Mean Corpuscular Volume and Mortality in Patients with CKD

Hsieh, Yih Shou; Chang, Chia‐Chu; Kor, Chew‐Teng; Yang, Yu; Wen, Yao-Ko; Chiu, Ping‐Fang

doi:10.2215/cjn.00970116

Cited by 48 publications

(49 citation statements)

References 28 publications

(32 reference statements)

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“…Increasing such variables of the red component of blood cells decreases the sperm quantity and quality, although a clear cause‐effect relationship could not be established. This is in line with previous studies detecting an impairment in human health status when erythrocyte distribution 31‐35 and the average globular volume increase, 36‐38 suggesting that sperm parameters are potential health markers 7 …”

Section: Discussionsupporting

confidence: 92%

“…Two parameters of impaired human health, erythrocyte distribution 31‐35 and average globular volume, 36‐38 were inversely related to total sperm number (adjusted R‐squared: 0.002; coefficient: −4.109 and −0.582, P < .010 and P < .050, respectively) and concentration (adjusted R ‐squared: 0.004; coefficient: −17.022 and −2.229, P < .001 and P < .001, respectively). Moreover, these two blood parameters were inversely related to total sperm motility (adjusted R ‐squared: 0.008; coefficient: −2.658 and −0.237, respectively; P < .001), and directly related to immobility sperm rate (adjusted R ‐squared: 0.008; coefficient: 2.653 and 0.237, P < .010 and P < .050, respectively).…”

Section: Resultsmentioning

confidence: 98%

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Multilevel approach to male fertility by machine learning highlights a hidden link between haematological and spermatogenetic cells

Santi

Spaggiari

Casonati³

et al. 2020

Andrology

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Background Male infertility represents a complex clinical condition requiring an accurate multilevel assessment, in which machine learning technology, combining large data series in non‐linear and highly interactive ways, could be innovatively applied. Methods A longitudinal, observational, retrospective, big data study was carried out, applying for the first time the ML in the context of male infertility. A large database including all semen samples collected between 2010 and 2016 was generated, together with blood biochemical examinations, environmental temperature and air pollutants exposure. First, the database was analysed with principal component analysis and multivariable linear regression analyses. Second, classification analyses were performed, in which patients were a priori classified according to semen parameters. Third, machine learning algorithms were applied in a training phase (80% of the entire database) and in a tuning phase (20% of the data set). Finally, conventional statistical analyses were applied considering semen parameters and those other variables extracted during machine learning. Results The final database included 4239 patients, aggregating semen analyses, blood and environmental parameters. Classification analyses were able to recognize oligozoospermic, teratozoospermic, asthenozoospermic and patients with altered semen parameters (0.58 accuracy, 0.58 sensitivity and 0.57 specificity). Machine learning algorithms detected three haematological variables, that is lymphocytes number, erythrocyte distribution and mean globular volume, significantly related to semen parameters (0.69 accuracy, 0.78 sensitivity and 0.41 specificity). Conclusion This is the first machine learning application to male fertility, detecting potential mathematical algorithms able to describe patients' semen characteristics changes. In this setting, a possible hidden link between testicular and haematopoietic tissues was suggested, according to their similar proliferative properties.

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Section: Discussionsupporting

confidence: 92%

Section: Resultsmentioning

confidence: 98%

Multilevel approach to male fertility by machine learning highlights a hidden link between haematological and spermatogenetic cells

Santi

Spaggiari

Casonati³

et al. 2020

Andrology

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“…Elevated MCV (generally > 100 fL) is often characteristic of underlying conditions such as nutritional deficiencies, drug and alcohol use [7], vitamin B12 deficiency [8], certain medications or bone marrow disorders [9]. In patients with ≥ 3 chronic kidney disease (CKD) stages, MCV was associated with all-cause mortality, cardiovascular disease mortality, and infectionassociated mortality [10]. Other authors [11] applied Cox regression analysis MCV to predict composite cardiovascular (CV) events in CKD patient as a major confounding factor.…”

Section: Introductionmentioning

confidence: 99%

Selected hematological biomarkers to predict acute mortality in emergency department patients: Recent Polish hospital statistics

