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Rashidmayvan M, Mansoori A, Aghasizadeh M, Dianati M, Barati S, Sahranavard T, Darroudi S, Ahari RK, Esmaily H, Ferns G, Sarabi MRM, Faridni R, Ghayour-Mobarhan M, Moohebati M. Prediction of cardiovascular disease risk by serum zinc and copper concentrations and anthropometric measurements. J Trace Elem Med Biol 2024; 83:127385. [PMID: 38278053 DOI: 10.1016/j.jtemb.2024.127385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 01/02/2024] [Accepted: 01/04/2024] [Indexed: 01/28/2024]
Abstract
INTRODUCTION We aimed to investigate the association between cardiovascular disease (CVD) and various anthropometric indices, as well as the serum levels of copper (Cu) and zinc (Zn), copper-zinc ratio (Cu/Zn ratio) and zinc-copper ratio (Zn/Cu ratio), in a large population sample from northeastern Iranian. METHOD 9704 individuals aged 35 to 65 were enrolled in the first phase of the study. After a 10-year follow-up, 7560 participants were enrolled into the second phase. The variables used in this study included demographic characteristics, such as gender and age; biochemical parameters including: serum Zn, Cu, Cu/Zn ratio, and Zn/Cu ratio; anthropometric parameters including: waist circumference (WC), body mass index (BMI), and waist-to-hip ratio (WHR). The relationship between the aforementioned indices and CVD was examined using decision tree (DT) and logistic regression (LR) models. RESULTS A total of 837 individuals were diagnosed with CVD among the 7560 participants. LR analysis showed that BMI, age, WH zinc-copper ratio (Zn/Cu ratio), and serum Zn/Cu ratio were significantly associated the development of CVD in men, and WHR, age, BMI, serum Cu, and Cu/Zn ratio in women. DT analysis showed that, age was the most important predictor of CVD in both genders. 71% of women, older than 49 years, with a WHR≥ 0.89, serum Cu< 75 (µg/dl), BMI≥ 22.93 (kg/m2), and serum Cu≥ 14 (µg/dl), had the highest risk of CVD. In men, among those who were ≥ 53 years, with a WHR≥ 0.98, serum Zn/Cu ratio< 1.69, and BMI≥ 22.30, had the highest risk of CVD. CONCLUSION Among Iranian adult population, BMI, age, and WHR were one of the predictors of CVD for both genders. The Zn/Cu ratio was CVD predictor for men while Cu/Zn ratio was CVD predictor for women.
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Affiliation(s)
- Mohammad Rashidmayvan
- Department of Nutrition, Food Sciences and Clinical Biochemistry, School of Medicine, Social Determinants of Health Research Center, Gonabad University of Medical Sciences, Gonabad, Iran
| | - Amin Mansoori
- Department of Applied Mathematics, Ferdowsi University of Mashhad, Mashhad, Iran; Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Malihe Aghasizadeh
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Maryam Dianati
- Student Research Committee, Rafsanjan University of Medical Sciences, Rafsanjan, Iran
| | - Sama Barati
- Student Research Committee, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Toktam Sahranavard
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Susan Darroudi
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran.
| | - Rana Kolahi Ahari
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Habibollah Esmaily
- Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran; Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Gordon Ferns
- Brighton and Sussex Medical School, Division of Medical Education, Brighton, United Kingdom
| | | | - Reyhaneh Faridni
- Department of Nutrition, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Majid Ghayour-Mobarhan
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran.
| | - Mohsen Moohebati
- Cardiovascular Research Center, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
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Lee YC, Chang CT, Chen RH, Wang TY, Chen CC. HbA1c and systolic blood pressure variation to predict all-cause mortality in patients with type 2 diabetes mellitus. Prim Care Diabetes 2024; 18:146-150. [PMID: 38309986 DOI: 10.1016/j.pcd.2024.01.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/20/2024] [Accepted: 01/28/2024] [Indexed: 02/05/2024]
Abstract
BACKGROUND Glycated hemoglobin A1c (HbA1c) variation or blood pressure (BP) variation was known to be an independent predictor of all-cause mortality in patients with type 2 diabetes mellitus (T2DM). This study aimed to investigate the combined effect of HbA1c and systolic blood pressure (SBP) variation on all-cause mortality and if there was a gender difference in patients with T2DM. METHODS Patients with T2DM who had at least three HbA1c, SBP measurements within 12-24 months during 2001-2007 were included. Coefficient of variation (CV) was used to evaluate variation. The 75th percentile of HbA1c-CV and SBP-CV were set as a cutoff to define high and low variation. Hazard ratios (HRs) and 95% confidence intervals were estimated using Cox proportional hazard models. RESULTS A total of 2744 patients were included, of whom 769 died during the 11.7 observation years. The associated risk of all-cause mortality was 1.22 [1.01- 1.48], P = 0.044, for low HbA1c-CV & high SBP-CV; 1.28 [1.04-1.57], P = 0.020, for high HbA1c-CV & low SBP-CV; and 1.68 [1.31-2.17], P < 0.001, for high HbA1c-CV & high SBP-CV. The associated risk remained unchanged in either males or females older than 50 years old, although there is only numerically higher for high HbA1c-CV & low SBP-CV in females older than 50 years old. CONCLUSIONS Both HbA1c and SBP variation were significant predictors of all-cause mortality in patients with T2DM. The combined effect was higher than either alone and no gender difference in patients older than 50 years old.
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Affiliation(s)
- Yun-Chi Lee
- Division of Endocrinology and Metabolism, Department of Medicine, China Medical University Hospital, Taichung 40447, Taiwan; Department of Medicine, China Medical University, Taichung 40402, Taiwan
| | - Chwen-Tzuei Chang
- Division of Endocrinology and Metabolism, Department of Medicine, China Medical University Hospital, Taichung 40447, Taiwan
| | - Rong-Hsing Chen
- Division of Endocrinology and Metabolism, Department of Medicine, China Medical University Hospital, Taichung 40447, Taiwan
| | - Tzu-Yuan Wang
- Division of Endocrinology and Metabolism, Department of Medicine, China Medical University Hospital, Taichung 40447, Taiwan; School of Medicine, China Medical University, Taichung 40402, Taiwan
| | - Ching-Chu Chen
- Division of Endocrinology and Metabolism, Department of Medicine, China Medical University Hospital, Taichung 40447, Taiwan; School of Chinese Medicine, China Medical University, Taichung 40402, Taiwan.
