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BACKGROUND: The impact of dynamic changes in the degree of atherosclerosis on the development of prediabetes remains unclear. This study aims to investigate the association between cumulative atherogenic index of plasma (CumAIP) exposure during follow-up and the development of prediabetes in middle-aged and elderly individuals. METHODS: A total of 2,939 prediabetic participants from the first wave of the China Health and Retirement Longitudinal Study (CHARLS) were included. The outcomes for these patients, including progression to diabetes and regression to normal fasting glucose (NFG), were determined using data from the third wave. CumAIP was calculated as the ratio of the average AIP values measured during the first and third waves to the total exposure duration. The association between CumAIP and the development of prediabetes was analyzed using multivariable Cox regression and restricted cubic spline (RCS) regression. RESULTS: During a median follow-up period of 3 years, 15.21% of prediabetic patients progressed to diabetes, and 22.12% regressed to NFG. Among the groups categorized by CumAIP quartiles, the proportion of prediabetes progressing to diabetes gradually increased (Q1: 10.61%, Q2: 13.62%, Q3: 15.65%, Q4: 20.95%), while the proportion regressing to NFG gradually decreased (Q1: 23.54%, Q2: 23.71%, Q3: 22.18%, Q4: 19.05%). Multivariable-adjusted Cox regression showed a significant positive linear correlation between high CumAIP exposure and prediabetes progression, and a significant negative linear correlation with prediabetes regression. Furthermore, in a stratified analysis, it was found that compared to married individuals, those who were unmarried (including separated, divorced, widowed, or never married) had a relatively higher risk of CumAIP-related diabetes. CONCLUSION: CumAIP is closely associated with the development of prediabetes. High CumAIP exposure not only increases the risk of prediabetes progression but also hinders its regression within a certain range. These findings suggest that monitoring and maintaining appropriate AIP levels may help prevent the deterioration of blood glucose levels.
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Aterosclerose , Biomarcadores , Glicemia , Progressão da Doença , Estado Pré-Diabético , Humanos , Estado Pré-Diabético/sangue , Estado Pré-Diabético/diagnóstico , Estado Pré-Diabético/epidemiologia , Pessoa de Meia-Idade , Masculino , Feminino , China/epidemiologia , Idoso , Fatores de Risco , Glicemia/metabolismo , Medição de Risco , Biomarcadores/sangue , Fatores de Tempo , Estudos Longitudinais , Aterosclerose/sangue , Aterosclerose/epidemiologia , Aterosclerose/diagnóstico , Fatores Etários , PrognósticoRESUMO
BACKGROUND: The Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE) Index, serves as an effective tool for evaluating body fat (BF) levels. This research seeks to clarify the association between the CUN-BAE Index and metabolic dysfunction-associated steatotic liver disease (MASLD) from a gender perspective. METHODS: The study utilized data from a comprehensive health assessment initiative known as "Human Dock", involving 14,251 participants. MASLD was diagnosed using abdominal ultrasound, primarily evaluated based on the following sonographic features: hepatorenal echo contrast, vascular blurring, deep attenuation, liver brightness. First, we evaluated the association of MASLD with the CUN-BAE Index using multivariate logistic regression. Second, we visualized this association and estimated potential threshold effect points using the restricted cubic spline (RCS) regression model. Ultimately, we evaluated the ability of the CUN-BAE Index to detect MASLD through receiver operating characteristic (ROC) curves. RESULTS: The female-to-male ratio was 1:1.08, with a MASLD prevalence rate of 17.59%. Following the adjustment for confounding variables, an increase of one unit in the CUN-BAE Index corresponded to a 14% increase in the risk of MASLD for males and an 18% increase for females. RCS analysis revealed an S-shaped relationship between MASLD prevalence and the CUN-BAE Index for both genders, with potential threshold effect points at approximately 30 in females and 15 in males. Beyond these threshold points, the prevalence of MASLD increased rapidly. Further subgroup analyses indicated significant differences in the relationship of the CUN-BAE Index with MASLD within age and body mass index (BMI) subgroups in females, with a stronger association observed in younger and non-obese female participants. Additionally, ROC analysis revealed that the CUN-BAE Index possesses a strong ability to distinguish MASLD in both genders, especially in females. CONCLUSIONS: This research is the first to identify a positive relationship between the CUN-BAE Index and MASLD. The CUN-BAE Index appears to be more suitable for early screening of MASLD in females.
