Research article

Hepcidin and iron metabolism in preterm infants

  • Received: 16 December 2022 Revised: 15 March 2023 Accepted: 30 March 2023 Published: 21 April 2023
  • Background 

    Iron deficiency (ID) and ID anemia are widespread in low-income countries, particularly among preterm infants. Hepcidin is a key regulator of iron metabolism, which offers the possibility of new solutions to diagnose ID in premature infants.

    Objective 

    To explore the relationship between iron metabolism and hepcidin in premature infants.

    Materials and methods 

    The study involved 81 preterm infants between 28+1 and 36+6 who underwent iron status indicators and hepcidin testing at 6 months of corrected gestational age. The preterm infants were divided into two groups based on iron status indicators: ID and no ID.

    Results 

    Serum hepcidin was lower for premature infants with ID compared to those without ID (log10hepcidin, 1.18 ± 0.44 vs 1.49 ± 0.37, p = 0.002). A single-variate linear regression model was used to explore the correlation between hepcidin and other indicators of iron metabolism. A strongly positive relationship was observed between hepcidin levels and ferritin levels (p < 0.001) in the correlation analysis.

    Conclusions 

    Hepcidin can be used as an efficient indicator of iron storage and a promising indicator for the early diagnosis of ID in premature infants.

    Citation: Sufeng Ruan, Sufei Yang, Jinrong Li, Fei Xiong, Di Qie, You Lu, Zhanghui Tang, Fan Yang. Hepcidin and iron metabolism in preterm infants[J]. AIMS Molecular Science, 2023, 10(2): 99-108. doi: 10.3934/molsci.2023008

    Related Papers:

    [1] Qianqian Zhang, Mingye Mu, Heyuan Ji, Qiushi Wang, Xingyu Wang . An adaptive type-2 fuzzy sliding mode tracking controller for a robotic manipulator. Electronic Research Archive, 2023, 31(7): 3791-3813. doi: 10.3934/era.2023193
    [2] Lingling Zhang . Vibration analysis and multi-state feedback control of maglev vehicle-guideway coupling system. Electronic Research Archive, 2022, 30(10): 3887-3901. doi: 10.3934/era.2022198
    [3] Duhui Chang, Yan Geng . Distributed data-driven iterative learning control for multi-agent systems with unknown input-output coupled parameters. Electronic Research Archive, 2025, 33(2): 867-889. doi: 10.3934/era.2025039
    [4] Zhizhou Zhang, Yueliang Pan, Weilong Zhao, Jinchu Zhang, Zheng Zi, Yuan Xie, Hehong Zhang . Frequency analysis of a discrete-time fast nonlinear tracking differentiator algorithm based on isochronic region method. Electronic Research Archive, 2024, 32(9): 5157-5175. doi: 10.3934/era.2024238
    [5] Jian Gong, Yuan Zhao, Jinde Cao, Wei Huang . Platoon-based collision-free control for connected and automated vehicles at non-signalized intersections. Electronic Research Archive, 2023, 31(4): 2149-2174. doi: 10.3934/era.2023111
    [6] Chao Ma, Hang Gao, Wei Wu . Adaptive learning nonsynchronous control of nonlinear hidden Markov jump systems with limited mode information. Electronic Research Archive, 2023, 31(11): 6746-6762. doi: 10.3934/era.2023340
    [7] Tongtong Yu, Zhizhou Zhang, Yang Li, Weilong Zhao, Jinchu Zhang . Improved active disturbance rejection controller for rotor system of magnetic levitation turbomachinery. Electronic Research Archive, 2023, 31(3): 1570-1586. doi: 10.3934/era.2023080
    [8] Xingting Geng, Jianwen Feng, Yi Zhao, Na Li, Jingyi Wang . Fixed-time synchronization of nonlinear coupled memristive neural networks with time delays via sliding-mode control. Electronic Research Archive, 2023, 31(6): 3291-3308. doi: 10.3934/era.2023166
    [9] Ning Li, Yuequn Gao . Modified fractional order social media addiction modeling and sliding mode control considering a professionally operating population. Electronic Research Archive, 2024, 32(6): 4043-4073. doi: 10.3934/era.2024182
    [10] Chao Yang, Juntao Wu, Zhengyang Qiao . An improved fixed-time stabilization problem of delayed coupled memristor-based neural networks with pinning control and indefinite derivative approach. Electronic Research Archive, 2023, 31(5): 2428-2446. doi: 10.3934/era.2023123
  • Background 

    Iron deficiency (ID) and ID anemia are widespread in low-income countries, particularly among preterm infants. Hepcidin is a key regulator of iron metabolism, which offers the possibility of new solutions to diagnose ID in premature infants.

