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AIMS: The study aimed to determine the dimensionality of the Spanish version of the PQ-16 among Colombian adolescent school students. METHODS: A validation study was designed with the participation of 334 Colombian adolescent students aged between 13 and 17 (M = 15.2, SD = 1.1); 171 (52.1%) were girls, and 163 (47.9%) were boys, 229 (68.6%) were ninth-grade students and 105 (31.4%) were tenth-grade students. Confirmatory factor analysis was performed, internal consistency was calculated with the Kuder-Richardson and McDonald's omega tests, and correlation with suicide ideation was computed with the Kendall correlation (r). RESULTS: The confirmatory factor analysis showed that the PQ-16 adequately fit a unidimensional structure: RMSEA = 0.05 (90%CI 0.04-0.06), CFI = 0.91, TLI = 0.90, SRMR = 0.05, chi-squared = 193.18 (df = 102, p < 0.001) and normalized chi-squared = 1.89. This factor presented high internal consistency: Kuder-Richardson test and McDonald's omega of 0.83. The correlation between the PQ-16 and suicide ideation was r = 0.45 (p < 0.001). CONCLUSION: The PQ-16 is a one-dimensional tool with high internal consistency and correlation with suicide ideation among schooled adolescents. Further research should explore the PQ-16 performance against a structured clinical interview.
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This article provides a comprehensive analysis of the feature extraction methods applied to vibro-acoustic signals (VA signals) in the context of robot-assisted interventions. The primary objective is to extract valuable information from these signals to understand tissue behaviour better and build upon prior research. This study is divided into three key stages: feature extraction using the Cepstrum Transform (CT), Mel-Frequency Cepstral Coefficients (MFCCs), and Fast Chirplet Transform (FCT); dimensionality reduction employing techniques such as Principal Component Analysis (PCA), t-Distributed Stochastic Neighbour Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP); and, finally, classification using a nearest neighbours classifier. The results demonstrate that using feature extraction techniques, especially the combination of CT and MFCC with dimensionality reduction algorithms, yields highly efficient outcomes. The classification metrics (Accuracy, Recall, and F1-score) approach 99%, and the clustering metric is 0.61. The performance of the CT-UMAP combination stands out in the evaluation metrics.
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Robótica , Algoritmos , Acústica , Análise por Conglomerados , Análise de Componente PrincipalRESUMO
Workers' job satisfaction benefits the organization, which constitutes a competitive advantage. This is why the Quality of Work Life (QoWL) study has gained relevance in recent years. For this reason, various scales have been developed to evaluate this organizational variable constantly. However, to date, there is no evidence in the scientific literature of a Spanish version that evaluates the validity and reliability of these scales in a Peruvian context. Thus, this study aimed to translate into Spanish and adapt and analyze the validity and reliability of a scale to assess the quality of work life in Peruvian teachers based on Walton's model. For this purpose, 457 regular basic education teachers from a private educational network located in the three regions of Peru were surveyed. The analyses used the Structural Equation Model (SEM) with the AMOS 24 statistical software. Confirmatory Factor Analysis provided an excellent fit model of eight factors and 31 elements (CMIN/DF = 2.351; CFI = 0.955; SRMR = 0.062; RMSEA = 0.054; Pclose = 0.052). It also demonstrated good internal consistency (α = between 0.806 and 0.938; CR = between 0.824 and 0.939; AVE = between 0.547 and 0.794). These results contribute to the study of QoWL in Peru.
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The use of sensors in different applications to improve the monitoring of a process and its variables is required as it enables information to be obtained directly from the process by ensuring its quality. This is now possible because of the advances in the fabrication of sensors and the development of equipment with a high processing capability. These elements enable the development of portable smart systems that can be used directly in the monitoring of the process and the testing of variables, which, in some cases, must evaluated by laboratory tests to ensure high-accuracy measurement results. One of these processes is taste recognition and, in general, the classification of liquids, where electronic tongues have presented some advantages compared with traditional monitoring because of the time reduction for the analysis, the possibility of online monitoring, and the use of strategies of artificial intelligence for the analysis of the data. However, although some methods and strategies have been developed, it is necessary to continue in the development of strategies that enable the results in the analysis of the data from electrochemical sensors to be improved. In this way, this paper explores the application of an electronic tongue system in the classification of liquor beverages, which was directly applied to an alcoholic beverage found in specific regions of Colombia. The system considers the use of eight commercial sensors and a data acquisition system with a machine-learning-based methodology developed for this aim. Results show the advantages of the system and its accuracy in the analysis and classification of this kind of alcoholic beverage.
