The hypothesis underlying the present study is that osteoporosis and its multimorbidity non-communicable diseases (NCDs) share genes, their encoded proteins and pathways that stratify patients in subgroups and their tendency to co-occur together. The overall goal of VOS is to map genetic and environmental factors that underlie the risk of fragility fracture and the co-occurrence of related chronic diseases (i.e., the so-called "diseasome") in the Vietnamese people. We pursue the following specific aims:

1. Assess the skeletal burden and its associated morbidities in the general population. We aim to estimate the prevalence of osteoporosis, osteoarthritis and multimorbidity, the incidence of fragility fractures and osteoarthritis in men and women by age group;
2. Determine the extent to which variation in osteoporosis, fracture and osteoarthritis susceptibility is determined by genetic factors. We also aim to identify genetic variants that are associated with osteoporosis, fracture and osteoarthritis;
3. Understand the interplay between genes and environmental factors in the determination of the association between osteoporosis and other chronic diseases, NCDs including osteoarthritis, sarcopenia, obesity, diabetes, metabolic syndrome, and cardiovascular disease.

This research is an ongoing collaboration with the Johns Hopkins University School of Medicine, USA.

We have conducted a case-control study to validate and develop prediction tools for early cancer diagnosis for common cancers in Vietnam.

The secondary aim is to establish a bioresource which can be used in collaboration with other clinical investigators or contributed to international consortia of epidemiological studies of cancer in Asia and internationally.

We also plan to collaborate with the Johns Hopkins University School of Medicine, USA conduct a clinical trial to examine the effect of cfDNA in detecting minimal residual disease (MRD) and indicating of adjuvant chemotherapy in colorectal and breast cancer. We hope the clinical application of cfDNA-based MRD detection can assist clinical decision-making and improve patient outcomes in malignant tumors.

Publications:

1. CHRISTOPHER DOUVILLE, KAMEL LAHOUEL, ALBERT KUO, HALEY GRANT, BRACHA ERLANGER AVIGDOR, SAMUEL D. CURTIS , MAHMOUD SUMMERS, JOSHUA D. COHEN, YUXUAN WANG, AUSTIN MATTOX, JONATHAN DUDLEY, LISA DOBBYN, MARIA POPOLI, JANINE PTAK, NADINE NEHME, NATALIE SILLIMAN, CHERIE BLAIR, KATHARINE ROMANS, CHRISTOPHER THOBURN, JENNIFER GIZZI, ROBERT E. SCHOEN, JEANNE TIE, PETER GIBBS, LAN T. HO-PHAM, BICH N. H. TRAN, THACH S. TRAN, TUAN V. NGUYEN, MICHAEL GOGGINS, CHRISTOPHER L. WOLFGANG, TIAN-LI WANG, IE-MING SHIH, ANNE MARIE LENNON, RALPH H. HRUBAN, CHETAN BETTEGOWDA, KENNETH W. KINZLER, NICKOLAS PAPADOPOULOS, BERT VOGELSTEIN, AND CRISTIAN TOMASETTI. Machine learning to detect the SINEs of cancer. Science Translational Medicine 16:731 2024 – DOI – PubMed
2. Wang Y, Douville C, Cohen JD, Mattox A, Curtis S, Silliman N, Popoli M, Ptak J, Dobbyn L, Nehme N, Dudley JC, Summers M, Zhang M, Lan T. Ho-Pham, Bich N H Tran, Thach S Tran, Tuan V Nguyen, Chetan Bettegowda, Nickolas Papadopoulos, Kenneth W Kinzler, Bert Vogelstein. Detection of rare mutations, copy number alterations, and methylation in the same template DNA molecules. Proceedings of the National Academy of Sciences (PNAS) 2023 – DOI – PubMed
3. More publications coming soon during 2025.

click to view more

Using AI for medical imaging analysis has grown significantly in recent years and offers advantages in interpreting data.

We plan to conduct a prospective study to validate and develop detection tools for breast cancer in Vietnam. Also to release the entire end-to-end platform for development, deployment and operation of breast cancer AI as open-source.

With the strength points: large sample size (>10,000 participants), combining demographic/clinical data, diversity of dataset (wide age and breast density groups), and different vendor equipments (Hologic and Siemen), the study can help to:

1. develop an AI CAD tools having its robustness against clinical heterogeneous populations for detection breast cancer and
2. develop reproducible experiments and deployments in clinical practice.