    Objective 

    To explore the relationship between iron metabolism and hepcidin in premature infants.

    Materials and methods 

    The study involved 81 preterm infants between 28+1 and 36+6 who underwent iron status indicators and hepcidin testing at 6 months of corrected gestational age. The preterm infants were divided into two groups based on iron status indicators: ID and no ID.

    Results 

    Serum hepcidin was lower for premature infants with ID compared to those without ID (log10hepcidin, 1.18 ± 0.44 vs 1.49 ± 0.37, p = 0.002). A single-variate linear regression model was used to explore the correlation between hepcidin and other indicators of iron metabolism. A strongly positive relationship was observed between hepcidin levels and ferritin levels (p < 0.001) in the correlation analysis.

    Conclusions 

    Hepcidin can be used as an efficient indicator of iron storage and a promising indicator for the early diagnosis of ID in premature infants.



    Alcohol dependence (AD) or Alcoholism, also regarded as alcohol use disorder (AUD), is a complex and relapsing neuropsychiatric disorder [1],[2]. World Health Organization (WHO) reported that approximately 140 million individuals addicted to alcohol globally, resulting in to 2.5 million death each year [3]. AD is regarded as a “reward deficiency syndrome” that intemperately affects public health [4],[5]. It has been found to be influenced by both genetic and environmental factors [6],[7]. Exact patho-physiological and molecular mechanism of AD is not known yet. However, molecular genetic studies support that multiple genes determine an individual's predisposition to AD [8]. Heritability of AD likely plays an important role in its development and is determined to be moderate to high [9],[10]. It was reported frequently that alcohol consumption increased homocysteine (Hcy) concentration i.e hyperhomocysteinaemia [11]. However, inconsistent results of the combined effect of both positive and negative association have been reported between alcohol intake and Hcy [12]. Hyperhomocystenemia is already reported as risk factor for several diseases or disorders including neural tube defects, Alzheimer disease, schizophrenia, pregnancy complications, cardiovascular diseases, noninsulin dependent diabetes and end-stage renal disease as evidenced from several studies [13].

    Homocysteine is a sulfur containing amino acid, several genetic and environmental risk factor are reported for higher plasma concentration of homocysteine [14]. Homocysteine (Hcy) is synthesized in methionine and folate cycle by demethylation of methionine. 5,10-methylenetetrahydrofolate reductase (MTHFR) enzyme of folate cycle plays an important role in homocysteine metabolism. MTHFR gene is present on chromosome 1p36.3. Numerous single nucleotide polymorphisms (SNP) are known in MTHFR gene like C677T and A1298C etc [15],[16]. The most clinically important and studies polymorphism is C677T (rs1801133), in which cytosine (C) is substituted with thymine (T) at 677 nucleotide position and consequently alanine is replaced by valine in MTHFR enzyme (Ala 222 Val) [17],[18]. The variant MTHFR enzyme is thermolabile with reduced activity (~70%) and it increased the plasma homocysteine concentrations [15] (Frosst et al., 1995). Globally, frequency of mutant T allele varies greatly [19][23]. Yadav et al. [23] have conducted a comprehensive C667T polymorphism study and reported the highest frequency in European populations ranging from 24.1% to 64.3% and, lowest frequency from African population. Several studies revealed association of MTHFR gene C677T polymorphism with AD. However, findings showed inconsistent results [24][26]. To derive a more precise estimation of the relationship, authors have performed a meta-analysis.

    Present meta-analysis is carried out according to MOOSE (Meta-analysis of observational studies in epidemiology) guidelines.

    Articles were retrieved through Pubmed, Google scholar, Springer Link, and Science Direct databases up to February 28, 2020, using following key words: “Methylenetetrahydrofolate reductase” or “MTHFR” or “C677T” or “rs1801133” or “polymorphism” and “Alcohol dependence” or “Alcoholism” or “AD” or “Addiction”.

