BMF CP120: Demographic and Lifestyle Risk Factors in the Development of Diabetes Mellitus and Tuberculosis
Ni Putu Wulan Purnama Sari
Faculty of Nursing, Widya Mandala Surabaya Catholic University, East Java, Indonesia
January 15, 2026
“To ensure the health of the prophet of the Bird Village, the disciples scrambled to make preparations: nutritious vegetables, mashed cornmeal, soft rice, herbs, etc.”
—In “No-Fish Dietary”, Wild Wise Weird (2024)
1. Project description
1.1. Main objectives
The current study is conducted to examine the following research questions:
- How are the demographic, i.e., age and sex, and lifestyle risk factors, i.e., smoking, alcohol consumption, and nutritional status, associated with Diabetes Mellitus (DM)?
- How are the demographic, i.e., age and sex, and lifestyle factors, i.e., smoking, alcohol consumption, and nutritional status, associated with Tuberculosis (TB)?
- How does DM moderate the relationship between risk factors, e.g., age, sex, smoking, alcohol consumption, and nutritional status, and TB?
1.2. Materials
The Granular Interaction Thinking Theory (GITT) will be employed for the conceptual development of this study, while the Bayesian Mindsponge Framework (BMF) analytics will be utilized for statistical analysis [1,2]. The study will follow the principles, philosophy, and ethical standards outlined in [3]. The dataset comprises responses from 73 multidrug-resistant TB patients and 219 TB patients in West Sumatra, Indonesia [4]. Statistical analyses will be conducted using the bayesvl R package, which utilizes the Markov chain Monte Carlo (MCMC) algorithm for estimation [5].
1.3. Main findings
Results of preliminary analysis showed that sex and smoking are negatively associated with DM, while age exhibits a positive association. In addition, alcohol consumption and nutritional status exhibit positive associations with DM but with moderate reliability, compared to age. In terms of TB, nutritional status exhibits a negative association. Sex, age, and alcohol consumption are positively associated with TB. The role of smoking in TB development remains unclear, indicating a need for further investigation.
The main finding of this study is that DM positively moderates the relationship between risk factors, e.g., age, sex, smoking, alcohol consumption, and nutritional status, and TB (see Figure 1). This finding suggests that while risk factors and DM alone do not contribute to TB development, when risk factors interact with DM, then the development of TB is supported. Reduced immunity might play a major role in TB incidence among diabetic patients.
Figure 1: The estimated posterior distributions with HPDI at 89%
2. Collaboration procedure
Portal users should follow these steps to register to participate in this research project:
- Create an account on the website (preferably using an institution email).
- Comment your name, affiliation, and your desired role in the project below this post.
- Patiently wait for the formal agreement on the project from the AISDL mentor.
If you have further inquiries, please contact us at aisdl_team@mindsponge.info
If you have been invited to join the project by an AISDL member, you are still encouraged to follow the above formal steps.
All the resources for conducting and writing the research manuscript will be distributed upon project participation.
Mentor for this project: Ni Putu Wulan Purnama Sari.
Other members who have joined this project: Minh-Hoang Nguyen and Quan-Hoang Vuong.
The research project strictly adheres to scientific integrity standards, including authorship rights and obligations, without incurring an economic burden at participants’ expenses.
References
[1] Nguyen MH, Sari NPWP, Duong MPT, et al. (2025) What makes readers love a fiction book: A statistical analysis on Wild Wise Weird using real-world data from Amazon readers’ reviews. Sage Open, 15(4), 21582440251407823. https://doi.org/10.1177/21582440251407823
[1] Vuong QH, Nguyen MH, La VP (2022) The mindsponge and BMF analytics for innovative thinking in social sciences and humanities. Walter de Gruyter GmbH. https://books.google.com/books?id=EGeEEAAAQBAJ
[3] Vuong QH, Nguyen MH (2025). Developing Bayesian probabilistic reasoning capacity in HSS disciplines: Qualitative evaluation on bayesvl and BMF analytics for ECRs. arXiv preprint arXiv:2601.06038. https://doi.org/10.48550/arXiv.2601.06038
[4] Nindrea RD, Sari NP, Harahap WA, et al. (2020) Survey data of multidrug-resistant tuberculosis, Tuberculosis patients characteristics and stress resilience during COVID-19 pandemic in West Sumatera Province, Indonesia. Data in Brief, 32, 106293. https://doi.org/10.1016/j.dib.2020.106293
[5] Vuong QH, La VP (2019) bayesvl package for Bayesian statistical analyses in R. http://books.google.com/books/about?id=uq2lEQAAQBAJ