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Nariz Eletrônico , Paladar , Inteligência Artificial , Bebidas , Bebidas Alcoólicas , LínguaRESUMO
Mental health problems are one of the various ills that afflict the world's population. Early diagnosis and medical care are public health problems addressed from various perspectives. Among the mental illnesses that most afflict the population is depression; its early diagnosis is vitally important, as it can trigger more severe illnesses, such as suicidal ideation. Due to the lack of homogeneity in current diagnostic tools, the community has focused on using AI tools for opportune diagnosis. Unfortunately, there is a lack of data that allows the use of IA tools for the Spanish language. Our work has a cross-lingual scheme to address this issue, allowing us to identify Spanish and English texts. The experiments demonstrated the methodology's effectiveness with an F1-score of 0.95. With this methodology, we propose a method to solve a classification problem for depression tweets (or short texts) by reusing English language databases with insufficient data to generate a classification model, such as in the Spanish language. We also validated the information obtained with public data to analyze the behavior of depression in Mexico during the COVID-19 pandemic. Our results show that the use of these methodologies can serve as support, not only in the diagnosis of depression, but also in the construction of different language databases that allow the creation of more efficient diagnostic tools.
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Since the last decade, carbon nanomaterials have had a notable impact on different fields such as bioimaging, drug delivery, artificial tissue engineering, and biosensors. This is due to their good compatibility toward a wide range of chemical to biological molecules, low toxicity, and tunable properties. Especially for biosensor technology, the characteristic features of each dimensionality of carbon-based materials may influence the performance and viability of their use. Surface area, porous network, hybridization, functionalization, synthesis route, the combination of dimensionalities, purity levels, and the mechanisms underlying carbon nanomaterial interactions influence their applications in bioanalytical chemistry. Efforts are being made to fully understand how nanomaterials can influence biological interactions, to develop commercially viable biosensors, and to gain knowledge on the biomolecular processes associated with carbon. Here, we present a comprehensive review highlighting the characteristic features of the dimensionality of carbon-based materials in biosensing.
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Técnicas Biossensoriais , Nanoestruturas , Carbono/química , Nanoestruturas/química , Sistemas de Liberação de Medicamentos , Técnicas Biossensoriais/métodosRESUMO
The clinical diagnosis of oculo-auriculo-vertebral spectrum (OAVS) is established when microtia is present in association with hemifacial hypoplasia (HH) and/or ocular, vertebral, and/or renal malformations. Genetic and non-genetic factors have been associated with microtia/OAVS. Although the etiology remains unknown in most patients, some cases may have an autosomal dominant, autosomal recessive, or multifactorial inheritance. Among the possible genetic factors, gene−gene interactions may play important roles in the etiology of complex diseases, but the literature lacks related reports in OAVS patients. Therefore, we performed a gene−variant interaction analysis within five microtia/OAVS candidate genes (HOXA2, TCOF1, SALL1, EYA1 and TBX1) in 49 unrelated OAVS Mexican patients (25 familial and 24 sporadic cases). A statistically significant intergenic interaction (p-value < 0.001) was identified between variants p.(Pro1099Arg) TCOF1 (rs1136103) and p.(Leu858=) SALL1 (rs1965024). This intergenic interaction may suggest that the products of these genes could participate in pathways related to craniofacial alterations, such as the retinoic acid (RA) pathway. The absence of clearly pathogenic variants in any of the analyzed genes does not support a monogenic etiology for microtia/OAVS involving these genes in our patients. Our findings could suggest that in addition to high-throughput genomic approaches, future gene−gene interaction analyses could contribute to improving our understanding of the etiology of microtia/OAVS.
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Earth observation offers an unprecedented opportunity to monitor intensively cultivated areas providing key support to assess fertilizer needs and crop water uptake. Routinely, vegetation traits mapping can help farmers to monitor plant development along the crop's phenological cycle, which is particularly relevant for irrigated agricultural areas. The high spatial and temporal resolution of the Sentinel-2 (S2) multispectral instrument leverages the possibility to estimate leaf area index (LAI), canopy chlorophyll content (CCC), and vegetation water content (VWC) from space. Therefore, our study presents a hybrid retrieval workflow combining a physically-based strategy with a machine learning regression algorithm, i.e., Gaussian processes regression, and an active learning technique to estimate LAI, CCC and VWC of irrigated winter wheat. The established hybrid models of the three traits were validated against in-situ data of a wheat campaign in the Bonaerense valley, South of the Buenos Aires Province, Argentina, in the year 2020. We obtained good to highly accurate validation results with LAI: R2 = 0.92, RMSE = 0.43 m2 m-2, CCC: R2 = 0.80, RMSE = 0.27 g m-2 and VWC: R2 = 0.75, RMSE = 416 g m-2. The retrieval models were also applied to a series of S2 images, producing time series along the seasonal cycle, which reflected the effects of fertilizer and irrigation on crop growth. The associated uncertainties along with the obtained maps underlined the robustness of the hybrid retrieval workflow. We conclude that processing S2 imagery with optimised hybrid models allows accurate space-based crop traits mapping over large irrigated areas and thus can support agricultural management decisions.