    Inclusion criteria were following: (1) MTHFR C677T polymorphism and alcohol dependence association was investigated in the study, (2) MTHFR C677T genotype/allele numbers in alcohol dependence cases and controls were given in the study, (3) sufficient information for calculating the odds ratio (OR) with 95% confidence interval (CI) and (4) Articles published in English language were only considered. Major reasons for studies exclusion were as follows: (1) no alcohol dependence cases analyzed, (2) the C677T polymorphism details information missing, and (3) duplicate article.

    Name of first author, country name, number of cases and controls, number of genotypes in cases and controls and journal name with full reference from each article were extracted.

    All analysis were done according to the method of Rai et al. [27]. Odds ratio (ORs) with 95% confidence intervals (CIs) were calculated using fixed effect and random effect models [28],[29]. A five genetic models viz. allele contrast, co-dominant, homozygote, dominant and recessive models were calculated. Heterogeneity was investigated and quantified by I2 statistic [30]. Chi-squared analysis was used to determine whether the genotype distribution of control group was in Hardy–Weinberg equilibrium or not. Subetaoup analysis was conducted by ethnicity. In included articles, case samples were not categorized on the basis of gender, so the subetaoup analysis based on gender did not performed in present meta-analysis. Publication bias was investigated by Egger's regression intercept test [31]. P value <0.05 was considered statistically significant. All calculations were done by softwares MIX version 1.7 [32] and MetaAnalyst [33] program.

    Selection of studies is given in fow diagram (Figure 1). Following the exclusion criteria, 10 individual case-control studies with a total of 1676 cases and 1594 controls were included into this meta-analysis [24][26],[34][40]. One author [38] reported their data in to two categories, we included both set of data as different studies. Hence, total number of included studies in present meta-analysis is eleven (Table 1).

    Figure 1.  Flow diagram of study search and selection process.
    Table 1.  Details of included eleven studies in the present meta-analysis.
    Study Ethnicity Control Case Case
    Genotype
    Control Genotype
    HWE p-value of controls
    CC CT TT CC CT TT
    Bonsch, 2006 Caucasian 115 134 64 56 14 60 41 14 0.10
    Lutz, 2006 Caucasian 102 221 95 94 32 53 41 8 0.98
    Lutz, 2007 Caucasian 101 142 65 58 19 53 40 8 0.90
    Saffroy, 2008 Caucasian 93 242 108 113 21 35 41 17 0.41
    Benyamina, 2009 Caucasian 93 120 56 53 11 35 41 17 0.41
    Fabris, 2009 Caucasian 236 63 17 35 11 69 113 54 0.55
    Shin, 2010 Asian 232 68 11 39 18 42 129 61 0.07
    Supic, 2010, Heavy Alcoholic Caucasian 57 32 13 9 10 27 24 6 0.84
    Supic, 2010, Non heavy Alcoholic Caucasian 105 64 37 23 4 53 42 10 0.69
    Singh, 2014 Asian 313 139 91 44 4 228 78 7 0.91
    Singh, 2015 Asian 147 451 312 125 14 107 35 5 0.32

     | Show Table
    DownLoad: CSV

    Overall, eleven studies provided 1676/1594 cases/controls for MTHFR C677T polymorphism. The prevalence of C and T alleles in AD cases was 71.22% and 28.79% respectively. The percentage frequency of TT genotype among cases and controls was 9.43% and 12.98%, respectively whereas prevalence of CT heterozygote among AD cases was 38.72% and 39.21% in controls. The prevalence of CC homozygote among AD cases and controls was 51.85% and 47.80%, respectively. Genotypes were in Hardy-Weinberg equilibrium in all controls. In control group the percentage of C and T allele frequencies was 67.41% and 32.59% respectively (Figure 2).

    Figure 2.  Bar diagram showing percentage of C and T allele frequencies in control group of total 11 studies, 3 Asian studies and 8 Caucasian studies.