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Lv X, Luo J, Huang W, Guo H, Bai X, Yan P, Jiang Z, Zhang Y, Jing R, Chen Q, Li M. Identifying diagnostic indicators for type 2 diabetes mellitus from physical examination using interpretable machine learning approach. Front Endocrinol (Lausanne) 2024; 15:1376220. [PMID: 38562414 PMCID: PMC10982324 DOI: 10.3389/fendo.2024.1376220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 02/29/2024] [Indexed: 04/04/2024] Open
Abstract
Background Identification of patients at risk for type 2 diabetes mellitus (T2DM) can not only prevent complications and reduce suffering but also ease the health care burden. While routine physical examination can provide useful information for diagnosis, manual exploration of routine physical examination records is not feasible due to the high prevalence of T2DM. Objectives We aim to build interpretable machine learning models for T2DM diagnosis and uncover important diagnostic indicators from physical examination, including age- and sex-related indicators. Methods In this study, we present three weighted diversity density (WDD)-based algorithms for T2DM screening that use physical examination indicators, the algorithms are highly transparent and interpretable, two of which are missing value tolerant algorithms. Patients Regarding the dataset, we collected 43 physical examination indicator data from 11,071 cases of T2DM patients and 126,622 healthy controls at the Affiliated Hospital of Southwest Medical University. After data processing, we used a data matrix containing 16004 EHRs and 43 clinical indicators for modelling. Results The indicators were ranked according to their model weights, and the top 25% of indicators were found to be directly or indirectly related to T2DM. We further investigated the clinical characteristics of different age and sex groups, and found that the algorithms can detect relevant indicators specific to these groups. The algorithms performed well in T2DM screening, with the highest area under the receiver operating characteristic curve (AUC) reaching 0.9185. Conclusion This work utilized the interpretable WDD-based algorithms to construct T2DM diagnostic models based on physical examination indicators. By modeling data grouped by age and sex, we identified several predictive markers related to age and sex, uncovering characteristic differences among various groups of T2DM patients.
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Affiliation(s)
- Xiang Lv
- College of Chemistry, Sichuan University, Chengdu, China
| | - Jiesi Luo
- Basic Medical College, Southwest Medical University, Luzhou, China
| | - Wei Huang
- Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Metabolic Vascular Disease Key Laboratoryof Sichuan Province, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Hui Guo
- College of Chemistry, Sichuan University, Chengdu, China
| | - Xue Bai
- Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Metabolic Vascular Disease Key Laboratoryof Sichuan Province, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Pijun Yan
- Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Metabolic Vascular Disease Key Laboratoryof Sichuan Province, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Zongzhe Jiang
- Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Metabolic Vascular Disease Key Laboratoryof Sichuan Province, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Yonglin Zhang
- Department of Pharmacy, The Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Runyu Jing
- School of Cyber Science and Engineering, Sichuan University, Chengdu, China
| | - Qi Chen
- Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Metabolic Vascular Disease Key Laboratoryof Sichuan Province, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Department of Nursing, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- School of Nursing, Southwest Medical University, Luzhou, China
| | - Menglong Li
- College of Chemistry, Sichuan University, Chengdu, China
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Mansoori A, Seifi N, Vahabzadeh R, Hajiabadi F, Mood MH, Harimi M, Poudineh M, Ferns G, Esmaily H, Ghayour-Mobarhan M. The relationship between anthropometric indices and the presence of hypertension in an Iranian population sample using data mining algorithms. J Hum Hypertens 2024; 38:277-285. [PMID: 38040904 DOI: 10.1038/s41371-023-00877-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 09/10/2023] [Accepted: 11/01/2023] [Indexed: 12/03/2023]
Abstract
Hypertension (HTN) is a common chronic condition associated with increased morbidity and mortality. Anthropometric indices of adiposity are known to be associated with a risk of HTN. The aim of this study was to identify the anthropometric indices that best associate with HTN in an Iranian population. 9704 individuals aged 35-65 years were recruited as part of the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) study. Demographic and anthropometric data of all participants were recorded. HTN was defined as a systolic blood pressure (SBP) ≥ 140 mmHg, and/ or a diastolic blood pressure (DBP) ≥ 90 mmHg on two subsequent measurements, or being treated with oral drug therapy for BP. Data mining methods including Logistic Regression (LR), Decision Tree (DT), and Bootstrap Forest (BF) were applied. Of 9704 participants, 3070 had HTN, and 6634 were normotensive. LR showed that body roundness index (BRI), body mass index (BMI) and visceral adiposity index (VAI) were significantly associated with HTN in both genders (P < 0.0001). BRI showed the greatest association with HTN (OR = 1.276, 95%CI = (1.224, 1.330)). For BMI we had OR = 1.063, 95%CI = (1.047, 1.080), for VAI we had OR = 1.029, 95%CI = (1.020, 1.038). An age < 47 years and BRI < 4.04 was associated with a 90% probability of being normotensive. The BF indicated that age, sex and BRI had the most important role in HTN. In summary, among anthropometric indices the most powerful indicator for discriminating hypertensive from normotensive patients was BRI.
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Affiliation(s)
- Amin Mansoori
- International UNESCO center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
- Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
- Department of Applied Mathematics, Ferdowsi University of Mashhad, Mashhad, Iran, Mashhad, Iran
| | - Najmeh Seifi
- International UNESCO center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Reihaneh Vahabzadeh
- Student Research Committee, Paramedicine Faculty, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Fatemeh Hajiabadi
- Student Research Committee, Paramedicine Faculty, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Melika Hakimi Mood
- Department of Nutrition Sciences, Varastegan Institute for Medical Sciences, Mashhad, Iran
| | - Mahdiar Harimi
- Department of Nutrition Sciences, Varastegan Institute for Medical Sciences, Mashhad, Iran
| | - Mohadeseh Poudineh
- Faculty of Medicine, Islamic Azad University of Mashhad, Mashhad, Iran
- Student of Research Committee, School of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran, Zanjan, Iran
| | - Gordon Ferns
- Brighton and Sussex Medical School, Division of Medical Education, Brighton, UK
| | - Habibollah Esmaily
- Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran.
- Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
| | - Majid Ghayour-Mobarhan
- International UNESCO center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran.
