that offer practical lessons for Tanzania. MethodsA systematic search was conducted across the PubMed, Scopus, Google Scholar, and ScienceDirect databases, targeting studies published between 2010 and 2024. Data on bioactive compounds, therapeutic efficacy, consumption prevalence, and regional practices were extracted and synthesized. A comparative analysis was performed for Tanzania, East Africa, Sub-Saharan Africa, and global regions. ResultsLactoferrin, immunoglobulins, insulin-like proteins, bioactive peptides, and probiotics are the most studied compounds in cow, goat, camel, sheep, and buffalo milk. Camel milk has demonstrated significant glycemic control effects, with fasting blood glucose reduction ranging from 9% to 18% in diabetic patients. Awareness of milks medicinal potential remains low in Tanzania ([~]10%) compared to Kenya (30%), Ethiopia (40%), and Europe (>70%). The industrial extraction of bioactive (e.g., lactoferrin) is limited in Sub-Saharan Africa but well-established in Europe, Asia, and Oceania. ConclusionTanzania presents significant untapped potential for integrating medicinal milk into its public health and dairy development strategies. Urgent multi-sectoral efforts focusing on awareness campaigns, research investment, dairy innovation, and policy reforms are essential.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.