    No significant association was observed between the MTHFR C677T polymorphism and the susceptibility to AD in all the genetic models using random effect model (for T vs. C (allele contrast): OR = 1.04, 95% CI = 0.88–1.24; CT vs. CC (co-dominant): OR = 1.02, 95% CI = 0.62–1.68; for TT+CT vs. CC (dominant): OR = 1.10, 95% CI = 0.94–1.29; for TT vs. CC (homozygote): OR = 1.01, 95% CI = 0.66–1.51; for TT vs. CT + CC (recessive): OR = 0.97, 95% CI = 0.66–1.40) (Table 2; Figures 3).

    A true heterogeneity existed between studies for allele contrast (Pheterogeneity = 0.02, Q = 20.64, I2 = 51.56%, t2 = 0.04, z = 0.69), co-dominant genotype (Pheterogeneity < 0.0001, Q = 86.64, I2 = 88.46%, t2 = 0.61, z = 4.29), homozygote genotype (Pheterogeneity = 0.02, Q = 20.93, I2 = 52.24%, t2 = 0.24, z = 0.1), and recessive genotype (Pheterogeneity = 0.02, Q = 21.00, I2 = 52.40%, t2 = 0.20, z = 0.47) comparisons. The “I2” value of more than 50% shows high level of true heterogeneity.

    Table 2.  Summary estimates for the odds ratio (OR) of MTHFR C677T in various allele/genotype contrasts, the significance level (p value) of heterogeneity test (Q test), and the I2 metric and publication bias p-value (Egger Test).
    Genetic Models Fixed effect OR (95% CI), p Random effect OR (95% CI), p Heterogeneity p-value (Q test) I2 (%) Publication Bias (p of Egger's test)
    Allele Contrast (T vs. C) 1.04 (0.92–1.17), 0.48 1.04 (0.88–1.24), 0.60 0.02 51.56 0.62
    Dominant (TT+CT vs. CC) 1.41 (1.20–1.65), <0.0001 1.02 (0.62–1.68), 0.92 <0.0001 88.46 0.06
    Homozygote (TT vs. CC) 0.98 (0.75–1.29), 0.92 1.01 (0.66–1.51), 0.97 0.02 52.24 0.26
    Co-dominant (CT vs. CC) 1.10 (0.94–1.29), 0.21 1.10 (0.94–1.29), 0.21 0.43 0 0.48
    Recessive (CC+CT vs. TT) 0.94 (0.73–1.20), 0.63 0.97 (0.66–1.40), 0.86 0.02 52.4 0.28

     | Show Table
    DownLoad: CSV
    Figure 3.  Random effect Forest plot of allele contrast model (T vs. C) of total 11 studies of MTHFR gene C677T polymorphism.

    Out of 11 studies included in the present meta-analysis, 3 studies were carried out in Asian countries, and 8 studies were carried out on Caucasian (Table 1). The subetaoup analysis by ethnicity did not reveal any significant association between MTHFR C677T polymorphism and AD in Asian population (T vs. C: OR = 1.16; 95% CI = 0.93–1.44; p = 0.17; I2 = 3.1%; Pheterogeneity = 0.65; TT vs. CC: OR = 1.16; 95% CI = 0.62–2.02; p = 0.69; I2 = 3.1%; Pheterogeneity = 0.89; and TT+CT vs. CC: OR = 1.26; 95% CI = 0.96–1.67; p = 0.09; I2 = 3.1%; Pheterogeneity = 0.81); and Caucasian population (T vs. C: OR = 0.99; 95% CI = 0.86–1.14; p = 0.93; I2 = 61.75%; Pheterogeneity = 0.01; TT vs. CC: OR= 0.95; 95% CI = 0.70–1.29; p = 0.75; I2 = 65.63%; Pheterogeneity = 0.004; and TT+CT vs. CC: OR = 1.03; 95% CI = 0.85–1.25; p = 0.75; I2 = 13.93%; Pheterogeneity = 0.32) (Figures 4 and 5).

    Figure 4.  Random effect Forest plot of allele contrast model (T vs. C) of total 3 Asian studies of MTHFR gene C677T polymorphism.
    Figure 5.  Random effect Forest plot of allele contrast model (A vs. G) of total 8 Caucasian studies of MTHFR gene C677T polymorphism.