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Significance: The identification and manipulation of spatially identified neuronal ensembles with optical methods have been recently used to prove the causal link between neuronal ensemble activity and learned behaviors. However, the standardization of a conceptual framework to identify and manipulate neuronal ensembles from calcium imaging recordings is still lacking. Aim: We propose a conceptual framework for the identification and manipulation of neuronal ensembles using simultaneous calcium imaging and two-photon optogenetics in behaving mice. Approach: We review the computational approaches that have been used to identify and manipulate neuronal ensembles with single cell resolution during behavior in different brain regions using all-optical methods. Results: We proposed three steps as a conceptual framework that could be applied to calcium imaging recordings to identify and manipulate neuronal ensembles in behaving mice: (1) transformation of calcium transients into binary arrays; (2) identification of neuronal ensembles as similar population vectors; and (3) targeting of neuronal ensemble members that significantly impact behavioral performance. Conclusions: The use of simultaneous two-photon calcium imaging and two-photon optogenetics allowed for the experimental demonstration of the causal relation of population activity and learned behaviors. The standardization of analytical tools to identify and manipulate neuronal ensembles could accelerate interventional experiments aiming to reprogram the brain in normal and pathological conditions.
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Blood tests play an essential role in everyday medicine and are used by doctors in several diagnostic procedures. Moreover, this data is multivariate - and often some diseases, such as COVID-19, could have different symptom manifestations and outcomes. This study proposes a method of extracting useful information from blood tests using UMAP technique - Uniform Manifold Approximation and Projection for Dimension Reduction combined with DBSCAN clustering and statistical approaches. The analysis performed here indicates several clusters of infection prevalence varying between 2%-37%, showing that our procedure is indeed capable of finding different patterns. A possible explanation is that COVID-19 is not just a respiratory infection but a systemic disease with critical hematological implications, primarily on white-cell fractions, as indicated by relevant statistical test p -values in the range of 0.03-0.1. The novel analysis procedure proposed could be adopted in other data-sets of different illnesses to help researchers to discover new patterns of data that could be used in various diseases and contexts.
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Resumen: Antecedentes: El Cuestionario de Imagen Corporal (BSQ, por sus siglas en inglés) se ha utilizado ampliamente en población clínica y general destacando su carácter unidimensional para la medida de la insatisfacción corporal. Diversas investigaciones han generado hasta 10 versiones cortas basadas en reducciones del BSQ-34. Sin embargo, hasta el momento ninguna de ellas ha sido sometida a pruebas de confiabilidad y validez en muestras independientes. Objetivo: Analizar la estructura interna del BSQ-8D y su relación con los tres factores del Test de Actitudes Alimentarias (EAT-26, por sus siglas en inglés), así como la fiabilidad obtenida con un diseño de consistencia interna y otro de estabilidad temporal en una muestra de mujeres universitarias. Método: Participaron 492 mujeres universitarias quienes contestaron el BSQ-8D y el EAT-26. Resultados: El análisis factorial confirmatorio corroboró la estructura unidimensional del BSQ-8D. La consistencia interna fue adecuada, con α =.91 y ω = .89, así como la confiabilidad test-retest con un CCI = .80. La correlación entre las puntuaciones obtenidas de esta versión del BSQ y el EAT-26 fue de .56 y con sus factores fue .58 para Dieta, .33 para Bulimia y Preocupación por la Comida y .26 para Control Oral. Conclusiones: Estos hallazgos aportan evidencia empírica independiente que apoya la unidimensionalidad, la confiabilidad y la relación con las actitudes hacia la comida del BSQ-8D. Para fortalecer la solidez de esta versión del BSQ-8D hace falta recolectar datos en muestra clínica y en muestras de varones con y sin trastornos alimentarios y de la ingestión de alimentos.
Abstract: Background: The Body Shape Questionnaire (BSQ) has been widely used in the clinical and general population, highlighting its unidimensional nature. Research evidence has generated 10 short versions based on reductions of the BSQ-34. Hitherto, short versions have not been applied to analyze validity and reliability with independent samples. Aim: To analyze the internal structure of the BSQ-8D and its relationship with the three factors of the Eating Attitudes Test-26 (AET-26), as well as the reliability obtained with a design of internal consistency and another of temporal stability in a sample of university women. Methods: Participants were 492 undergraduate women who completed the BSQ-8D and the EAT-26. Results: Confirmatory factor analysis supported the one factor structure of the BSQ-8D. The internal consistency was adequate, α = .91 and ω = .89, as well as the test-retest reliability ICC = .80. Correlation between this BSQ-8D version and those obtained in the EAT-26 was = .56 besides the correlations with its factors .58 for Dieting, .33 for Bulimia and Food Concerns, and .26 for Oral Control. Conclusions: These findings added independent evidence about the unidimensionality of the instrument. To strengthen the robustness of this version of the BSQ-8D it is necessary to collect data in clinical and men sample with and without feeding and eating disorders.
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Humanos , Feminino , Adolescente , Adulto , Adulto JovemRESUMO
A common method to collect information in the behavioral and health sciences is the self-report. However, the validity of self-reports is frequently threatened by response biases, particularly those associated with inconsistent responses to positively and negatively worded items of the same dimension, known as wording effects. Modeling strategies based on confirmatory factor analysis have traditionally been used to account for this response bias, but they have recently become under scrutiny due to their incorrect assumption of population homogeneity, inability to recover uncontaminated person scores or preserve structural validities, and their inherent ambiguity. Recently, two constrained factor mixture analysis (FMA) models have been proposed by Arias et al. (2020) and Steinmann et al. (2021) that can be used to identify and screen inconsistent response profiles. While these methods have shown promise, tests of their performance have been limited and they have not been directly compared. Thus the objective of the current study was to assess and compare their performance with data from the Dominican Republic of the Rosenberg Self-Esteem Scale (N = 632). Additionally, as this scale had not yet been studied for this population, another objective was to show how using constrained FMAs could help in the validation of mixed-worded scales. The results indicated that removing the inconsistent respondents identified by both FMAs (≈8%) reduced the amount of wording effects in the database. However, whereas the Steinmann et al. method only cleaned the data partially, the Arias et al. (2020) method was able to remove the great majority of the wording effects variance. Based on the screened data with the Arias et al. method, we evaluated the psychometric properties of the RSES for the Dominican population, and the results indicated that the scores had good validity and reliability properties. Given these findings, we recommend that researchers incorporate constrained FMAs into their toolbox and consider using them to screen out inconsistent respondents to mixed-worded scales.
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Phonological awareness is one of the most important predictors of reading. However, there is still controversy concerning its dimensionality. This study evaluated the dimensionality of phonological awareness among Brazilian Portuguese-speaking children. A total of 212 children performed six phonological awareness tasks in the last year of kindergarten. Of those children, 177 performed the same tasks when they were in the first grade. The phonological awareness measures differed in both their cognitive demand (detection, blending, segmentation, and elision) and the phonological unit involved (rhyme, syllable, and phoneme). Confirmatory factor analyzes were employed to test several models of phonological awareness dimensionality. The results indicated that the best model was an oblique model of phonological units with two correlated latent factors: phonemic awareness and supraphonemic awareness. This model presented the best fit to the data both in kindergarten and in the first grade. In addition, supraphonemic awareness in the kindergarten predicted phoneme awareness in the first grade; however, phonemic awareness in the kindergarten did not predict supraphonemic awareness in the first grade. These results are compatible with phonological awareness developing from larger phonological units (e.g., syllables) to small phonological units (e.g., phonemes) and the reciprocal relationship between phonological awareness and reading. From a theoretical point of view, these results also suggest that phonological awareness is a one-dimensional construct that can be evaluated by tests employing different phonological units (e.g., syllables, rhymes, phonemes).
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This paper proposes an adaptation of the Fisher's discriminability criterion (named here as discriminant power, DP) for choosing principal components (obtained from Principal Component Analysis, PCA), which will be used to construct supervised Linear Discriminant Analysis (LDA) models for solving classification problems of food data. The proposed PCA-DP-LDA algorithm was then applied to (i) simulated data, (ii) classify soybean oils with respect to expiration date, and (iii) identify cachaça adulteration with wood extracts that simulated aging. For comparison, PCA-DP-LDA was evaluated against conventional PCA-LDA (based on explained variance) and Partial Least Squares-Discriminant Analysis (PLS-DA). Among them, PCA-DP-LDA achieved the most parsimonious and interpretable results, with similar or better classification performance. Therefore, the new algorithm can be considered a good alternative to the already well-established discriminant methods, being potentially applied where the discriminability of the principal components may not follow the same behavior of the explained variance.
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Algoritmos , Óleo de Soja , Análise Discriminante , Análise dos Mínimos Quadrados , Análise de Componente PrincipalRESUMO
Abstract Phonological awareness is one of the most important predictors of reading. However, there is still controversy concerning its dimensionality. This study evaluated the dimensionality of phonological awareness among Brazilian Portuguese-speaking children. A total of 212 children performed six phonological awareness tasks in the last year of kindergarten. Of those children, 177 performed the same tasks when they were in the first grade. The phonological awareness measures differed in both their cognitive demand (detection, blending, segmentation, and elision) and the phonological unit involved (rhyme, syllable, and phoneme). Confirmatory factor analyzes were employed to test several models of phonological awareness dimensionality. The results indicated that the best model was an oblique model of phonological units with two correlated latent factors: phonemic awareness and supraphonemic awareness. This model presented the best fit to the data both in kindergarten and in the first grade. In addition, supraphonemic awareness in the kindergarten predicted phoneme awareness in the first grade; however, phonemic awareness in the kindergarten did not predict supraphonemic awareness in the first grade. These results are compatible with phonological awareness developing from larger phonological units (e.g., syllables) to small phonological units (e.g., phonemes) and the reciprocal relationship between phonological awareness and reading. From a theoretical point of view, these results also suggest that phonological awareness is a one-dimensional construct that can be evaluated by tests employing different phonological units (e.g., syllables, rhymes, phonemes).
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ConscientizaçãoRESUMO
Analysis of population genetic variation and structure is a common practice for genome-wide studies, including association mapping, ecology, and evolution studies in several crop species. In this study, machine learning (ML) clustering methods, K-means (KM), and hierarchical clustering (HC), in combination with non-linear and linear dimensionality reduction techniques, deep autoencoder (DeepAE) and principal component analysis (PCA), were used to infer population structure and individual assignment of maize inbred lines, i.e., dent field corn (n = 97) and popcorn (n = 86). The results revealed that the HC method in combination with DeepAE-based data preprocessing (DeepAE-HC) was the most effective method to assign individuals to clusters (with 96% of correct individual assignments), whereas DeepAE-KM, PCA-HC, and PCA-KM were assigned correctly 92, 89, and 81% of the lines, respectively. These findings were consistent with both Silhouette Coefficient (SC) and Davies-Bouldin validation indexes. Notably, DeepAE-HC also had better accuracy than the Bayesian clustering method implemented in InStruct. The results of this study showed that deep learning (DL)-based dimensional reduction combined with ML clustering methods is a useful tool to determine genetically differentiated groups and to assign individuals into subpopulations in genome-wide studies without having to consider previous genetic assumptions.
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Vocalizations from birds are a fruitful source of information for the classification of species. However, currently used analyses are ineffective to determine the taxonomic status of some groups. To provide a clearer grouping of taxa for such bird species from the analysis of vocalizations, more sensitive techniques are required. In this study, we have evaluated the sensitivity of the Uniform Manifold Approximation and Projection (UMAP) technique for grouping the vocalizations of individuals of the Rough-legged Tyrannulet Phyllomyias burmeisteri complex. Although the existence of two taxonomic groups has been suggested by some studies, the species has presented taxonomic difficulties in classification in previous studies. UMAP exhibited a clearer separation of groups than previously used dimensionality-reduction techniques (i.e., principal component analysis), as it was able to effectively identify the two taxa groups. The results achieved with UMAP in this study suggest that the technique can be useful in the analysis of species with complex in taxonomy through vocalizations data as a complementary tool including behavioral traits such as acoustic communication.
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Articular cartilage is an avascular tissue with a structure that allows it to support and cushion the overload of the surfaces in contact. It maintains its metabolic functions due to the contribution of different signaling pathways. However, several factors play a role in its deterioration, allowing to the development of osteoarthritis (OA), and one of the major factors is genetic. Our goal was to identify gene-gene interactions (epistasis) between five signaling pathways involved in the articular cartilage metabolism as possible indicators of OA risk. We applied the Multifactor-Dimensionality Reduction (MDR) method to identify and characterize the epistasis between 115 SNPs located in 73 genes related to HIF-1α, Wnt/ß-catenin, cartilage extracellular matrix metabolism, oxidative stress, and uric acid transporters. Ninety three patients diagnosed with primary knee OA and 150 healthy controls were included in the study. Genotyping was performed with the OpenArray system, the statistical analysis was carried out with the STATA software v14, and epistasis was analyzed with the MDR software v3.0.2. The MDR analysis revealed that the best interaction model was between polymorphisms rs17786744 of the STC1 gene and rs2615977 of the COL11A1 gene, with an entropy value of 4.44%, CVC 8/10, OR 5.60, 95% CI 3.27-9.59, p < 0.0001. Under this interaction model, we identified high and low risk genotypes involved in OA development. Our results suggest complex interactions between STC1 and COL11A1 genes that might have an impact on genetic susceptibility to develop OA. Further studies are required to confirm it.
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Colágeno Tipo XI/genética , Glicoproteínas/genética , Osteoartrite do Joelho/genética , Adulto , Alelos , Estudos de Casos e Controles , Epistasia Genética/genética , Feminino , Frequência do Gene , Predisposição Genética para Doença/genética , Genótipo , Humanos , Masculino , Pessoa de Meia-Idade , Redução Dimensional com Múltiplos Fatores/métodos , Osteoartrite/genética , Polimorfismo de Nucleotídeo Único/genética , Fatores de Risco , SoftwareRESUMO
Landslide susceptibility maps can be developed with artificial neural networks (ANNs). In order to train our ANNs, a digital elevation model (DEM) and a scar map of one previous event were used. Eleven attributes are generated, possibly containing redundant information. Our base model is formed by, essentially, one input (the DEM), eleven attributes, 30 neurons, and one output (susceptibility). Principal components (PCs) group information in the first projected variables, the last ones can be expendable. In the present paper, four groups of models were trained: one with eleven attributes generated from the DEM; one with 8 out of 11 attributes, in which 3 were eliminated by their high correlation with others; other, with the data projected over its PCs; and another, using 8 out of 11 PCs. The used number of neurons in hidden layer is 30, calibrated based on a complexity analysis that is an in-house developed method. The ANN models trained with the original data generated better statistical results than their counterparts trained with the PC transformed input. Keeping the original 11 attributes calculated provided the best metrics among all models, showing that eliminating attributes also eliminates information used by the model. Using 11 PC transformed attributes hindered trained. However, for the model with eight PCs, training was much faster than its counterpart with little accuracy loss. The metrics and maps achieved were considered acceptable, conveying the power of our model based on ANNs, which uses essentially one input (the DEM) for mapping areas susceptible to mass movements.
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Deslizamentos de Terra , Redes Neurais de Computação , Algoritmos , Monitoramento Ambiental , RiscoRESUMO
INTRODUCTION/OBJECTIVES: Articular cartilage is the target tissue of osteoarthritis (OA), and because it lacks capillary networks, the microenvironment is hypoxic. Hypoxia inducible factor-1 alpha (HIF-1α) regulates the homeostasis of this tissue. The aim of this study was to investigate whether genetic polymorphisms of the HIF-1α signaling pathway are involved in the development of knee OA. METHOD: We performed a case-control association study and genotyped 134 knee OA patients and 267 healthy controls. All participants were genotyped in order to evaluate 42 SNPs from 22 genes involved in the HIF-1α signaling pathway using the OpenArray technology. Gene-gene interactions (epistasis) were analyzed using the multifactor dimensionality reduction (MDR) method. RESULTS: The MDR analysis showed epistasis between AKT2 (rs8100018) and IGF1 (rs2288377), AKT2 (rs8100018) and IGF1 (rs35767), IGF1 (rs35767) and COL2A1 (rs1793953), and between GSK3B (rs6438552) and IGF1 (rs35767) polymorphisms, with information gain values of 21.24%, 8.37%, 9.93%, and 5.73%, respectively. Additionally, our model allowed us to identify high- and low-risk genotypes among COL2A1 rs1793953, GSK3B rs6438552, AKT2 rs8100018, and IGF1 rs35767 polymorphisms. CONCLUSIONS: Knowing the interactions of these polymorphisms involved in HIF-1α signaling pathway could provide a new diagnostic support tool to identify individuals at high risk of developing knee OA.