Brzeźniakiewicz‐Janus

Lancé

Tukiendorf

et al. 2020

Preprint

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Background Complete blood count (CBC): red cell distribution width (RDW), mean platelet volume (MPV), mean corpuscular volume (MCV), mean cell hemoglobin (MCH), mean cell hemoglobin concentration (MCHC), or platelet (PLT) count are referred as predictors of adverse clinical outcomes in patients. The aim of the research was to identify potential factors of acute mortality in Polish ED patients by using selected hematological biomarkers and routine statistical tools. Methods The study presents statistical results on patients who were recently discharged from inpatient facilities within one month prior to the index emergency department (ED) visit. In total, the analysis comprised 14,904 patients with the first RDW, MPV, MCV, MCH, MCHC, or PLT biomarkers’ measurements recorded in the Emergency Department within the years 2016–2019 with a subsequent one month of all-cause mortality observation. The patients were classified with the codes of the International Statistical Classification of Diseases and Related Health Problems after 10th Revision (ICD10). Results Based on the analysis of RDW, MPV, MCV, MCH, MCHC, and PLT on acute deaths in patients, we establish strong linear and quadratic relationships between the risk factors under study and the clinical response (P < 0.05), however, with different mortality courses and threats. In our statistical analysis, (1) gradient linear relationships were found for RDW and MPV along an entire range of the analyzed biomarkers’ measurements, (2) following the quadratic modeling, an increasing risk of death above 95 fL was determined for MCV, and (3) no relation to excess death in ED patients was calculated for MCH, MCHC and PLT. Conclusion The study shows that there are likely relationships between blood counts and expected patient mortality at some time interval from measurements. Up to 1 month of observation since the first measurement of an hematological biomarker, RDW and MPV stand for a strong relationship with acute mortality of patients, whereas MCV, MCH, MCHC, and PLT give the U-shaped association, RDW and MPV can be established as the stronger predictors of early deaths of patients, MCV only in the highest levels (> 95 fL), whereas MCH, MCHC, and PLT have no impact on the excess acute mortality in ED patients.

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“…In addition, Braekkan et al 11 that hematocrit was an independent risk factor for hypertension in the general population. In addition, previous studies have shown that high mean corpuscular volume (MCV) is a prognostic indicator for acute decompensated heart failure 13 and that it is associated with all-cause mortality, cardiovascular disease mortality, and infectionassociated mortality in patients with chronic kidney disease stages 3-5, 14 and that mean corpuscular hemoglobin (MCH) is a predictor of iron deficiency. 15 Although numerous studies have indicated that lifestyle factors such as smoking, alcohol consumption, shift work, and exercise may exert an important effect on WBC count, [16][17][18][19] little is known about the association between hematological parameters and lifestyle factors.…”

Section: Introductionmentioning

confidence: 99%

Association between lifestyle and hematological parameters: A study of Chinese male steelworkers

Jean

Hsu

Hung

et al. 2019

Clinical Laboratory Analysis

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BackgroundIncreasing evidence suggests an association between lifestyle and white blood cell (WBC) count; however, no study has examined the effects of lifestyle associations on hematological parameters. The aim of this study was to examine the association between lifestyle factors and hematological parameters in a large population‐based sample of Chinese male steelworkers.MethodsThis study included 3189 male workers at a steel plant who responded to a cross‐sectional questionnaire on basic attributes, lifestyle, and sleep. All workers in the plant underwent periodic health checkups. Hematological parameters were also examined at the checkup.ResultsStepwise linear regression analyses showed that smoking, poor sleep, shift work, and obesity were all significant factors associated with WBC count. Obesity was independently associated with RBC count. Furthermore, smoking and obesity were associated with hemoglobin, and smoking, poor sleep, and obesity were independently associated with hematocrit. Moreover, smoking was the main factor associated with MCV and MCH. When the subjects were divided into quartiles according to WBC count, RBC count, hemoglobin, hematocrit, MCV, MCH, and increased WBC count were associated with smoking, poor sleep, shift work, and obesity. Increased hemoglobin was associated with smoking and obesity. Furthermore, an increased RBC count was associated with obesity, and increased hematocrit was associated with smoking, poor sleep, and obesity. Similarly, increased MCV and MCH were also associated with smoking.ConclusionThis study indicates that lifestyle factors may exert an important effect on hematological parameters (eg, WBC count, RBC count, hemoglobin, hematocrit, MCV, and MCH).

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Mean Corpuscular Volume and Mortality in Patients with CKD

Abstract: In patients with stages 3-5 CKD, mean corpuscular volume was associated with all-cause mortality, cardiovascular disease mortality, and infection-associated mortality, independent of other factors. The underlying pathophysiologic mechanisms warrant additional investigation.

Cited by 48 publications

References 28 publications

Multilevel approach to male fertility by machine learning highlights a hidden link between haematological and spermatogenetic cells

Multilevel approach to male fertility by machine learning highlights a hidden link between haematological and spermatogenetic cells

Selected hematological biomarkers to predict acute mortality in emergency department patients: Recent Polish hospital statistics

Association between lifestyle and hematological parameters: A study of Chinese male steelworkers

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