    Symmetrical shape of Funnel plots' revealed absence of publication bias. P values of Egger's test were more than 0.05, also provided statistical evidence for the funnel plots' symmetry (p = 0.62 for T vs. C; p = 0.48 for TT vs. CC; p = 0.26 for CT vs. CC; p = 0.48 for TT+AC vs. CC; p = 0.28 for TT vs. CT+CC) (Table 2; Figure 6).

    Figure 6.  Funnel plot- Precision by log odds ratio for allele contrast model (T vs. C) of total 11 studies of MTHFR gene C677T polymorphism.

    In vivo and in vitro studies has demonstrated that homocysteine has neurotoxic effects especially on dopamine neurons of reward pathway [11]. In addition, hyperhomocysteinemia is also reported in AD [11],[41]. In MTHFR gene several polymorphisms are reported but according to deficit hypothesis of addiction, only C677T polymorphism-dependent alteration of the reward system possibly leads to alcohol addiction. Further, homovanillic acid (HVA) is a potential indicator of central dopaminergic neuronal activity [42] and experimentally, it was demonstrated that higher concentration of homocysteine lowers the level of HVA in rat striatum region [43]. On the basis of 11 studies providing data on MTHFR C677T genotype and AD risk in two ethnic populations, including over 3,205 subjects, our meta-analysis provides an evidence that TT and CT genotypes or T allele are not associated with AD risk. Hence the present meta-analysis indicated that C677T is not a risk factor of AD.

    Meta-analysis is a statistical tool to combine the information of independent case-control studies with similar target [44]. Several meta-analysis are published, which evaluated effects of folate pathway genes polymorphisms in susceptibility of diseases/disorders- cleft lip and palate [45], down syndrome [46][48], male infertility [49], bipolar disorder [50], schizophrenia [51],[52], depression [53], obsessive compulsive disorder [54], hyperurecemia [55], epilepsy [56], Alzheimers disease [57], esophageal cancer [58], and ovary cancer [59].

    Several limitations that should be acknowledged like (i) calculated crude Odds ratio, (ii) included the less number of available studies (10 studies) and the limited sample size of each included study, (iii) observed higher between study heterogeneity, (iv) considered only one gene polymorphism and (v) not considered other confounding factors like diet, gender etc. In addition to limitations, current meta-analysis has several strength also such as—higher study power and larger sample size in comparison to individual case control studies, and absence of publication bias etc.

    In conclusion, pooled analysis of data from 11 separate articles indicates that the MTHFR 677TT genotype is not a risk factor for AD. The results of present meta-analysis should be interpreted with certain cautions due to presence of higher heterogeneity and small number of included studies. Future large-scale, population-based association studies from different regions of the world are required to investigate potential gene-gene and gene-environment interactions involving the MTHFR C677T polymorphism in determining AD risk.


    Acknowledgments



    The authors disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This study was supported by a Ministry of Science and Technology's national key scientific research project grant (Research on Environmental Risk Prevention and Control of Bad Birth Outcomes Based on Multi-center Collaboration Network; 2019YFC0840702-1).

    Conflict of interest



    The authors declare no conflict of interest.