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Bambo GM, Asmelash D, Alemayehu E, Gedefie A, Duguma T, Kebede SS. Changes in selected hematological parameters in patients with type 1 and type 2 diabetes: a systematic review and meta-analysis. Front Med (Lausanne) 2024; 11:1294290. [PMID: 38444411 PMCID: PMC10912516 DOI: 10.3389/fmed.2024.1294290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 01/29/2024] [Indexed: 03/07/2024] Open
Abstract
Background Diabetes mellitus is a chronic metabolic disorder that causes hyperglycemia and various life-threatening health problems. Although hematological parameters play a significant role in the progression and pathogenesis of diabetes, many studies have explored contradictory findings. Therefore, this evidence-based study aimed to determine the pooled mean difference of white blood cell and red blood cell parameters in diabetic patients in order to investigate hematological dysfunctions in type 1 and type 2 diabetes mellitus. Methods Articles were extensively searched in bibliographic databases (PubMed, Cochrane library, Scopus, Web of Science, PsycINFO, Embase, online archives and university repositories) using appropriate entry terms. For studies meeting the eligibility criteria, the first author's name, year of publication, study design and area, type of diabetes mellitus, sample size, and mean and standard deviation of hematological parameters were extracted using Microsoft Excel and exported to Stata 11 for meta-analysis. The pooled standardized mean difference (SMD) was determined using the random effects model, and heterogeneity was quantified using Higgins' I2 statistics. Egger's test and funnel plot were performed to measure bias. Furthermore, a sensitivity analysis was performed to determine the small study effect. Results Initially 39, 222 articles were identified. After screening of the entire methodology, 22 articles with 14,041 study participants (6,146 T2DM, 416 T1DM patients and 7,479 healthy controls) were included in this study. The pooled SMD in TLC (109/L) was 0.66 and -0.21, in T2DM and T1DM, respectively. Differences in absolute differential WBC counts for neutrophils, eosinophils, basophils, lymphocytes and monocytes in T2DM were 0.84, -1.59, 3.20, 0.36 and 0.26, respectively. The differences in relative differential counts (%) in T2DM were as follows: neutrophils: 1.31, eosinophils: -0.99, basophils: 0.34, lymphocytes: -0.19 and monocyte: -0.64. The SMD of differential counts of WBC (109/L) parameters; neutrophils, lymphocytes, monocytes and basophils in T1DM were -0.10, -0.69, 0.19, and -0.32, respectively. The pooled SMD in RBC parameters in T2DM were as follows: RBC: -0.57 (106/μL), Hb: -0.73 g/dL and HCT: -1.22%, Where as in T1DM RBC, Hb and HCT were -1.23 (106/μL), -0.80 g/dL and -0.29%, respectively. Conclusion Patients with T2DM had significantly increased TLC counts, absolute neutrophil, basophil, lymphocyte, monocyte counts and relative counts of neutrophils and basophils in comparison to controls. On the contrary, the absolute eosinophil count and relative lymphocyte, eosinophil and monocyte counts were decreased. In T1DM, WBC parameters were significantly decreased except monocytes. RBC parameters were found to be significantly decreased in T2DM patients. In T1DM, Hb and HCT were significantly decreased. However, there is no significant difference in RBC as compared with non-diabetic controls. The findings indicated a significant alteration of WBC and RBC parameters in both diabetic patients suggesting the considerable metabolic effect of diabetes on hematologic parameters. Systematic review registration https://www.crd.york.ac.uk/prospero/export_details_pdf.php, identifier [CRD42023413486].
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Affiliation(s)
- Getachew Mesfin Bambo
- Department of Medical Laboratory Sciences, College of Health Sciences, Mizan-Tepi University, Mīzan, Ethiopia
| | - Daniel Asmelash
- Department of Medical Laboratory Sciences, College of Health Sciences, Mizan-Tepi University, Mīzan, Ethiopia
| | - Ermiyas Alemayehu
- Department of Medical Laboratory Sciences, College of Medicine and Health Sciences, Wollo University, Dessie, Ethiopia
| | - Alemu Gedefie
- Department of Medical Laboratory Sciences, College of Medicine and Health Sciences, Wollo University, Dessie, Ethiopia
| | - Tadesse Duguma
- Department of Medical Laboratory Sciences, College of Health Sciences, Mizan-Tepi University, Mīzan, Ethiopia
| | - Samuel Sahile Kebede
- Department of Medical Laboratory Sciences, College of Health Sciences, Mizan-Tepi University, Mīzan, Ethiopia
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Mirinezhad MR, Aghsizadeh M, Fazl Mashhadi M, Moazedi S, Mohammadi Bajgiran M, Ghazizadeh H, Yaghouti S, Mohammadian Ghosooni M, Mohammadi MA, Hasanzadeh E, Ebrahimi Dabagh A, Rastegarmoghadam Ebrahimian A, Akbarpour E, Esmaily H, Ferns GA, Hamzehloei T, Pasdar A, Ghayour-Mobarhan M. Association between Genetic Variants Linked to Premature Ovarian Insufficiency and Inflammatory Markers: A Cross-Sectional Study. Int J Fertil Steril 2024; 18:100-107. [PMID: 38368511 PMCID: PMC10875312 DOI: 10.22074/ijfs.2023.560209.1365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 03/12/2023] [Accepted: 06/25/2023] [Indexed: 02/19/2024]
Abstract
BACKGROUND Premature menopause (PM) is the cessation of ovarian function before age 40. PM women are more likely to have cardiovascular diseases (CVDs), diabetes, and mental disorders. This is the first study that assessed the association of single nucleotide polymorphisms (SNPs) with anti-heat shock protein 27 (Hsp27), High-sensitivity C-reactive protein (hs- CRP), and PM and serum pro-oxidant-antioxidant balance (PAB), as putative risk factors for CVDs. We aimed to explore the association of oxidative stress markers with eight different SNPs shown to be related to premature menopause. MATERIALS AND METHODS In this cross-sectional research, we included 183 healthy women and 117 premature menopausal women. We determined baseline characteristics for all participants and measured serum hs-CRP, anti-HSP-27 antibody titer, and PAB levels using the established methods. Genotyping for eight SNPs was done using the tetra amplification refractory mutation system polymerase chain reaction (Tetra-ARMS PCR) and allele-specific oligonucleotide PCR (ASO-PCR) methods. RESULTS We found a significant difference between mean serum PAB levels and the genetic variant of rs16991615 (P=0.03). ANCOVA showed a significant effect of the genotypes rs4806660 and rs10183486 on hs-CRP serum levels in the case and control groups, respectively (P=0.04 and P=0.007). ANCOVA also showed an association between rs244715 genotypes and anti-hsp27 serum levels in the case group (P=0.02). There was a significant effect of the genotypes of rs451417 on the serum hs-CRP level in the control group (P=0.03). CONCLUSION There was a significant association of the genetic variants related to PM with oxidative stress and inflammatory markers (serum PAB, anti-hsp27 antibody, and hs-CRP). Accordingly, this seems to be an effective approach to predicting susceptible subjects for cardiovascular and mental disorders as well as various cancers.
