Type of the Paper (Article, Review, Communication, etc.)

Keywords: Iron Status; Iron Deficiency Anemia; Dietary Protein Intake.

2. Materials and Methods  

2.1. Participants 

The present study was based on data from the National Diet Nutrition Survey (NDNS) conducted in the United Kingdom from 1986-1987. Data files from the NDNS survey were analysed for a cross-sectional study of the association between dietary protein intake and iron status. The four original NDNS data files used in the present study were: anthropometric data,  blood analytes data, urine analytes data, data from the 7-day food diary and interview data which became publicly available in the year 2000 [1]. All data used in this paper was collected by technicians who were recruited by the Medical Council Human Nutrition Research Cambridge [1]. Participants who were pregnant or lactating were excluded from this study as they had increased dietary requirements which would influence results. Participants with missing data were also excluded. The original NDNS data files contained 2251 individual participants who were men and women aged 19-64 years [1]. The present study contains 928 participants as not all participants had relevant or complete data for the purposes of this study. Data collection of the original NDNS study was ethically approved by the Multi-centre Research Ethics Committee (MCREC) and the National Health Service Local Research Ethics Committees (LRECs) [1] 

2.2. Anthropometry 

As the present study investigates data from the NDNS, it contains a cross-sectional design. To investigate the association of dietary protein intake with iron status including risk of iron deficiency anaemia, the present study incorporated anthropometric, biochemical, environmental and dietary variables to account for a broader range of confounding variables. Anthropometric markers chosen for the present study were weight (kg), height (cm) and body mass index (BMI) (kg/m²) which were provided by the NDNS anthropometric data file.

 2.3. Biochemistry 

Biochemical markers were provided from the NDNS blood analytes data file. Iron biomarkers chosen from the blood analytes file were serum ferritin (ug/l) and haemoglobin (g/dl). Cut-offs from the NDNS user guide were applied to serum ferritin and haemoglobin data to create low, normal and high groups. Serum ferritin groups were sex dependent. For men, low, normal and high serum ferritin was defined as below 20ug/l, 20ug/l – 200ug/l and above 200ug/l respectively [1]. For women low, normal and high serum ferritin was defined as below 10ug/l, 10 – 150ug/l and above 150ug/l respectively [1]. Blood haemoglobin concentration groups were also sex dependent. For men, low, normal and high haemoglobin was defined as below 13.5g/dl, 13.5 – 16.5g/dl and above 16.5g/dl respectively. For women, low, normal and high haemoglobin was defined as less than 12g/dl, 12 – 16g/dl and above 16g/dl respectively [1]. The average values of serum B12 (pM/l) and serum folate (mN/l) were also analysed.

2.4. Environmental 

Environment data was provided by the NDNS interview data. The present study used gross annual income (GAI) in pounds (£) as the environmental variable of interest. Gross annual income was grouped using the range of responses provided participants in the NDNS interviews. There were four GAI groups which, in ascending order, were defined as less than 2000£ – less than 8000£, 8000£ – less than 18000£, 18000£ – less than 25000£ and 25000£ – less than 30000£. 

2.5. Dietary Analysis 

The NDNS dietary data file was refined for relevance to the present research question. Participants’ actual and average daily intakes were calculated from the NDNS seven day food diary. The average daily intakes of protein (g/day), carbohydrates (g/day) and fats (g/day) and iron (mg/day) haem-iron (mg/day), non-haem iron (mg/day), vitamin B12 (g/day) and folate (g/day) were calculated by summing the actual daily intakes for each individual nutrient. The percentage of participants taking vitamin supplementation was also calculated.

Protein intake (g/day) was classified as low, normal or high to create a new categorical variable which was weight dependent. The rationale for defining protein intake as low, normal and high was participants’ range and average results of protein intake. Cut-off values for protein intake groups were drawn from the UK National  Health Service protein recommended nutrient intake (RNI) [2]. Based on the UK RNI of 0.75g protein per kg body weight [2], protein intake groups were defined separately for participants who weighed below and above the average weight. For participants below the average weight, adequate intake for the lightest weight (kg) (29.2g protein/day) was calculated and intakes below this value was considered low intake, while intakes above adequacy for the average weight (55.3g protein/day) were considered high intake. For participants below the average weight, intakes between adequacy for the lightest weight and the average weight were classified as normal. For participants weighing above the median weight, the same calculations were repeated to identify cut-offs. For participants weighing above the median weight, intakes below adequacy for the median were classified as low,  intakes above adequacy for the cohort’s heaviest weight (126.2g protein/day) were considered high intake and intakes between the adequacy for the median and heaviest weight were considered normal.

Basal metabolic rate (BMR) was calculated using the Henry equation [3], which uses age and sex to assign an assumed, constant amount of daily energy expended in megajoules (MJ) by an individual. For the purpose of this study BMR in MJ was converted to kilojoules (kJ). Energy intake to BMR ratio (EI:BMR) was subsequently calculated using average daily energy intake (kJ) and BMR (kJ). Percentage of total energy intake (%TEI) from the macronutrients, protein, carbohydrates and fats were separately calculated using average daily intake of protein, carbohydrates and fats (g/day) and average daily energy intake.  Identification of mis-reporters was undertaken by use of Goldberg cut-offs  [4] which identified under-reporters and plausible reporters as participants with an EI:BMR ratio less than 0.9, and above 0.9 respectively.

Using protein intake classifications of low, normal and high, dietary intakes for both macronutrients and micronutrients were split to analyse the differences in dietary intake between low, normal and high protein consumers.

2.6. Statistical analysis

Data was analysed by the Statistical Program for Social Science (IBM SPSS) [5], version 26. Normality of the participant characteristics and dietary data was assessed using standard descriptive statistics. The associations between variables were analysed using the Chi-Squared Independence test, the Mann-Whitney U and Kruskal-Wallis test where appropriate. To examine the association between serum ferritin and iron status risk factors and dietary variables, a multivariable linear regression was undertaken. As the dependent variable, serum ferritin non-parametric, outliers were excluded and a log transformation was undertaken before serum ferritin was entered into the multivariable regression; this adjusted serum ferritin variable was used in all three regression models. At least one anthropometric, biochemical, dietary and environmental variable was selected for models 2 and 3 of the regression and associations were examined in three models. Model 1 was only adjusted for age and sex, model 2 was multivariable-adjusted for age, sex, BMI, haemoglobin concentration, iron intake, protein intake and gross annual income and model 3 contained the same variables as model 2 with the inclusion of EI:BMR.

3. Results

3.1. Participant characteristics 

Significance was observed in all the variables (Table 1) (p < 0.005), excluding age (p = 0.517). The average age of the total group was 42 years and age was not significantly different between males and females. Males were significantly taller and heavier than females (p < 0.001). The average BMI for both males and females indicated that participants were overweight [6]. The BMI of males was also significantly higher (p = 0.003) than that of females at an average of 26.64 kg/m² and an average BMI of 25.70 kg/m²  was observed in females.

The majority (87.3%) of participants for the total group had normal serum ferritin levels. Average serum ferritin for the total cohort was 63ug/l (34ug/l, 107ug/l). Serum ferritin was significantly higher in males than females (p < 0.001), the average serum ferritin value for males and females were 92.0ug/l and 43.0ug/l respectively. A significantly higher percentage of males (4.5%) than females (3.2%) had low serum ferritin (p < 0.001) but more males than females had high serum ferritin (p < 0.001). While 14.8% of males were classified as having high serum ferritin and only 4% of females were classified as having high serum ferritin (Table 1).

The majority (89.3%) of both male and female participants had normal haemoglobin (g/dl) status. Average blood haemoglobin concentration (g/dl) for the total group was 14.1g/dl (13.2g/dl, 15.1g/dl), which falls within the normal range of haemoglobin. Significant differences in haemoglobin status were observed between sex (p < 0.001) (Table 1). More females than males had low haemoglobin (7.4%) and normal haemoglobin (92.0%) however a higher percentage of males had high haemoglobin status than females (7.8%). On average, males also had significantly higher concentrations of serum B12 (p < 0.001) (Table 1). Observations of differences in iron biomarkers of serum ferritin and haemoglobin between sex is indicative of an inherent difference between males and females unaffected by protein intake.

The majority of participants had GAI between less than 2000£ and less than 25,000£ (55%) while the remaining 45% reported GAI of 25,000£ to less than 30,000£. A higher percentage of females (16.%) than males (11.3%) fell into the lowest GIA category and a lower percentage of females (40.4%) than males (50.6%) fell into the highest GIA category (Table 1).

Table 1. Demographic and Iron status characteristics – change to 1 decimal point

BMI: Body Mass Index. SFerritin: Serum Ferritin. Hb: Haemoglobin. Conc.: concentration. GAI :Gross Annual Income classifications. ^** significant difference of p < 0.001 identified. * statistical difference of p < 0.05 identified. † cumulative percentage of this categorical variable does not equal 100% for female participants due to missing data.