    [1] Camaschella C (2019) Iron deficiency. Blood 133: 30-39. https://doi.org/10.1182/blood-2018-05-815944
    [2] Moreno-Fernandez J, Ochoa JJ, Latunde-Dada GO, et al. (2019) Iron Deficiency and Iron Homeostasis in Low Birth Weight Preterm Infants: A Systematic Review. Nutrients 11: 1090. https://doi.org/10.3390/nu11051090
    [3] Akkermans MD, Uijterschout L, Abbink M, et al. (2016) Predictive factors of iron depletion in late preterm infants at the postnatal age of 6 weeks. Eur J Clin Nutr 70: 941-946. https://doi.org/10.1038/ejcn.2016.34
    [4] Zimmermann MB (2020) Global look at nutritional and functional iron deficiency in infancy. Hematology 2020: 471-477. https://doi.org/10.1182/hematology.2020000131
    [5] Ferri C, Procianoy RS, Silveira RC (2013) Prevalence and Risk Factors for Iron-Deficiency Anemia in Very-Low-Birth-Weight Preterm Infants at 1 Year of Corrected Age. J Tropi Pediatr 60: 53-60. https://doi.org/10.1093/tropej/fmt077
    [6] Suwannakeeree P, Jangmeonwai P The prevalence and risk factors of iron deficiency anemia in Thai infants by complete blood count at 9-month-old (2020).
    [7] Campbell RK, Buhimschi CS, Zhao G, Dela Rosa C, Stetson BT, Backes CH, Buhimschi IA (2022) Prevalence of and Risk Factors for Iron Deficiency in Twin and Singleton Newborns. Nutrients 14: 3854. https://doi.org/10.3390/nu14183854
    [8] Pivina L, Semenova Y, Doşa MD, et al. (2019) Iron Deficiency, Cognitive Functions, and Neurobehavioral Disorders in Children. J Mol Neurosci 68: 1-10. https://doi.org/10.1007/s12031-019-01276-1
    [9] Sundararajan S, Rabe H (2021) Prevention of iron deficiency anemia in infants and toddlers. Pediatr Res 89: 63-73. https://doi.org/10.1038/s41390-020-0907-5
    [10] Siddappa AM, Georgieff MK, Wewerka S, et al. (2004) Iron Deficiency Alters Auditory Recognition Memory in Newborn Infants of Diabetic Mothers. Pediatr Res 55: 1034-1041. https://doi.org/10.1203/01.pdr.0000127021.38207.62
    [11] McCarthy EK, Murray DM, Kiely ME (2022) Iron deficiency during the first 1000 days of life: are we doing enough to protect the developing brain?. Proc Nutr Soc 81: 108-118. https://doi.org/10.1017/S0029665121002858
    [12] Christensen RD, Bahr TM, Ward DM (2022) Iron deficiency in newborn infants: global rewards for recognizing and treating this silent malady. Newborn (Clarksville, Md) 1: 97-103. https://doi.org/10.5005/jp-journals-11002-0021
    [13] Bahr TM, Ward DM, Jia X, et al. (2021) Is the erythropoietin-erythroferrone-hepcidin axis intact in human neonates?. Blood Cells, Molecules, and Diseases 88: 102536. https://doi.org/10.1016/j.bcmd.2021.102536
    [14] Christensen RD, Bahr TM, Ward DM (2022) Iron deficiency in newborn infants: global rewards for recognizing and treating this silent malady. Newborn (Clarksville, Md) 1: 97-103. https://doi.org/10.5005/jp-journals-11002-0021
    [15] Camaschella C, Nai A, Silvestri L (2020) Iron metabolism and iron disorders revisited in the hepcidin era. Haematologica 105: 260-272. https://doi.org/10.3324/haematol.2019.232124
    [16] Nemeth E, Ganz T (2023) Hepcidin and Iron in Health and Disease. Annu Revi Med 74: 261-277. https://doi.org/10.1146/annurev-med-043021-032816
    [17] Berglund SK, Chmielewska AM, Domellöf M, et al. (2021) Hepcidin is a relevant iron status indicator in infancy: results from a randomized trial of early vs. delayed cord clamping. Pediatr Res 89: 1216-1221. https://doi.org/10.1038/s41390-020-1045-9
    [18] Albaroudi IN, Khodder M, Al Saadi T, et al. (2018) Prevalence, diagnosis, and management of iron deficiency and iron deficiency anemia among Syrian children in a major outpatient center in Damascus, Syria. Avicenna J Med 8: 92-103. https://doi.org/10.4103/ajm.AJM_169_17
    [19] Babaei M, Shafiei S, Bijani A, et al. (2017) Ability of serum ferritin to diagnose iron deficiency anemia in an elderly cohort. Revista brasileira de hematologia e hemoterapia 39: 223-228. https://doi.org/10.1016/j.bjhh.2017.02.002