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Affiliation(s)
- Mohammad Reza Mirinezhad
- Department of Medical Genetics and Molecular Medicine, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Maliheh Aghsizadeh
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
- Student Research Committee, Mashhad University of Medical Sciences, Mashhad, Iran
| | | | - Sara Moazedi
- Department of Nutrition Sciences, Varastegan Institute for Medical Sciences, Mashhad, Iran
| | - Maryam Mohammadi Bajgiran
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hamideh Ghazizadeh
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
- Student Research Committee, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Shayan Yaghouti
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mahdi Mohammadian Ghosooni
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
| | | | - Elahe Hasanzadeh
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Ali Ebrahimi Dabagh
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Arezoo Rastegarmoghadam Ebrahimian
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Ensieh Akbarpour
- Student Research Committee, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Habibollah Esmaily
- Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Gordon A Ferns
- Brighton and Sussex Medical School, Division of Medical Education, Brighton, UK
| | - Tayebeh Hamzehloei
- Department of Medical Genetics and Molecular Medicine, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Alireza Pasdar
- Department of Medical Genetics and Molecular Medicine, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
- Division of Applied Medicine, Medical School, University of Aberdeen, Scatland, UK
- Metabolic Syndrome Research Centre, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Majid Ghayour-Mobarhan
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran.
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Mansoori A, Farizani Gohari NS, Etemad L, Poudineh M, Ahari RK, Mohammadyari F, Azami M, Rad ES, Ferns G, Esmaily H, Ghayour Mobarhan M. White blood cell and platelet distribution widths are associated with hypertension: data mining approaches. Hypertens Res 2024; 47:515-528. [PMID: 37880498 DOI: 10.1038/s41440-023-01472-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 09/23/2023] [Accepted: 09/27/2023] [Indexed: 10/27/2023]
Abstract
In this paper, we are going to investigate the association between Hypertension (HTN) and routine hematologic indices in a cohort of Iranian adults. The data were obtained from a total population of 9704 who were aged 35-65 years, a prospective study was designed. The association between hematologic factors and HTN was assessed using logistic regression (LR) analysis and a decision tree (DT) algorithm. A total of 9704 complete datasets were analyzed in this cohort study (N = 3070 with HTN [female 62.47% and male 37.52%], N = 6634 without HTN [female 58.90% and male 41.09%]). Several variables were significantly different between the two groups, including age, smoking status, BMI, diabetes millitus, high sensitivity C-reactive protein (hs-CRP), uric acid, FBS, total cholesterol, HGB, LYM, WBC, PDW, RDW, RBC, sex, PLT, MCV, SBP, DBP, BUN, and HCT (P < 0.05). For unit odds ratio (OR) interpretation, females are more likely to have HTN (OR = 1.837, 95% CI = (1.620, 2.081)). Among the analyzed variables, age and WBC had the most significant associations with HTN OR = 1.087, 95% CI = (1.081, 1.094) and OR = 1.096, 95% CI = (1.061, 1.133), respectively (P-value < 0.05). In the DT model, age, followed by WBC, sex, and PDW, has the most significant impact on the HTN risk. Ninety-eight percent of patients had HTN in the subgroup with older age (≥58), high PDW (≥17.3), and low RDW (<46). Finally, we found that elevated WBC and PDW are the most associated factor with the severity of HTN in the Mashhad general population as well as female gender and older age.
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Affiliation(s)
- Amin Mansoori
- International UNESCO center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
- Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
- Department of Applied Mathematics, Ferdowsi University of Mashhad, Mashhad, Iran
| | | | - Leila Etemad
- International UNESCO center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mohadeseh Poudineh
- Student of Research Committee, School of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran
| | - Rana Kolahi Ahari
- International UNESCO center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
| | | | - Mobin Azami
- Student of Research Committee, Kurdistan University of Medical Sciences, Sanandaj, Iran
| | - Elias Sadooghi Rad
- Student Research Committee, School of Medicine, Birjand University of Medical sciences, Birjand, Iran
| | - Gordon Ferns
- Brighton and Sussex Medical School, Division of Medical Education, Brighton, United Kingdom
| | - Habibollah Esmaily
- Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran.
- Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
| | - Majid Ghayour Mobarhan
- International UNESCO center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran.
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Jiang L, Yang Z, Wang D, Gong H, Li J, Wang J, Wang L. Diabetes prediction model for unbalanced community follow-up data set based on optimal feature selection and scorecard. Digit Health 2024; 10:20552076241236370. [PMID: 38449681 PMCID: PMC10915850 DOI: 10.1177/20552076241236370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/14/2024] [Indexed: 03/08/2024] Open
Abstract
Objectives Diabetes is a metabolic disease and early detection is crucial to ensuring a healthy life for people with prediabetes. Community care plays an important role in public health, but the association between community follow-up of key life characteristics and diabetes risk remains unclear. Based on the method of optimal feature selection and risk scorecard, follow-up data of diabetes patients are modeled to assess diabetes risk. Methods We conducted a study on the diabetes risk assessment model and risk scorecard using follow-up data from diabetes patients in Haizhu District, Guangzhou, from 2016 to 2023. The raw data underwent preprocessing and imbalance handling. Subsequently, features relevant to diabetes were selected and optimized to determine the optimal subset of features associated with community follow-up and diabetes risk. We established the diabetes risk assessment model. Furthermore, for a comprehensible and interpretable risk expression, the Weight of Evidence transformation method was applied to features. The transformed features were discretized using the quantile binning method to design the risk scorecard, mapping the model's output to five risk levels. Results In constructing the diabetes risk assessment model, the Random Forest classifier achieved the highest accuracy. The risk scorecard obtained an accuracy of 85.16%, precision of 87.30%, recall of 80.26%, and an F1 score of 83.27% on the unbalanced research dataset. The performance loss compared to the diabetes risk assessment model was minimal, suggesting that the binning method used for constructing the diabetes risk scorecard is reasonable, with very low feature information loss. Conclusion The methods provided in this article demonstrate effectiveness and reliability in the assessment of diabetes risk. The assessment model and scorecard can be directly applied to community doctors for large-scale risk identification and early warning and can also be used for individual self-examination to reduce risk factor levels.
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Affiliation(s)
- Liangjun Jiang
- College of Information and Communication Engineering, State Key Lab of Marine Resource Utilisation in South China Sea, Hainan University, Haikou, China
| | - Zerui Yang
- Electronics & Information School, Yangtze University, Jingzhou, China
| | - Donghai Wang
- Shenzhen Center for Disease Control and Prevention, Shenzhen, China
| | - Haimei Gong
- College of Information and Communication Engineering, State Key Lab of Marine Resource Utilisation in South China Sea, Hainan University, Haikou, China
| | - Juan Li
- Haizhu District Community Health Development Guidance Center, Guangzhou, China
| | - Jing Wang
- Shenzhen E-link Wisdom Co., Ltd, Shenzhen, China
| | - Lei Wang
- College of Information and Communication Engineering, State Key Lab of Marine Resource Utilisation in South China Sea, Hainan University, Haikou, China
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Mansoori A, Hosseini N, Ghazizadeh H, Aghasizadeh M, Drroudi S, Sahranavard T, Izadi HS, Amiriani A, Farkhani EM, Ferns GA, Ghayour-Mobarhan M, Moohebati M, Esmaily H. Association between biochemical and hematologic factors with COVID-19 using data mining methods. BMC Infect Dis 2023; 23:897. [PMID: 38129798 PMCID: PMC10734144 DOI: 10.1186/s12879-023-08676-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 10/06/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND AND AIM Coronavirus disease (COVID-19) is an infectious disease that can spread very rapidly with important public health impacts. The prediction of the important factors related to the patient's infectious diseases is helpful to health care workers. The aim of this research was to select the critical feature of the relationship between demographic, biochemical, and hematological characteristics, in patients with and without COVID-19 infection. METHOD A total of 13,170 participants in the age range of 35-65 years were recruited. Decision Tree (DT), Logistic Regression (LR), and Bootstrap Forest (BF) techniques were fitted into data. Three models were considered in this study, in model I, the biochemical features, in model II, the hematological features, and in model II, both biochemical and homological features were studied. RESULTS In Model I, the BF, DT, and LR algorithms identified creatine phosphokinase (CPK), blood urea nitrogen (BUN), fasting blood glucose (FBG), total bilirubin, body mass index (BMI), sex, and age, as important predictors for COVID-19. In Model II, our BF, DT, and LR algorithms identified BMI, sex, mean platelet volume (MPV), and age as important predictors. In Model III, our BF, DT, and LR algorithms identified CPK, BMI, MPV, BUN, FBG, sex, creatinine (Cr), age, and total bilirubin as important predictors. CONCLUSION The proposed BF, DT, and LR models appear to be able to predict and classify infected and non-infected people based on CPK, BUN, BMI, MPV, FBG, Sex, Cr, and Age which had a high association with COVID-19.
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Affiliation(s)
- Amin Mansoori
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
- Department of Applied Mathematics, Ferdowsi University of Mashhad, Mashhad, Iran
- Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Nafiseh Hosseini
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
- Faculty of Medicine, Islamic Azad University of Mashhad, Mashhad, Iran
| | - Hamideh Ghazizadeh
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
- Division of Clinical Biochemistry, CALIPER Program, Pediatric Laboratory Medicine, the Hospital for Sick Children, Toronto, ON, Canada
| | - Malihe Aghasizadeh
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Susan Drroudi
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Toktam Sahranavard
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hanie Salmani Izadi
- Student Research Committee, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Amirhossein Amiriani
- Student Research Committee, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Ehsan Mosa Farkhani
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Gordon A Ferns
- Brighton & Sussex Medical School, Division of Medical Education, Falmer, Brighton, BN1 9PH, Sussex, UK
| | - Majid Ghayour-Mobarhan
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mohsen Moohebati
- Cardiovascular Research Center, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
| | - Habibollah Esmaily
- Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran.
- Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
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10
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Das A, Dhillon P. Application of machine learning in measurement of ageing and geriatric diseases: a systematic review. BMC Geriatr 2023; 23:841. [PMID: 38087195 PMCID: PMC10717316 DOI: 10.1186/s12877-023-04477-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 11/10/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND As the ageing population continues to grow in many countries, the prevalence of geriatric diseases is on the rise. In response, healthcare providers are exploring novel methods to enhance the quality of life for the elderly. Over the last decade, there has been a remarkable surge in the use of machine learning in geriatric diseases and care. Machine learning has emerged as a promising tool for the diagnosis, treatment, and management of these conditions. Hence, our study aims to find out the present state of research in geriatrics and the application of machine learning methods in this area. METHODS This systematic review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and focused on healthy ageing in individuals aged 45 and above, with a specific emphasis on the diseases that commonly occur during this process. The study mainly focused on three areas, that are machine learning, the geriatric population, and diseases. Peer-reviewed articles were searched in the PubMed and Scopus databases with inclusion criteria of population above 45 years, must have used machine learning methods, and availability of full text. To assess the quality of the studies, Joanna Briggs Institute's (JBI) critical appraisal tool was used. RESULTS A total of 70 papers were selected from the 120 identified papers after going through title screening, abstract screening, and reference search. Limited research is available on predicting biological or brain age using deep learning and different supervised machine learning methods. Neurodegenerative disorders were found to be the most researched disease, in which Alzheimer's disease was focused the most. Among non-communicable diseases, diabetes mellitus, hypertension, cancer, kidney diseases, and cardiovascular diseases were included, and other rare diseases like oral health-related diseases and bone diseases were also explored in some papers. In terms of the application of machine learning, risk prediction was the most common approach. Half of the studies have used supervised machine learning algorithms, among which logistic regression, random forest, XG Boost were frequently used methods. These machine learning methods were applied to a variety of datasets including population-based surveys, hospital records, and digitally traced data. CONCLUSION The review identified a wide range of studies that employed machine learning algorithms to analyse various diseases and datasets. While the application of machine learning in geriatrics and care has been well-explored, there is still room for future development, particularly in validating models across diverse populations and utilizing personalized digital datasets for customized patient-centric care in older populations. Further, we suggest a scope of Machine Learning in generating comparable ageing indices such as successful ageing index.
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Affiliation(s)
- Ayushi Das
- International Institute for Population Sciences, Deonar, Mumbai, 400088, India
| | - Preeti Dhillon
- Department of Survey Research and Data Analytics, International Institute for Population Sciences, Deonar, Mumbai, 400088, India.
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Kolahi Ahari R, Mansoori A, Sahranavard T, Miri MS, Feizi S, Esmaily H, Ghayour‐Mobarhan M. Serum uric acid to high-density lipoprotein ratio as a novel indicator of inflammation is correlated with the presence and severity of metabolic syndrome: A large-scale study. Endocrinol Diabetes Metab 2023; 6:e446. [PMID: 37605374 PMCID: PMC10638626 DOI: 10.1002/edm2.446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 07/31/2023] [Accepted: 08/04/2023] [Indexed: 08/23/2023] Open
Abstract
INTRODUCTION We investigated the association of serum uric acid to high-density lipoprotein ratio (UHR) with the presence and severity of metabolic syndrome (MetS) among MASHAD cohort participants. METHODS In this cross-sectional study, according to International Diabetes Federation criteria, the cohort participants were divided into MetS (+) and MetS (-) groups. MetS (+) were classified into Group 1 (those with 3 MetS criteria), Group 2 (those with 4 MetS criteria) and Group 3 (those with 5 MetS criteria). UHR was compared among the groups. RESULTS Data related to 9637 subjects including 3824 MetS (+) and 5813 MetS (-) were analysed. The mean UHR was significantly higher (p < .001) in the MetS (+) group compared with the MetS (-) group. UHR increased as the MetS severity increased (p < .001). ROC analysis revealed that UHR greater than 9.5% has 89.07% sensitivity and 77.03% specificity in differentiating MetS (-) from MetS (+) subjects. CONCLUSION Among MASHAD cohort study participants, a significant association between UHR and MetS was found. Furthermore, there is an increase in UHR as the severity of MetS increases. Registration number of MASHAD cohort study: 85134.
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Affiliation(s)
- Rana Kolahi Ahari
- International UNESCO Center for Health‐Related Basic Sciences and Human NutritionMashhad University of Medical SciencesMashhadIran
| | - Amin Mansoori
- International UNESCO Center for Health‐Related Basic Sciences and Human NutritionMashhad University of Medical SciencesMashhadIran
- Department of Biostatistics, School of HealthMashhad University of Medical SciencesMashhadIran
| | - Toktam Sahranavard
- International UNESCO Center for Health‐Related Basic Sciences and Human NutritionMashhad University of Medical SciencesMashhadIran
| | - Monireh Sadat Miri
- Department of Biology, Faculty of Sciences, Mashhad BranchIslamic Azad UniversityMashhadIran
| | - Sara Feizi
- Department of Biology, Faculty of Sciences, Mashhad BranchIslamic Azad UniversityMashhadIran
| | - Habibollah Esmaily
- Department of Biostatistics, School of HealthMashhad University of Medical SciencesMashhadIran
| | - Majid Ghayour‐Mobarhan
- International UNESCO Center for Health‐Related Basic Sciences and Human NutritionMashhad University of Medical SciencesMashhadIran
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12
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Poudineh M, Mansoori A, Sadooghi Rad E, Hosseini ZS, Salmani Izadi F, Hoseinpour M, Mahmoudi Zo M, Ghoflchi S, Tanbakuchi D, Nazar E, Ferns G, Effati S, Esmaily H, Ghayour-Mobarhan M. Platelet distribution widths and white blood cell are associated with cardiovascular diseases: data mining approaches. Acta Cardiol 2023; 78:1033-1044. [PMID: 37694924 DOI: 10.1080/00015385.2023.2246199] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 06/12/2023] [Accepted: 08/03/2023] [Indexed: 09/12/2023]
Abstract
OBJECTIVE To investigate the association between cardiovascular diseases (CVDs) and haematologic factors in a cohort of Iranian adults. METHOD For a total population of 9,704 aged 35 to 65, a prospective study was designed. Haematologic factors and demographic characteristics (such as gender, age, and smoking status) were completed for all participants. The association between haematologic factors and CVDs was assessed through logistic regression (LR) analysis, decision tree (DT), and bootstrap forest (BF). RESULTS Almost all of the included factors were significantly associated with CVD (p<.001). Among the included factors, were: age, white blood cell (WBC), and platelet distribution width (PDW) had the strongest correlation with the development of CVD. For unit OR interpretation, WBC has been represented as the most remarkable risk factor for CVD (OR: 1.22 (CI 95% (1.18, 1.27))). Also, age is associated with an increase in the odds of CVD + occurrence (OR: 1.12 (CI 95% (1.11, 1.13))). Moreover, males are times more likely to develop CVD than females (OR: 1.39 (CI 95% (1.22, 1.58))). In DT model, age is the best classifier factor in CVD development, followed by WBC and PDW. Furthermore, based on the BF algorithm, the most crucial factors correlated with CVD are age, WBC, PDW, sex, and smoking status. CONCLUSION The obtained result from LR, DT, and BF models confirmed that age, WBC, and PDW are the most crucial factors for the development of CVD.
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Affiliation(s)
- Mohadeseh Poudineh
- Student Research Committee, School of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran
| | - Amin Mansoori
- International UNESCO center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
- Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Elias Sadooghi Rad
- Student Research Committee, School of Medicine, Birjand University of Medical Sciences, Birjand, Iran
| | | | - Faezeh Salmani Izadi
- Student Research Committee, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mahdieh Hoseinpour
- Student Research Committee, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mostafa Mahmoudi Zo
- Student Research Committee, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Sahar Ghoflchi
- International UNESCO center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Davoud Tanbakuchi
- Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Eisa Nazar
- International UNESCO center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
- Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
- Department of Applied Mathematics, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Gordon Ferns
- Brighton and Sussex Medical School, Division of Medical Education, Brighton, United Kingdom
| | - Sohrab Effati
- Department of Applied Mathematics, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Habibollah Esmaily
- Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
- Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Majid Ghayour-Mobarhan
- International UNESCO center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
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Miller DM, Yadanapudi K, Rai V, Rai SN, Chen J, Frieboes HB, Masters A, McCallum A, Williams BJ. Untangling the web of glioblastoma treatment resistance using a multi-omic and multidisciplinary approach. Am J Med Sci 2023; 366:185-198. [PMID: 37330006 DOI: 10.1016/j.amjms.2023.06.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 05/01/2023] [Accepted: 06/13/2023] [Indexed: 06/19/2023]
Abstract
Glioblastoma (GBM), the most common human brain tumor, has been notoriously resistant to treatment. As a result, the dismal overall survival of GBM patients has not changed over the past three decades. GBM has been stubbornly resistant to checkpoint inhibitor immunotherapies, which have been remarkably effective in the treatment of other tumors. It is clear that GBM resistance to therapy is multifactorial. Although therapeutic transport into brain tumors is inhibited by the blood brain barrier, there is evolving evidence that overcoming this barrier is not the predominant factor. GBMs generally have a low mutation burden, exist in an immunosuppressed environment and they are inherently resistant to immune stimulation, all of which contribute to treatment resistance. In this review, we evaluate the contribution of multi-omic approaches (genomic and metabolomic) along with analyzing immune cell populations and tumor biophysical characteristics to better understand and overcome GBM multifactorial resistance to treatment.
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Affiliation(s)
- Donald M Miller
- Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Medicine, School of Medicine, University of Louisville, Louisville, KY, USA.
| | - Kavitha Yadanapudi
- Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Medicine, School of Medicine, University of Louisville, Louisville, KY, USA
| | - Veeresh Rai
- Brown Cancer Center, University of Louisville, Louisville, KY, USA
| | - Shesh N Rai
- Brown Cancer Center, University of Louisville, Louisville, KY, USA; Biostatistics and Informatics Shared Resources, University of Cincinnati Cancer Center, Cincinnati, OH, USA; Cancer Data Science Center of University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Joseph Chen
- Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Bioengineering, Speed School of Engineering, University of Louisville, Louisville, KY, USA
| | - Hermann B Frieboes
- Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Bioengineering, Speed School of Engineering, University of Louisville, Louisville, KY, USA; Center for Preventative Medicine, University of Louisville, Louisville, KY, USA
| | - Adrianna Masters
- Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Radiation Oncology, University of Louisville, Louisville, KY, USA
| | - Abigail McCallum
- Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Neurosurgery, University of Louisville, Louisville, KY, USA
| | - Brian J Williams
- Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Neurosurgery, University of Louisville, Louisville, KY, USA
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Talkhi N, Nooghabi MJ, Esmaily H, Maleki S, Hajipoor M, Ferns GA, Ghayour-Mobarhan M. Prediction of serum anti-HSP27 antibody titers changes using a light gradient boosting machine (LightGBM) technique. Sci Rep 2023; 13:12775. [PMID: 37550399 PMCID: PMC10406940 DOI: 10.1038/s41598-023-39724-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 07/29/2023] [Indexed: 08/09/2023] Open
Abstract
Previous studies have proposed that heat shock proteins 27 (HSP27) and its anti-HSP27 antibody titers may play a crucial role in several diseases including cardiovascular disease. However, available studies has been used simple analytical methods. This study aimed to determine the factors that associate serum anti-HSP27 antibody titers using ensemble machine learning methods and to demonstrate the magnitude and direction of the predictors using PFI and SHAP methods. The study employed Python 3 to apply various machine learning models, including LightGBM, CatBoost, XGBoost, AdaBoost, SVR, MLP, and MLR. The best models were selected using model evaluation metrics during the K-Fold cross-validation strategy. The LightGBM model (with RMSE: 0.1900 ± 0.0124; MAE: 0.1471 ± 0.0044; MAPE: 0.8027 ± 0.064 as the mean ± sd) and the SHAP method revealed that several factors, including pro-oxidant-antioxidant balance (PAB), physical activity level (PAL), platelet distribution width, mid-upper arm circumference, systolic blood pressure, age, red cell distribution width, waist-to-hip ratio, neutrophils to lymphocytes ratio, platelet count, serum glucose, serum cholesterol, red blood cells were associated with anti-HSP27, respectively. The study found that PAB and PAL were strongly associated with serum anti-HSP27 antibody titers, indicating a direct and indirect relationship, respectively. These findings can help improve our understanding of the factors that determine anti-HSP27 antibody titers and their potential role in disease development.
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Affiliation(s)
- Nasrin Talkhi
- Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mehdi Jabbari Nooghabi
- Department of Statistics, Ferdowsi University of Mashhad, Mashhad, Iran
- Department of Mathematical Sciences, University of Copenhagen, 2100, Copenhagen, Denmark
| | - Habibollah Esmaily
- Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
- Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Saba Maleki
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mojtaba Hajipoor
- Department of Nutrition Sciences, Varastegan Institute for Medical Sciences, Mashhad, Iran
| | - Gordon A Ferns
- Division of Medical Education, Brighton & Sussex Medical School, Falmer, Brighton, BN1 9PH, Sussex, UK
| | - Majid Ghayour-Mobarhan
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran.
- Metabolic Syndrome Research Center, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
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Ghazizadeh H, Shakour N, Ghoflchi S, Mansoori A, Saberi-Karimiam M, Rashidmayvan M, Ferns G, Esmaily H, Ghayour-Mobarhan M. Use of data mining approaches to explore the association between type 2 diabetes mellitus with SARS-CoV-2. BMC Pulm Med 2023; 23:203. [PMID: 37308948 DOI: 10.1186/s12890-023-02495-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 05/25/2023] [Indexed: 06/14/2023] Open
Abstract
BACKGROUND AND OBJECTIVE Corona virus causes respiratory tract infections in mammals. The latest type of Severe Acute Respiratory Syndrome Corona-viruses 2 (SARS-CoV-2), Corona virus spread in humans in December 2019 in Wuhan, China. The purpose of this study was to investigate the relationship between type 2 diabetes mellitus (T2DM), and their biochemical and hematological factors with the level of infection with COVID-19 to improve the treatment and management of the disease. MATERIAL AND METHOD This study was conducted on a population of 13,170 including 5780 subjects with SARS-COV-2 and 7390 subjects without SARS-COV-2, in the age range of 35-65 years. Also, the associations between biochemical factors, hematological factors, physical activity level (PAL), age, sex, and smoking status were investigated with the COVID-19 infection. RESULT Data mining techniques such as logistic regression (LR) and decision tree (DT) algorithms were used to analyze the data. The results using the LR model showed that in biochemical factors (Model I) creatine phosphokinase (CPK) (OR: 1.006 CI 95% (1.006,1.007)), blood urea nitrogen (BUN) (OR: 1.039 CI 95% (1.033, 1.047)) and in hematological factors (Model II) mean platelet volume (MVP) (OR: 1.546 CI 95% (1.470, 1.628)) were significant factors associated with COVID-19 infection. Using the DT model, CPK, BUN, and MPV were the most important variables. Also, after adjustment for confounding factors, subjects with T2DM had higher risk for COVID-19 infection. CONCLUSION There was a significant association between CPK, BUN, MPV and T2DM with COVID-19 infection and T2DM appears to be important in the development of COVID-19 infection.
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Affiliation(s)
- Hamideh Ghazizadeh
- The Hospital for Sick Children, CALIPER Program, Division of Clinical Biochemistry, Pediatric Laboratory Medicine, Toronto, ON, Canada
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Neda Shakour
- Department of Medical Chemistry, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Sahar Ghoflchi
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Amin Mansoori
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran.
- Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran.
| | - Maryam Saberi-Karimiam
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mohammad Rashidmayvan
- Department of Nutrition, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
- Department of Nutrition, Food Sciences and Clinical Biochemistry, School of Medicine, Social Determinants of Health Research Center, Gonabad University of Medical Sciences, Gonabad, Iran
| | - Gordon Ferns
- Division of Medical Education, Brighton and Sussex Medical School, Brighton, UK
| | - Habibollah Esmaily
- Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
- Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Majid Ghayour-Mobarhan
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran.
- Department of Nutrition, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
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Fardi F, Bahari Khasraghi L, Shahbakhti N, Salami Naseriyan A, Najafi S, Sanaaee S, Alipourfard I, Zamany M, Karamipour S, Jahani M, Majidpoor J, Kalhor K, Talebi M, Mohsen Aghaei-Zarch S. An interplay between non-coding RNAs and gut microbiota in human health. Diabetes Res Clin Pract 2023:110739. [PMID: 37270071 DOI: 10.1016/j.diabres.2023.110739] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 04/22/2023] [Accepted: 05/30/2023] [Indexed: 06/05/2023]
Abstract
Humans have a complicated symbiotic relationship with their gut microbiome, which is postulated to impact host health and disease broadly. Epigenetic alterations allow host cells to regulate gene expression without altering the DNA sequence. The gut microbiome, offering environmental hints, can influence responses to stimuli by host cells with modifications on their epigenome and gene expression. Recent increasing data suggest that regulatory non-coding RNAs (miRNAs, circular RNAs, and long lncRNA) may affect host-microbe interactions. These RNAs have been suggested as potential host response biomarkers in microbiome-associated disorders, including diabetes and cancer. This article reviews the current understanding of the interplay between gut microbiota and non-coding RNA, including lncRNA, miRNA, and circular RNA. This can lead to a profound understanding of human disease and influence therapy. Furthermore, microbiome engineering as a mainstream strategy for improving human health has been discussed and confirms the hypothesis about a direct cross-talk between microbiome composition and non-coding RNA.
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Affiliation(s)
- Fatemeh Fardi
- Department of Microbiology, Faculty of Biological Sciences, Islamic Azad University, Kish international, Kish, Iran
| | - Leila Bahari Khasraghi
- 15 Khordad Educational Hospital, Shahid Beheshti University of Medical Science, Tehran, Iran
| | - Negin Shahbakhti
- Department of biology, Faculty of Zoology, University of Razi, Kermanshah, Iran
| | - Amir Salami Naseriyan
- Department of Microbial Biotechnology, Islamic Azad University, Varamin-Pishva Branch, Tabriz, Iran
| | - Sajad Najafi
- Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Saameh Sanaaee
- Department of New Science, Faculty of Cellular and Molecular biology, Islamic Azad University, Tehran Medical Branch, Tehran, Iran
| | - Iraj Alipourfard
- Institute of Physical Chemistry, Polish Academy of Sciences, Warsaw, Poland.
| | - Marzieh Zamany
- Shahid Akbarabadi Clinical Research Development Unit, Iran University of medical Science, Tehran, Iran
| | - Saman Karamipour
- Department of Genetics and Molecular biology, Faculty of Medicine, Iran University of Medical science, Tehran, Iran
| | - Mehdi Jahani
- Department of Medical Genetics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Jamal Majidpoor
- Department of Anatomy, Faculty of Medicine, Infectious Disease Research Center, Gonabad University of Medical Sciences, Gonabad, Iran
| | - Kambiz Kalhor
- Department of Earth and Planetary Sciences, University of Tennessee, Knoxville, USA.
| | - Mehrdad Talebi
- Department of Medical Genetics, School of Medicine, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
| | - Seyed Mohsen Aghaei-Zarch
- Department of Genetics and Molecular biology, Faculty of Medicine, Iran University of Medical science, Tehran, Iran.
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Mansoori A, Hosseini ZS, Ahari RK, Poudineh M, Rad ES, Zo MM, Izadi FS, Hoseinpour M, Miralizadeh A, Mashhadi YA, Hormozi M, Firoozeh MT, Hajhoseini O, Ferns G, Esmaily H, Mobarhan MG. Development of Data Mining Algorithms for Identifying the Best Anthropometric Predictors for Cardiovascular Disease: MASHAD Cohort Study. High Blood Press Cardiovasc Prev 2023; 30:243-253. [PMID: 37204657 DOI: 10.1007/s40292-023-00577-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 04/25/2023] [Indexed: 05/20/2023] Open
Abstract
INTRODUCTION Many studies have been published to assess the best anthropometric measurements associated with cardiovascular diseases (CVDs), but controversies still exist. AIM Investigating the association between CVDs and anthropometric measurements among Iranian adults. METHODS For a total population of 9354 aged 35 to 65, a prospective study was designed. Anthropometric measurements including ABSI (A Body Shape Index), Body Adiposity Index (BAI), Body Mass Index (BMI), Waist to Height Ratio (WHtR), Body Round Index (BRI), HC (Hip Circumference), Demispan, Mid-arm circumference (MAC), Waist-to-hip (WH) and Waist Circumference (WC) were completed. The association between these parameters and CVDs were assessed through logistic regression (LR) and decision tree (DT) models. RESULTS During the 6-year follow-up, 4596 individuals (49%) developed CVDs. According to the LR, age, BAI, BMI, Demispan, and BRI, in male and age, WC, BMI, and BAI in female had a significant association with CVDs (p-value < 0.03). Age and BRI for male and age and BMI for female represent the most appropriate estimates for CVDs (OR: 1.07, (95% CI: 1.06, 1.08), 1.36 (1.22, 1.51), 1.14 (1.13, 1.15), and 1.05 (1.02, 1.07), respectively). In the DT for male, those with BRI ≥ 3.87, age ≥ 46 years, and BMI ≥ 35.97 had the highest risk to develop CVDs (90%). Also, in the DT for female, those with age ≥ 54 years and WC ≥ 84 had the highest risk to develop CVDs (71%). CONCLUSION BRI and age in male and age and BMI in female had the greatest association with CVDs. Also, BRI and BMI was the strongest indices for this prediction.
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Affiliation(s)
- Amin Mansoori
- International UNESCO center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, 99199-91766, Iran
- Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
| | | | - Rana Kolahi Ahari
- International UNESCO center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, 99199-91766, Iran
| | - Mohadeseh Poudineh
- Student Research committee, School of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran
| | - Elias Sadooghi Rad
- Student Research Committee, School of Medicine, Birjand University of Medical Sciences, Birjand, Iran
| | - Mostafa Mahmoudi Zo
- Student Research Committee, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Faezeh Salmani Izadi
- Student Research Committee, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mahdieh Hoseinpour
- Student Research Committee, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Amirreza Miralizadeh
- Student Research Committee, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | | | - Maryam Hormozi
- Department of Biology, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | | | - Omolbanin Hajhoseini
- Student Research Committee, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
- Department of Nutrition, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Gordon Ferns
- Division of Medical Education, Brighton and Sussex Medical School, Brighton, United Kingdom
| | - Habibollah Esmaily
- Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran.
- Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
| | - Majid Ghayour Mobarhan
- International UNESCO center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, 99199-91766, Iran.
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