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Adiposidade , Fígado Gorduroso , Curva ROC , Humanos , Masculino , Feminino , Pessoa de Meia-Idade , Adulto , Fígado Gorduroso/diagnóstico por imagem , Fígado Gorduroso/diagnóstico , Índice de Massa Corporal , Ultrassonografia/métodos , Fatores Sexuais , IdosoRESUMO
Background: The aim of this study was to investigate the relationship between triglyceride-glucose (TyG) index and cardiovascular disease (CVD) and all-cause mortality in adults with metabolic syndrome (MeS) and explore the mediating role of oxidative stress. Methods: This study included 6131 adults with MeS from the National Health and Nutrition Examination Survey (NHANES). The relationships between TyG index and mortality were elucidated using multivariate Cox proportional hazards models, restricted cubic splines (RCS) Fine-Gray competing risk model. In addition, mediation analysis was used to test the indirect effect of oxidative stress indicators. Results: Over a median 106-month follow-up, a total of 357 CVD and 1292 all-cause deaths were recorded. After multivariate adjustment, there was a J-type relationship between TyG index and CVD and all-cause mortality, with optimal inflection point of 9.13 and 8.92. After the threshold point, TyG index was positively associated with CVD (HR: 4.21, 95%CI: 1.82, 9.78) and all-cause mortality(HR: 2.93, 95%CI: 2.05, 4.18). Even using non-cardiovascular mortality as a competitive risk, the Fine-Gray model also illustrated that the cumulative CVD mortality incidence was higher in MeS with TyG index >9.13 (Fine-Gray P< 0.01). Mediation analysis revealed that biomarkers of oxidative stress, including gamma-glutamyl transferase and uric acid, collectively mediated 10.53% of the association between the TyG index and CVD mortality, and 8.44% of the association with all-cause mortality (P < 0.05). Conclusion: In the cohort study, TyG index was found to have a J-shaped association with CVD mortality and all-cause mortality in MeS population and oxidative stress may play a key mediating role in this relationship.
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Glicemia , Doenças Cardiovasculares , Síndrome Metabólica , Estresse Oxidativo , Triglicerídeos , Humanos , Síndrome Metabólica/sangue , Síndrome Metabólica/mortalidade , Síndrome Metabólica/epidemiologia , Feminino , Masculino , Doenças Cardiovasculares/mortalidade , Doenças Cardiovasculares/sangue , Pessoa de Meia-Idade , Triglicerídeos/sangue , Estudos Prospectivos , Glicemia/análise , Glicemia/metabolismo , Adulto , Inquéritos Nutricionais , Fatores de Risco , Idoso , Causas de Morte , Biomarcadores/sangue , Seguimentos , Estudos de CoortesRESUMO
Objective: Diabetes is a significant risk factor for acute heart failure, associated with an increased risk of mortality. This study aims to analyze the prognostic significance of admission blood glucose (ABG) on 30-day mortality in Chinese patients with acute decompensated heart failure (ADHF), with or without diabetes. Methods: This retrospective study included 1,462 participants from the JX-ADHF1 cohort established between January 2019 to December 2022. We conducted multivariate cox regression, restricted cubic spline, receiver operating characteristic curve analysis, and mediation analysis to explore the association and potential mechanistic pathways (inflammation, oxidative stress, and nutrition) between ABG and 30-day mortality in ADHF patients, with and without diabetes. Results: During the 30-day follow-up, we recorded 20 (5.36%) deaths in diabetic subjects and 33 (3.03%) in non-diabetics. Multivariate Cox regression revealed that ABG was independently associated with 30-day mortality in ADHF patients, with a stronger association in diabetics than non-diabetics (hazard ratio: Model 1: 1.71 vs 1.16; Model 2: 1.26 vs 1.19; Model 3: 1.65 vs 1.37; Model 4: 1.76 vs 1.33). Further restricted cubic spline analysis indicated a U-shaped relationship between ABG and 30-day mortality in non-diabetic ADHF patients (P for non-linearity < 0.001), with the lowest risk at ABG levels approximately between 5-7 mmol/L. Additionally, receiver operating characteristic analysis demonstrated that ABG had a higher predictive accuracy for 30-day mortality in diabetics (area under curve = 0.8751), with an optimal threshold of 13.95mmol/L. Finally, mediation analysis indicated a significant role of inflammation in ABG-related 30-day mortality in ADHF, accounting for 11.15% and 8.77% of the effect in diabetics and non-diabetics, respectively (P-value of proportion mediate < 0.05). Conclusion: Our study confirms that ABG is a vital indicator for assessing and predicting 30-day mortality risk in ADHF patients with diabetes. For ADHF patients, both with and without diabetes, our evidence suggests that physicians should be alert and closely monitor any changes in patient conditions when ABG exceeds 13.95 mmol/L for those with diabetes and 7.05 mmol/L for those without. Timely adjustments in therapeutic strategies, including endocrine and anti-inflammatory treatments, are advisable.
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Glicemia , Diabetes Mellitus , Insuficiência Cardíaca , Humanos , Insuficiência Cardíaca/mortalidade , Insuficiência Cardíaca/sangue , Feminino , Masculino , Prognóstico , Estudos Retrospectivos , Glicemia/análise , Idoso , Pessoa de Meia-Idade , Diabetes Mellitus/mortalidade , Diabetes Mellitus/sangue , Fatores de Risco , Doença Aguda , China/epidemiologia , Seguimentos , Admissão do PacienteRESUMO
Objective: Nutritional status is closely associated with the prognosis of heart failure. This study aims to assess the relationship between the Controlling Nutritional Status (CONUT) score and in-hospital mortality among patients with acute decompensated heart failure (ADHF) in Jiangxi, China. Methods: A retrospective cohort study was conducted. Multivariable Cox regression models and restricted cubic spline regression were employed to evaluate the relationship between the CONUT score and in-hospital mortality in ADHF patients from Jiangxi, China. The predictive value of the CONUT score for in-hospital mortality in ADHF patients was analyzed using receiver operating characteristic curves. Subgroup analyses were performed to identify risk dependencies of the CONUT score in specific populations. Results: The study included 1,230 ADHF patients, among whom 44 (3.58%) mortality events were recorded. After adjusting for confounding factors, a positive correlation was found between the CONUT score and the risk of in-hospital mortality in ADHF patients. Restricted cubic spline regression analysis indicated a non-linear relationship between the CONUT score and the risk of in-hospital mortality in ADHF patients, estimating a rapid increase in mortality risk when the CONUT score exceeded 5. Receiver operating characteristic analysis demonstrated a good predictive value of the CONUT score for all-cause mortality events in ADHF patients [area under the curve = 0.7625, optimal threshold = 5.5]. Additionally, a relatively higher risk associated with the CONUT score was observed in male patients and those with concomitant cerebral infarction. Conclusion: This study reveals a positive correlation between the CONUT score and the risk of in-hospital mortality in ADHF patients. Based on the findings of this study, we recommend maintaining a CONUT score below 5 for patients with ADHF in Jiangxi, China, as it may significantly contribute to reducing the risk of in-hospital all-cause mortality.
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Existing matrix factorization methods face challenges, including the cold start problem and global nonlinear data loss during similarity learning, particularly in predicting associations between long noncoding RNAs (LncRNAs) and diseases. To overcome these issues, we introduce HPTRMF, a matrix factorization approach incorporating high-order perturbation and flexible trifactor regularization. HPTRMF constructs a high-order correlation matrix utilizing the known association matrix, leveraging high-order perturbation to effectively address the cold start problem caused by data sparsity. Additionally, HPTRMF incorporates a flexible trifactor regularization term to capture similarity information on LncRNAs and diseases, enabling the effective handling of global nonlinear data loss by capturing such data in the similarity matrix. Experimental results demonstrate the superiority of HPTRMF over nine state-of-the-art algorithms in Leave-One-Out Cross-Validation (LOOCV) and Five-Fold Cross-Validation (5-Fold CV) on three data sets.HPTRMF and data sets are available in https://github.com/Llvvvv/HPTRMF.
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AIMS: The value of the systemic immune-inflammatory index (SII) in assessing adverse outcomes in various cardiovascular diseases has been extensively discussed. This study aims to evaluate the predictive value and risk stratification ability of SII for 30 day mortality in patients with acute decompensated heart failure (ADHF). METHODS: This analysis included 1452 patients hospitalized for ADHF, all the participants being part of the China Jiangxi-acute decompensated heart failure1 project. The risk stratification capability of the SII in patients with ADHF, as well as its correlation with the 30 day mortality risk among ADHF patients, was evaluated utilizing Kaplan-Meier survival analysis and multivariable Cox regression models. A restricted cubic spline was employed to model the dose-response relationship between the two, and the receiver operating characteristic curve was utilized to assess the predictive ability of SII for 30 day mortality. RESULTS: The Kaplan-Meier analysis revealed that the risk of mortality in the high SII group (SII ≥ 980 × 109/L) was significantly greater than that in the low SII group (SII < 980 × 109/L, log-rank P < 0.001). After adjusting for various confounding factors, a higher SII was associated with an increased risk of 30 day mortality in ADHF patients [hazard ratio (HR) = 2.03, 95% confidence interval (CI): 1.34-3.08]. Further restricted cubic spline analysis revealed a non-linear dose-response relationship between the two (P for non-linear = 0.006). Receiver operating characteristic analysis demonstrated that SII had a high accuracy in predicting 30 day mortality events in ADHF patients (AUC = 0.7479), and the optimal predictive threshold was calculated to be 980 × 109/L, a sensitivity of 0.7547 and a specificity of 0.7234. CONCLUSIONS: This study found a significant positive association between SII and 30 day all-cause mortality in ADHF patients. We determined the SII cut-off point for predicting 30 day all-cause mortality in patients with ADHF to be 980 × 109/L.
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Insuficiência Cardíaca , Humanos , Insuficiência Cardíaca/mortalidade , Insuficiência Cardíaca/imunologia , Masculino , Feminino , Prognóstico , Idoso , Doença Aguda , Medição de Risco/métodos , China/epidemiologia , Pessoa de Meia-Idade , Curva ROC , Taxa de Sobrevida/tendências , Inflamação/imunologia , Seguimentos , Biomarcadores/sangue , Estudos RetrospectivosRESUMO
Objective: Arteriosclerosis is a primary causative factor in cardiovascular diseases. This study aims to explore the correlation between the atherogenic index of plasma (AIP) and the 30-day mortality rate in patients with acute decompensated heart failure (ADHF). Methods: A total of 1,248 ADHF patients recruited from the Jiangxi-Acute Decompensated Heart Failure1 (JX-ADHF1) cohort between 2019 and 2022 were selected for this study. The primary outcome was the 30-day mortality rate. Multivariable Cox regression, restricted cubic splines (RCS), and stratified analyses were utilized to assess the relationship between AIP and the 30-day mortality rate in ADHF patients. Mediation models were employed for exploratory analysis of the roles of inflammation, oxidative stress, and nutrition in the association between AIP and the 30-day mortality rate in ADHF patients. Results: During the 30-day follow-up, 42 (3.37%) of the ADHF patients died. The mortality rates corresponding to the quartiles of AIP were as follows: Q1: 1.28%, Q2: 2.88%, Q3: 2.88%, Q4: 6.41%. The multivariable Cox regression revealed a positive correlation between high AIP and the 30-day mortality rate in ADHF patients [Hazard ratio (HR) 3.94, 95% confidence interval (CI): 1.08-14.28], independent of age, gender, heart failure type, cardiac function classification, and comorbidities. It is important to note that there was a U-shaped curve association between AIP (<0.24) and the 30-day mortality rate before the fourth quartile, with the lowest 30-day mortality risk in ADHF patients around an AIP of -0.1. Furthermore, mediation analysis suggested significant mediating effects of inflammation and nutrition on the 30-day mortality rate in ADHF patients related to AIP, with inflammation accounting for approximately 24.29% and nutrition for about 8.16% of the mediation effect. Conclusion: This retrospective cohort analysis reveals for the first time the association between AIP and the 30-day mortality rate in ADHF patients. According to our findings, maintaining an AIP around -0.1 in ADHF patients could be crucial for improving poor prognoses from a medical perspective. Additionally, for ADHF patients with high AIP, it is important to assess and, if necessary, enhance nutritional support and anti-inflammatory treatment.
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Aterosclerose , Insuficiência Cardíaca , Humanos , Insuficiência Cardíaca/mortalidade , Insuficiência Cardíaca/sangue , Masculino , Feminino , Idoso , Pessoa de Meia-Idade , Aterosclerose/mortalidade , Aterosclerose/sangue , Aterosclerose/complicações , Prognóstico , Seguimentos , Biomarcadores/sangue , Doença Aguda , Estudos de Coortes , Fatores de RiscoRESUMO
BACKGROUND: Malnutrition increases the risk of poor prognosis in patients with cardiovascular disease, and our current research was designed to assess the predictive performance of the Geriatric Nutrition Risk Index (GNRI) for the occurrence of poor prognosis after percutaneous coronary intervention (PCI) in patients with stable coronary artery disease (SCAD) and to explore possible thresholds for nutritional intervention. METHODS: This study retrospectively enrolled newly diagnosed SCAD patients treated with elective PCI from 2014 to 2017 at Shinonoi General Hospital, with all-cause death as the main follow-up endpoint. Cox regression analysis and restricted cubic spline (RCS) regression analysis were used to explore the association of GNRI with all-cause death risk and its shape. Receiver operating characteristic curve (ROC) analysis and piecewise linear regression analysis were used to evaluate the predictive performance of GNRI level at admission on all-cause death in SCAD patients after PCI and to explore possible nutritional intervention threshold points. RESULTS: The incidence of all-cause death was 40.47/1000 person-years after a mean follow-up of 2.18 years for 204 subjects. Kaplan-Meier curves revealed that subjects at risk of malnutrition had a higher all-cause death risk. In multivariate Cox regression analysis, each unit increase in GNRI reduced the all-cause death risk by 14% (HR 0.86, 95% CI 0.77, 0.95), and subjects in the GNRI > 98 group had a significantly lower risk of death compared to those in the GNRI < 98 group (HR 0.04, 95% CI 0.00, 0.89). ROC analysis showed that the baseline GNRI had a very high predictive performance for all-cause death (AUC = 0.8844), and the predictive threshold was 98.62; additionally, in the RCS regression analysis and piecewise linear regression analysis we found that the threshold point for the GNRI-related all-cause death risk was 98.28 and the risk will be significantly reduced when the subjects' baseline GNRI was greater than 98.28. CONCLUSIONS: GNRI level at admission was an independent predictor of all-cause death in SCAD patients after PCI, and GNRI equal to 98.28 may be a useful threshold for nutritional intervention in SCAD patients treated with PCI.
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Causas de Morte , Doença da Artéria Coronariana , Avaliação Geriátrica , Desnutrição , Avaliação Nutricional , Estado Nutricional , Intervenção Coronária Percutânea , Valor Preditivo dos Testes , Humanos , Masculino , Feminino , Intervenção Coronária Percutânea/efeitos adversos , Intervenção Coronária Percutânea/mortalidade , Idoso , Medição de Risco , Doença da Artéria Coronariana/mortalidade , Doença da Artéria Coronariana/terapia , Doença da Artéria Coronariana/diagnóstico , Desnutrição/diagnóstico , Desnutrição/mortalidade , Desnutrição/fisiopatologia , Estudos Retrospectivos , Fatores de Risco , Pessoa de Meia-Idade , Resultado do Tratamento , Fatores de Tempo , Fatores Etários , Idoso de 80 Anos ou mais , Japão/epidemiologiaRESUMO
AIM: The aim of this study was to investigate the relationship between the haemoglobin glycation index (HGI), and cardiovascular disease (CVD) and all-cause mortality in adults with pre-diabetes and diabetes. METHODS: This study included 10 267 adults with pre-diabetes and diabetes from the National Health and Nutrition Examination Survey (NHANES) 1999-2018. Sex-differentiated relationships between HGI and mortality were elucidated using multivariate Cox proportional hazards models, restricted cubic splines and a two-piecewise Cox proportional hazards model. RESULTS: During the median follow-up time of 103.5 months, a total of 535 CVD deaths and 1918 all-cause deaths were recorded. After multivariate adjustment, in males with pre-diabetes and diabetes, there was a U-shaped relationship between HGI and CVD mortality and all-cause mortality, with threshold points of -0.68 and -0.63, respectively. Before the threshold point, HGI was negatively associated with CVD mortality [hazard ratio (HR) 0.60; 95% confidence interval (CI) 0.41, 0.89] and all-cause mortality (HR 0.56; 95% CI 0.43, 0.74), and after the threshold point, HGI was positively associated with CVD mortality (HR 1.46; 95% CI 1.23, 1.73) and all-cause mortality (HR 1.40; 95% CI 1.23, 1.59). In contrast, HGI had an L-shaped relationship with all-cause mortality and no significant association with CVD mortality in females. To the left of the threshold points, the risk of all-cause mortality decreased (HR 0.50; 95% CI 0.35, 0.71) progressively with increasing HGI. CONCLUSIONS: In the cohort study, HGI in pre-diabetic and diabetic populations was found to have a U-shaped association with CVD mortality and all-cause mortality in males and an L-shaped association with all-cause mortality only in females. Further prospective and mechanistic studies are warranted.
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Doenças Cardiovasculares , Causas de Morte , Hemoglobinas Glicadas , Estado Pré-Diabético , Humanos , Masculino , Feminino , Estado Pré-Diabético/mortalidade , Estado Pré-Diabético/sangue , Estado Pré-Diabético/complicações , Doenças Cardiovasculares/mortalidade , Doenças Cardiovasculares/sangue , Pessoa de Meia-Idade , Estudos Prospectivos , Hemoglobinas Glicadas/metabolismo , Hemoglobinas Glicadas/análise , Adulto , Fatores Sexuais , Inquéritos Nutricionais , Fatores de Risco , Diabetes Mellitus/mortalidade , Diabetes Mellitus/sangue , Idoso , Mortalidade , Estudos de Coortes , Modelos de Riscos ProporcionaisRESUMO
Computational approaches employed for predicting potential microbe-disease associations often rely on similarity information between microbes and diseases. Therefore, it is important to obtain reliable similarity information by integrating multiple types of similarity information. However, existing similarity fusion methods do not consider multi-order fusion of similarity networks. To address this problem, a novel method of linear neighborhood label propagation with multi-order similarity fusion learning (MOSFL-LNP) is proposed to predict potential microbe-disease associations. Multi-order fusion learning comprises two parts: low-order global learning and high-order feature learning. Low-order global learning is used to obtain common latent features from multiple similarity sources. High-order feature learning relies on the interactions between neighboring nodes to identify high-order similarities and learn deeper interactive network structures. Coefficients are assigned to different high-order feature learning modules to balance the similarities learned from different orders and enhance the robustness of the fusion network. Overall, by combining low-order global learning with high-order feature learning, multi-order fusion learning can capture both the shared and unique features of different similarity networks, leading to more accurate predictions of microbe-disease associations. In comparison to six other advanced methods, MOSFL-LNP exhibits superior prediction performance in the leave-one-out cross-validation and 5-fold validation frameworks. In the case study, the predicted 10 microbes associated with asthma and type 1 diabetes have an accuracy rate of up to 90% and 100%, respectively.
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Algoritmos , Humanos , Biologia Computacional/métodos , Aprendizado de MáquinaRESUMO
DNA 4 mC plays a crucial role in the genetic expression process of organisms. However, existing deep learning algorithms have shortcomings in the ability to represent DNA sequence features. In this paper, we propose a 4 mC site identification algorithm, DNABert-4mC, based on a fusion of the pruned pre-training DNABert-Pruning model and artificial feature encoding to identify 4 mC sites. The algorithm prunes and compresses the DNABert model, resulting in the pruned pre-training model DNABert-Pruning. This model reduces the number of parameters and removes redundancy from output features, yielding more precise feature representations while upholding accuracy.Simultaneously, the algorithm constructs an artificial feature encoding module to assist the DNABert-Pruning model in feature representation, effectively supplementing the information that is missing from the pre-trained features. The algorithm also introduces the AFF-4mC fusion strategy, which combines artificial feature encoding with the DNABert-Pruning model, to improve the feature representation capability of DNA sequences in multi-semantic spaces and better extract 4 mC sites and the distribution of nucleotide importance within the sequence. In experiments on six independent test sets, the DNABert-4mC algorithm achieved an average AUC value of 93.81%, outperforming seven other advanced algorithms with improvements of 2.05%, 5.02%, 11.32%, 5.90%, 12.02%, 2.42% and 2.34%, respectively.
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Algoritmos , DNA , DNA/genética , NucleotídeosRESUMO
BACKGROUND: Atherosclerosis is closely linked with glucose metabolism. We aimed to investigate the role of the atherogenic index of plasma (AIP) in the reversal of prediabetes to normal blood glucose levels or its progression to diabetes. METHODS: This multi-center retrospective cohort study included 15,421 prediabetic participants from 32 regions across 11 cities in China, under the aegis of the Rich Healthcare Group's affiliated medical examination institutions. Throughout the follow-up period, we monitored changes in the glycemic status of these participants, including reversal to normal fasting glucose (NFG), persistence in the prediabetic state, or progression to diabetes. Segmented regression, stratified analysis, and restricted cubic spline (RCS) were performed based on the multivariable Cox regression model to evaluate the association between AIP and the reversal of prediabetes to NFG or progression to diabetes. RESULTS: During a median follow-up period of 2.9 years, we recorded 6,481 individuals (42.03%) reverting from prediabetes to NFG, and 2,424 individuals (15.72%) progressing to diabetes. After adjusting for confounders, AIP showed a positive correlation with the progression from prediabetes to diabetes [(Hazard ratio (HR) 1.42, 95% confidence interval (CI):1.24-1.64)] and a negative correlation with the reversion from prediabetes to NFG (HR 0.89, 95%CI:0.81-0.98); further RCS demonstrated a nonlinear relationship between AIP and the reversion from prediabetes to NFG/progression to diabetes, identifying a turning point of 0.04 for reversion to NFG and 0.17 for progression to diabetes. In addition, we observed significant differences in the association between AIP and reversion from prediabetes to NFG/progression to diabetes across age subgroups, specifically indicating that the risk associated with AIP for progression from prediabetes to diabetes was relatively higher in younger populations; likewise, a younger age within the adult group favored the reversion from prediabetes to NFG in relation to AIP. CONCLUSION: Our study, for the first time, reveals a negative correlation between AIP and the reversion from prediabetes to normoglycemia and validates the crucial role of AIP in the risk assessment of prediabetes progression. Based on threshold analysis, therapeutically, keeping the AIP below 0.04 was of paramount importance for individuals with prediabetes aiming for reversion to NFG; preventatively, maintaining AIP below 0.17 was vital to reduce the risk of diabetes onset for those with prediabetes.
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Aterosclerose , Diabetes Mellitus , Estado Pré-Diabético , Adulto , Humanos , Estado Pré-Diabético/diagnóstico , Estado Pré-Diabético/epidemiologia , Estudos Retrospectivos , Diabetes Mellitus/diagnóstico , Diabetes Mellitus/epidemiologia , Jejum , Aterosclerose/diagnóstico , Aterosclerose/epidemiologiaRESUMO
Most existing graph neural network-based methods for predicting miRNA-disease associations rely on initial association matrices to pass messages, but the sparsity of these matrices greatly limits performance. To address this issue and predict potential associations between miRNAs and diseases, we propose a method called strengthened hypergraph convolutional autoencoder (SHGAE). SHGAE leverages multiple layers of strengthened hypergraph neural networks (SHGNN) to obtain robust node embeddings. Within SHGNN, we design a strengthened hypergraph convolutional network module (SHGCN) that enhances original graph associations and reduces matrix sparsity. Additionally, SHGCN expands node receptive fields by utilizing hyperedge features as intermediaries to obtain high-order neighbor embeddings. To improve performance, we also incorporate attention-based fusion of self-embeddings and SHGCN embeddings. SHGAE predicts potential miRNA-disease associations using a multilayer perceptron as the decoder. Across multiple metrics, SHGAE outperforms other state-of-the-art methods in five-fold cross-validation. Furthermore, we evaluate SHGAE on colon and lung neoplasms cases to demonstrate its ability to predict potential associations. Notably, SHGAE also performs well in the analysis of gastric neoplasms without miRNA associations.
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MicroRNAs , MicroRNAs/genética , Algoritmos , Redes Neurais de Computação , Biologia Computacional/métodosRESUMO
[S U M M A R Y] Many miRNA-disease association prediction models incorporate Gaussian interaction profile kernel similarity (GIPS). However, the GIPS fails to consider the specificity of the miRNA-disease association matrix, where matrix elements with a value of 0 represent miRNA and disease relationships that have not been discovered yet. To address this issue and better account for the impact of known and unknown miRNA-disease associations on similarity, we propose a method called vector projection similarity-based method for miRNA-disease association prediction (VPSMDA). In VPSMDA, we introduce three projection rules and combined with logistic functions for the miRNA-disease association matrix and propose a vector projection similarity measure for miRNAs and diseases. By integrating the vector projection similarity matrix with the original one, we obtain the improved miRNA and disease similarity matrix. Additionally, we construct a weight matrix using different numbers of neighbors to reduce the noise in the similarity matrix. In performance evaluation, both LOOCV and 5-fold CV experiments demonstrate that VPSMDA outperforms seven other state-of-the-art methods in AUC. Furthermore, in a case study, VPSMDA successfully predicted 10, 9, and 10 out of the top 10 associations for three important human diseases, respectively, and these predictions were confirmed by recent biomedical resources.
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MicroRNAs , Humanos , MicroRNAs/genética , MicroRNAs/metabolismo , Predisposição Genética para Doença , Algoritmos , Modelos Genéticos , Área Sob a Curva , Biologia Computacional/métodosRESUMO
Objective: Several recent reports have suggested the use of mean arterial blood pressure (MAP) to assess/predict the risk of developing atherosclerosis, chronic kidney disease, diabetes, metabolic syndrome, and poor prognosis in a variety of cardiovascular and cerebrovascular diseases. The current study aimed to investigate the association of MAP with non-alcoholic fatty liver disease (NAFLD) and to explore the differences in this association across populations. Methods: This study used data from the NAGALA study from 1994 to 2016. MAP was calculated as 1/3 systolic blood pressure (SBP) + 2/3 diastolic blood pressure (DBP). Restricted cubic spline (RCS) and logistic regression models were used to examine the correlation of MAP with NAFLD. Results: The study population was 14,251 general people undergoing health screening, with a median (interquartile range) age of 42 (36-50) years; among them, 48% were women, and 2,507 (17.59%) were diagnosed with NAFLD. After fully controlling for confounders in the current dataset, MAP was positively and non-linearly associated with NAFLD [(odds ratios (ORs): 1.39, 95% confidence intervals (CIs): 1.15, 1.68); P for non-linearity = 0.024]; the dose-response curve showed that there was a transient saturation effect interval when MAP was between 85 and 95â mmHg, where the risk of NAFLD was neither increased nor decreased. The results of the stratified analysis showed that the risk of NAFLD associated with MAP appeared to be influenced only by age (P-interaction = 0.002), but not by sex, body mass index (BMI), habits of exercise, drinking status, or smoking status (P-interaction > 0.05); further age-stratified RCS analysis showed that the non-linear association between MAP and NAFLD in the young and middle-aged and the middle-aged and elderly populations was consistent with the results of the whole population, whereas, in the elderly population, a U-shaped curve association between MAP and NAFLD was observed, with both low and high MAP increasing the risk of NAFLD. Conclusion: In the general population, MAP was positively and non-linearly associated with NAFLD, and this association only differed significantly by age, but not by sex, BMI, habits of exercise, drinking status, and smoking status.
RESUMO
M6A methylation is the most prevalent and abundant RNA modification in mammals. Although there are many studies on the regulatory role of m6A methylation in the immune response, the m6A regulators in the pathogenesis of acute ST-segment elevation myocardial infarction (STEMI) remain unclear. We comprehensively analysed the role of m6A regulators in STEMI and built a predictive model, revealing the relationship between m6A methylations and the immune microenvironment. Differential analysis revealed that 18 of 24 m6A regulators were significantly differentially expressed, and there were substantial interactions between the m6A regulator. Then, we established a classifier and nomogram model based on 6 m6A regulators, which can easily distinguish the STEMI and control samples. Finally, two distinct m6A subtypes were obtained and significantly differentially expressed in terms of infiltrating immunocyte abundance, immune reaction activity and human leukocyte antigen genes. Three hub m6A phenotype related genes (RAC2, RELA, and WAS) in the midnightblue module were identified by weighted gene coexpression network analysis, and were associated with immunity. These findings suggest that m6A modification and the immune microenvironment play a key role in the pathogenesis of STEMI.
Assuntos
Infarto do Miocárdio com Supradesnível do Segmento ST , Humanos , Animais , Metilação , Infarto do Miocárdio com Supradesnível do Segmento ST/genética , Arritmias Cardíacas , Redes Reguladoras de Genes , Nomogramas , MamíferosRESUMO
Accumulating evidence suggests that long non-coding RNAs (lncRNAs) are associated with various complex human diseases. They can serve as disease biomarkers and hold considerable promise for the prevention and treatment of various diseases. The traditional random walk algorithms generally exclude the effect of non-neighboring nodes on random walking. In order to overcome the issue, the neighborhood constraint (NC) approach is proposed in this study for regulating the direction of the random walk by computing the effects of both neighboring nodes and non-neighboring nodes. Then the association matrix is updated by matrix multiplication for minimizing the effect of the false negative data. The heterogeneous lncRNA-disease network is finally analyzed using an unbalanced random walk method for predicting the potential lncRNA-disease associations. The LUNCRW model is therefore developed for predicting potential lncRNA-disease associations. The area under the curve (AUC) values of the LUNCRW model in leave-one-out cross-validation and five-fold cross-validation were 0.951 and 0.9486 ± 0.0011, respectively. Data from published case studies on three diseases, including squamous cell carcinoma, hepatocellular carcinoma, and renal cell carcinoma, confirmed the predictive potential of the LUNCRW model. Altogether, the findings indicated that the performance of the LUNCRW method is superior to that of existing methods in predicting potential lncRNA-disease associations.
Assuntos
Neoplasias Renais , RNA Longo não Codificante , Humanos , RNA Longo não Codificante/genética , Algoritmos , Área Sob a Curva , CaminhadaRESUMO
BACKGROUND: It is known that measuring the triglyceride glucose (TyG) index and TyG-related parameters [triglyceride glucose-body mass index (TyG-BMI), triglyceride glucose-waist circumference (TyG-WC), and triglyceride glucose-waist to height ratio (TyG-WHtR)] can predict diabetes; this study aimed to compare the predictive value of the baseline TyG index and TyG-related parameters for the onset of diabetes at different future periods. METHODS: We conducted a longitudinal cohort study involving 15,464 Japanese people who had undergone health physical examinations. The subject's TyG index and TyG-related parameters were measured at the first physical examination, and diabetes was defined according to the American Diabetes Association criteria. Multivariate Cox regression models and time-dependent receiver operating characteristic (ROC) curves were constructed to examine and compare the risk assessment/predictive value of the TyG index and TyG-related parameters for the onset of diabetes in different future periods. RESULTS: The mean follow-up period of the current study cohort was 6.13 years, with a maximum of 13 years, and the incidence density of diabetes was 39.88/10,000 person-years. In multivariate Cox regression models with standardized hazard ratios (HRs), we found that both the TyG index and TyG-related parameters were significantly and positively associated with diabetes risk and that the TyG-related parameters were stronger in assessing diabetes risk than the TyG index, with TyG-WC being the best parameter (HR per SD increase: 1.70, 95% CI 1.46, 1.97). In addition, TyG-WC also showed the highest predictive accuracy in time-dependent ROC analysis for diabetes occurring in the short-term (2-6 years), while TyG-WHtR had the highest predictive accuracy and the most stable predictive threshold for predicting the onset of diabetes in the medium- to long-term (6-12 years). CONCLUSIONS: These results suggest that the TyG index combined with BMI, WC, and WHtR can further improve its ability to assess/predict the risk of diabetes in different future periods, where TyG-WC was not only the best parameter for assessing diabetes risk but also the best risk marker for predicting future diabetes in the short-term, while TyG-WHtR may be more suitable for predicting future diabetes in the medium- to long-term.
Assuntos
Diabetes Mellitus Tipo 2 , Glucose , Humanos , Triglicerídeos , Curva ROC , Diabetes Mellitus Tipo 2/diagnóstico , Diabetes Mellitus Tipo 2/epidemiologia , Estudos Longitudinais , Índice de Massa Corporal , Fatores de RiscoRESUMO
Objective: Triglyceride glucose body mass index (TyG-BMI) has been shown to be strongly associated with a variety of chronic diseases. However, little is known about the associations between TyG-BMI and normal-high blood pressure (BP) values and hypertension (HTN). Method: The current study was cross-sectional in design and included 15,464 non-diabetic participants recruited between 1994 and 2016 in the NAGALA (NAfld in the Gifu Area, Longitudinal Analysis) study. Associations between TyG-BMI and normal-high BP values and HTN were assessed using multivariate logistic regression. The ability of the TyG index, BMI, and their combined index TyG-BMI to identify normal-high BP values and HTN was compared by receiver operating characteristic (ROC) curves. Results: Among the 15,464 eligible non-diabetic participants, 28.56% (4,416/15,464) and 6.23% (964/15,464) had normal-high BP values and HTN, respectively. Multivariate logistic regression analysis showed positive correlations between BMI, TyG index, TyG-BMI and normal-high BP values/HTN; after standardized regression coefficients, TyG-BMI had the strongest association with normal-high BP values and HTN compared to BMI and TyG index. In the fully adjusted model, the odds ratio (OR) value corresponding to the relationship between TyG-BMI and HTN/normal-high BP values was 2.35; when TyG-BMI was used as a categorical variable, compared with the lowest quartile of TyG-BMI the regression coefficient for the association of the highest quartile of TyG-BMI with normal-high BP values increased by 426%, while the regression coefficient for the association with HTN increased by 527%. In further spline regression analysis, we also found that there was a linearly positive correlation between TyG-BMI and systolic BP/diastolic BP (SBP/DBP), which supported the linear trend between TyG-BMI and HTN/normal-high BP values (P-trend <0.0001). In addition, ROC analysis showed that TyG-BMI had good diagnostic values for both normal-high BP values and HTN, and TyG index combined with BMI can significantly improve the ability of a single index to identify normal-high BP values and HTN. Conclusion: In the non-diabetic population, TyG-BMI showed a significant positive correlation with both normal-high BP values and HTN, and TyG-BMI was of higher value for the identification of both normal-high BP values and HTN compared to BMI and TyG index alone.