    [20] Ichinomiya K, Maruyama K, Inoue T, et al. (2017) Perinatal factors affecting serum hepcidin levels in low-birth-weight infants. Neonatology 112: 180-186. https://doi.org/10.1159/000473871
    [21] (2001) WHO, U.UNU. Iron deficiency anaemia: assessment, prevention, and control. Geneva: WHO.
    [22] Pigeon C, Ilyin G, Courselaud B, et al. (2001) A new mouse liver-specific gene, encoding a protein homologous to human antimicrobial peptide hepcidin, is overexpressed during iron overload. J Biol Chem 276: 7811-7819. https://doi.org/10.1074/jbc.M008923200
    [23] Uijterschout L, Domellöf M, Berglund SK, et al. (2016) Serum hepcidin in infants born after 32 to 37 wk of gestational age. Pediatr Res 79: 608-613. https://doi.org/10.1038/pr.2015.258
    [24] Pasricha SR, Atkinson SH, Armitage AE, et al. (2014) Expression of the iron hormone hepcidin distinguishes different types of anemia in African children. Sci Transl Med 6: 235re3-235re3. https://doi.org/10.1126/scitranslmed.3008249
    [25] Sanad M, Gharib AFJIjop (2011) Urinary hepcidin level as an early predictor of iron deficiency in children. A case control study 37: 1-8. https://doi.org/10.1186/1824-7288-37-37
    [26] Lesbordes-Brion JC, Viatte L, Bennoun M, et al. (2006) Targeted disruption of the hepcidin 1 gene results in severe hemochromatosis. Blood 108: 1402-1405. https://doi.org/10.1182/blood-2006-02-003376
    [27] Aranda N, Bedmar C, Arija V, Jardí C, Jimenez-Feijoo R, Ferré N, Tous MJAoH Serum hepcidin levels, iron status, and HFE gene alterations during the first year of life in healthy Spanish infants (2018)97: 1071-1080. https://doi.org/10.1007/s00277-018-3256-2
    [28] Chappell M, Rivella SJh New potential players in hepcidin regulation (2019)104: 1691. https://doi.org/10.3324/haematol.2019.224311
    [29] Roy CN, Mak HH, Akpan I, et al. (2007) Hepcidin antimicrobial peptide transgenic mice exhibit features of the anemia of inflammation. Blood 109: 4038-4044. https://doi.org/10.1182/blood-2006-10-051755
    [30] Roth MP, Meynard D, Coppin H (2019) Chapter Five - Regulators of hepcidin expression. Vitamins and Hormones.Academic Press 101-129. https://doi.org/10.1016/bs.vh.2019.01.005
    [31] Guyatt GH, Oxman AD, Ali M, et al. (1992) Laboratory diagnosis of iron-deficiency anemia. J Gen Intern Med 7: 145-153. https://doi.org/10.1007/BF02598003
    [32] Mattiello V, Schmugge M, Hengartner H, et al. (2020) Diagnosis and management of iron deficiency in children with or without anemia: consensus recommendations of the SPOG Pediatric Hematology Working Group. Eur J Pediatr 179: 527-545. https://doi.org/10.1007/s00431-020-03597-5
    [33] Berglund S, Lönnerdal B, Westrup B, et al. (2011) Effects of iron supplementation on serum hepcidin and serum erythropoietin in low-birth-weight infants. Am J Clin Nutr 94: 1553-1561. https://doi.org/10.3945/ajcn.111.013938
    [34] Rehu M, Punnonen K, Ostland V, et al. (2010) Maternal serum hepcidin is low at term and independent of cord blood iron status. Eur J Haematol 85: 345-352. https://doi.org/10.1111/j.1600-0609.2010.01479.x
    [35] Cross JH, Prentice AM, Cerami CJCdin (2020) Hepcidin, serum iron, and transferrin saturation in full-term and premature infants during the first month of life: a state-of-the-art review of existing evidence in humans. Curr Dev Nutr 4: nzaa104. https://doi.org/10.1093/cdn/nzaa104
    [36] Galesloot TE, Vermeulen SH, Geurts-Moespot AJ, et al. (2011) Serum hepcidin: reference ranges and biochemical correlates in the general population. Blood, Am J Hematol 117: e218-e225. https://doi.org/10.1182/blood-2011-02-337907
    [37] Mupfudze TG, Stoltzfus RJ, Rukobo S, et al. (2014) Hepcidin decreases over the first year of life in healthy African infants. Br J Haematol 164: 150-153. https://doi.org/10.1111/bjh.12567
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(2280) PDF downloads(177) Cited by(1)

Figures and Tables

Figures(2)  /  Tables(3)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog