Single nucleotide polymorphism genes and mitochondrial DNA haplogroups as biomarkers for early prediction of knee osteoarthritis structural progressors: use of supervised machine learning classifiers

Abstract

Background Knee osteoarthritis is the most prevalent chronic musculoskeletal debilitating disease. Current treatments are only symptomatic, and to improve this, we need a robust prediction model to stratify patients at an early stage according to the risk of joint structure disease progression. Some genetic factors, including single nucleotide polymorphism (SNP) genes and mitochondrial (mt)DNA haplogroups/clusters, have been linked to this disease. For the first time, we aim to determine, by using machine learning, whether some SNP genes and mtDNA haplogroups/clusters alone or combined could predict early knee osteoarthritis structural progressors. Methods Participants (901) were first classified for the probability of being structural progressors. Genotyping included SNP genes TP63, FTO, GNL3, DUS4L, GDF5, SUPT3H, MCF2L, and TGFA; mtDNA haplogroups H, J, T, Uk, and others; and clusters HV, TJ, KU, and C-others. They were considered for prediction with major risk factors of osteoarthritis, namely, age and body mass index (BMI). Seven supervised machine learning methodologies were evaluated. The support vector machine was used to generate gender-based models. The best input combination was assessed using sensitivity and synergy analyses. Validation was performed using tenfold cross-validation and an external cohort (TASOAC). Results From 277 models, two were defined. Both used age and BMI in addition for the first one of the SNP genes TP63, DUS4L, GDF5, and FTO with an accuracy of 85.0%; the second profits from the association of mtDNA haplogroups and SNP genes FTO and SUPT3H with 82.5% accuracy. The highest impact was associated with the haplogroup H, the presence of CT alleles for rs8044769 at FTO, and the absence of AA for rs10948172 at SUPT3H. Validation accuracy with the cross-validation (about 95%) and the external cohort (90.5%, 85.7%, respectively) was excellent for both models. Conclusions This study introduces a novel source of decision support in precision medicine in which, for the first time, two models were developed consisting of (i) age, BMI, TP63, DUS4L, GDF5, and FTO and (ii) the optimum one as it has one less variable: age, BMI, mtDNA haplogroup, FTO, and SUPT3H. Such a framework is translational and would benefit patients at risk of structural progressive knee osteoarthritis.

Bibliometric insights from publications on subchondral bone research in osteoarthritis

“…His team has been working on a series of studies on the pathogenesis, risk factors, biomarkers, and treatment of OA. In a recent study, they identified single nucleotide polymorphism genes and mitochondrial DNA haplogroups as biomarkers for early prediction of the progression of knee OA through machine learning ( Bonakdari et al, 2022 ; Martel-Pelletier and Pelletier, 2022 ). Highly cited literature can be considered the most valuable and influential research in the field, so new researchers can read these papers before further research ( Table 5 ; Figure 4 ).…”

Magnetic resonance imaging assessments for knee segmentation and their use in combination with machine/deep learning as predictors of early osteoarthritis diagnosis and prognosis

“…A recent study by Bonakdari et al . 222 evaluates whether eight single nucleotide polymorphism genes ( TP63 , FTO , GNL3 , DUS4L , GDF5 , SUPT3H , MC2FL , and TGFA ) and mitochondrial DNA haplogroups (H, J, T, Uk, and others) and clusters (HV, TJ, KU, and C-others), in addition to two risk factors (age and body mass index), could predict early knee OA structural progressors. The Jamshidi et al .…”

Emerging roles ofRNAbinding proteins in intervertebral disc degeneration and osteoarthritis

“…It has been implicated in multiple bioprocesses, including cell proliferation, differentiation, and cell cycle regulation. [116][117][118] GNL3 belongs to the GTP-binding protein family, containing an MMRHSR1 domain. 119 GNL3 is abundantly expressed in bone marrow mesenchymal stem cells (BM-MSCs) and is closely related to the differentiation of chondrocytes.…”

“…119 GNL3 is abundantly expressed in bone marrow mesenchymal stem cells (BM-MSCs) and is closely related to the differentiation of chondrocytes. [116][117][118] Recently, both human and animal studies found that GNL3 were significantly reduced in knee tissues of OA samples compared with non-OA samples. However, the potential mechanism underlying aberrant GNL3 expression in OA remains unclear.…”

“…GTP‐binding protein 3 (GNL3), known as nucleostemin, also plays an important role in PTGR involved in different stages of RNA metabolism. It has been implicated in multiple bioprocesses, including cell proliferation, differentiation, and cell cycle regulation 116–118 . GNL3 belongs to the GTP‐binding protein family, containing an MMRHSR1 domain 119 .…”