Multistep analysis reveals the relationship between blood indices at the time of ovum pick-up and in vitro embryo production in heifers

Kowsar, Rasoul; Komeili, Mehdi; Sadeghi, Nima; Sadeghi, Khaled

doi:10.1016/j.theriogenology.2020.10.026

Cited by 6 publications

(7 citation statements)

References 55 publications

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“…Kebede 56 found, using a multilevel analysis, that among the parameters evaluated, the hemoglobin level and weight of patients were linked with the CD4 count of patients with diabetes. We previously found www.nature.com/scientificreports/ that the proportion of blastocyst formation in bovines was connected with various blood indexes or follicular fluid CRP and bilirubin employing a multilevel analysis 37,57 . Our findings using bivariate analysis indicated that age, BMI, and HbA1c had the highest connection with diabetes in the current research.…”

Section: Discussionmentioning

confidence: 99%

“…The ROC curve analysis was conducted on the basis of network outputs that defined a correlation between blood biochemical parameters, age, and BMI, and diabetes incidence using the easyROC web-tool (available at: http://www.biosoft.hacettepe.edu.tr/easyROC/ ) 37 . This was accomplished to determine the predictive strength and cut-off point of network-detected parameters for detecting diabetes occurrence using the AUC analysis.…”

Section: Methodsmentioning

confidence: 99%

“…This was accomplished to determine the predictive strength and cut-off point of network-detected parameters for detecting diabetes occurrence using the AUC analysis. The optimal cut-off was determined by maximizing the Youden index 37 .…”

Section: Methodsmentioning

confidence: 99%

“…edu. tr/ easyR OC/) 37 . This was accomplished to determine the predictive strength and cut-off point of network-detected parameters for detecting diabetes occurrence using the AUC analysis.…”

Section: Methodsmentioning

confidence: 99%

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Multi-level analysis reveals the association between diabetes, body mass index, and HbA1c in an Iraqi population

Kowsar

Mansouri

2022

Sci Rep

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Type 2 diabetes (T2D) known as a complex metabolic disorder may cause health problems and changes in blood biochemical markers. A growing number of studies have looked into several biomarkers and their connections with T2D risk. However, few have explored the interconnection of these biomarkers, as well as the prospective alterations in the diabetes biomarker correlation network. We conducted a secondary analysis in order to introduce a multi-level approach to establish a relationship between diabetes, pre-diabetes, blood biochemical markers, age, and body mass index (BMI). The dataset was obtained from the Mendeley Data (available at https://data.mendeley.com/datasets/wj9rwkp9c2/1. In this study, three groups were established: non-diabetic (n = 103), pre-diabetic (n = 53), and diabetic (n = 844). According to the Heatmap analysis, non-diabetic and pre-diabetic individuals had the lowest BMI, age, and HbA1c. Diabetes and pre-diabetes were correlated with BMI (r = 0.58 and − 0.27, respectively), age (r = 0.47 and − 0.28, respectively), and HbA1c (r = 0.55 and − 0.21, respectively) using Pearson analysis. Using multivariate analysis, we found that diabetes, BMI, age, HbA1c, cholesterol, triglyceride, LDL, VLDL, and HDL were all associated. Network analysis revealed a connection between BMI and diabetes at the highest cut-off point. Moreover, receiver operating characteristic (ROC) analysis validated the network findings, revealing that BMI (area under the ROC curve, AUC = 0.95), HbA1c (AUC = 0.94), and age (AUC = 0.84) were the best predictors of diabetes. In conclusion, our multi-step study revealed that identifying significant T2D predictors, such as BMI and HbA1c, required a series of mathematical analyses.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Multi-level analysis reveals the association between diabetes, body mass index, and HbA1c in an Iraqi population

Kowsar

Mansouri

2022

Sci Rep

Self Cite

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“…Ovarian sex steroids (estradiol (E2) or progesterone (P4)) can also affect the immune system. Estrogen and progesterone have been shown to stimulate peripheral blood monocytes (14); it can be postulated that the action or number of blood cells can vary in response to reproductive events (15). There have been several studies on blood markers related to infertility (16)(17)(18)(19).…”

Section: Introductionmentioning

confidence: 99%

Utility of Blood Markers for Predicting Outcomes of Fertility Preservation in Patients With Breast Cancer

et al. 2022

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This study aimed to investigate the usability of blood markers for predicting controlled ovarian stimulation (COS) outcomes in patients with breast cancer undergoing fertility preservation (FP). In total, 91 patients with breast cancer who had undergone COS using a letrozole-combined gonadotropin-releasing hormone (GnRH) antagonist protocol before chemotherapy were enrolled retrospectively in a single tertiary hospital. FP outcomes were compared in terms of the mean platelet volume (MPV), MPV/platelet count (PC), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR). The cutoff values for obtaining 10 or more mature oocytes as favorable prognoses were obtained for each parameter, and the COS outcomes were compared based on the cutoff values. The optimal cutoff levels for MPV and MPV/PC were 10.15 [sensitivity: 90.0%; specificity: 45.1%; AUC: 0.687; 95% CI (0.563, 0.810)] and 0.41 [sensitivity: 65.0%; specificity: 67.6%; AUC: 0.682; 95% CI (0.568, 0.796)], respectively. The oocyte numbers did not significantly differ with respect to the cutoff values of NLR, PLR, and LMR (p > 0.05). However, the total number of acquired and mature oocytes were significantly lower in the group with MPV<10.15 than in that with MPV≥10.15 (8.0 ± 5.1 vs. 12.6 ± 9.1, p=0.003; 4.0 ± 3.7 vs. 7.3 ± 6.3, p=0.002, respectively). Similarly, considering the cutoff of MPV/PC as 0.41, the low-MPV/PC group showed a significantly lower total oocyte yield than the high-MPV/PC group (9.5 ± 7.1 vs. 13.1 ± 9.1, p=0.048), whereas the number of mature oocytes showed similar patterns with no statistical significance (5.3 ± 5.4 vs. 7.3 ± 6.1, p=0.092). From logistic regression analysis, age, anti-Müllerian hormone (AMH) level, MPV, and MPV/PC≥0.41 were found to be significant factors for the acquisition of 10 or more MII oocytes (p=0.049, OR: 0.850; p<0.001, OR: 1.622; p=0.018, OR: 3.184; p=0.013, OR: 9.251, respectively). MPV or MPV/PC can be a reliable marker for predicting FP outcome in patients with breast cancer. Protocols to acquire more mature oocytes, such as the dual-trigger approach, could be recommended for patients with breast cancer with MPV<10.15. Furthermore, a higher dose of gonadotropins was considered to obtain more oocytes in patients with MPV/PC<0.41.

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Association of follicular fluid parameters at the time of ovum pick-up with in vitro blastocyst formation in Holstein heifers: a multi-level analysis

et al. 2021

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Multistep analysis reveals the relationship between blood indices at the time of ovum pick-up and in vitro embryo production in heifers

Cited by 6 publications

References 55 publications

Multi-level analysis reveals the association between diabetes, body mass index, and HbA1c in an Iraqi population

Multi-level analysis reveals the association between diabetes, body mass index, and HbA1c in an Iraqi population

Utility of Blood Markers for Predicting Outcomes of Fertility Preservation in Patients With Breast Cancer

Association of follicular fluid parameters at the time of ovum pick-up with in vitro blastocyst formation in Holstein heifers: a multi-level analysis

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