3.2. Dietary Intake

The number of participants in the who were classified as having low, normal and high protein intake was 187, 711 and 30 respectively (Table 2). Energy intake (kJ/day) and macronutrient intake (g/day) was significantly different between protein intake groups ( p < 0.001). Energy intake of participants who had lower protein intake was significantly lower than that of participants who had normal or high protein intake (p < 0.001). The average energy intake of participants within the low protein intake classification was 8014.2kJ (Table 2) while  the average energy intake of participants with high protein intake was 13419.4kJ (Table 2). The percentage of total energy intake (%TEI) from protein and carbohydrates was significantly different between protein intake groups (Table 2), however %TEI from fats did not differ significantly for participants who consumed a low, normal or high amounts of protein (p = 0.350).  Average daily iron intake, including haeme-iron and non-haeme iron intakes were also significantly different between protein intake groups where participants who had high protein intake, also had higher total iron intake (p < 0.001). Average daily B12 (g/day) and folate (g/day) intake fell within normal ranges suggesting that they were unlikely to negatively impact the haemoglobin values observed in the present study.  Dietary B12 and folate intake also increased with protein intake (p < 0.001). Additionally, vitamin supplementation or lack thereof was not significantly associated with classification of protein intake. BMR was higher for high protein consumers than low – normal protein consumers (Table 2). EI:BMR ratio was also significantly different between protein intake classifications (p < 0.001), where low protein consumers had the lowest EI:BMR ratio. Additionally, the most under-reporters (38.0%) were identified within the low protein intake group. The low EI:BMR and high under-reporting percentage observed in the low protein intake group may suggest that participants in this classification did not truly have inadequate protein intake.

Table 2. Dietary characteristics and mis-reporters by Protein Intake Groups

%TEI: Percentage of Total Energy Intake from Protein, Carbohydrates or Fats. BMR: Basal Metabolic Rate calculated using Henry equation. EI:BMR: Energy Intake to Basal Metabolic Rate ratio calculated using energy intake and basal metabolic rate. ** Significant difference of p < 0.001 identified for this variable.

3.3. Protein Intake and Sex

Of the participants with low protein intake, a majority were female participants (85%). The percentage of males and females who had normal protein intake was approximately equal (51.6% males). All of the participants who reported high protein intake were males (Table 3).

Table 3. Protein Intake classifications for Males and Females.

** Significant association between of p < 0.001 identified

3.4. Associations between Iron Status and Dietary Intake and Participant Characteristics

All regression models were statistically significant (p < 0.001) (Table 4). Model 1 shows that 22.9% of variation in serum ferritin can be explained by age and sex. In model 1,  sex had the greatest unique influence on serum ferritin (p < 0.001,95%CI = (-0.891, -0.692, standardised Beta = – 0.451). Sex alone could explain 20.3% of variation within model 1.

In model 2, 29.3% of variation in serum ferritin can be explained by the independent variables in the model 2. The most influential variable in model 2 was blood haemoglobin (p < 0.001, 95% CI (0.148,0.242), standardised Beta = 0.290 ), 6.6% of variation in this model could be explained by haemoglobin. Sex was significantly associated with serum ferritin (p < 0.001, 95% CI = (-0.535, -0.261), standardised Beta = -0.227) BMI and protein intake were also significantly associated with serum ferritin (p < 0.05). No significant associations were found for iron intake or GAI. 

In model 3, 29.7% of the variation in serum ferritin can be predicted by variables within the model. Haemoglobin had the greatest influence on the significance of this model and was significantly associated with serum ferritin (p <0.001, 95%CI = (0.140,0.234), standardised Beta = 0.280), 7% of variation in this model could be explained by haemoglobin. This model found protein intake to be positively associated with serum ferritin (p = 0.002, 95%CI = (0.002,0.009), standardised Beta =  0.156). No significant associations were found between GAI.

Table 4. Multivariant Regression Analysis of Iron Status Biomarkers, Dietary intake and Participant characteristics.

95%CI: 95% Confidence Intervals. BMI: Body Mass Index. Hb conc.: Haemoglobin concentration. GAI: Gross Annual Income in pounds. EI:BMR: Energy intake to Basal Metabolic Rate ratio calculated using energy intake and basal metabolic rate. ** Significant association of p < 0.001 identified between serum ferritin (the dependent variable) and this independent variable. * Significant association of p < 0.05 identified between serum ferritin (the dependent variable) and this independent variable. † Most influential standardized beta within this model.

4. Discussion

Iron deficiency anaemia is predominately caused by inadequate dietary iron consumption or chronic blood loss [7, 8] and is one of the most common health conditions globally. As such, investigations of the aetiology of iron deficiency anaemia is highly important. The present study aimed to investigate the association of dietary protein intake on iron status. Iron status was primarily measured by serum ferritin as it is a pertinent biomarker for risk of iron deficiency anaemia [8]. Additionally, haemoglobin was used as a secondary measure of iron status as, though relevant to iron status, it is possible to have normal haemoglobin in the presence of low or inadequate iron stores [7,8].

Iron deficiency anaemia is defined by the World Health Organisation as haemoglobin concentrations below 12g/dl for men and 13g/dl for women [8]. In the current cohort risk of iron deficiency and iron deficiency anaemia were rare, with only 3.8% of individuals presenting with low serum ferritin and 6.8% presenting with low blood haemoglobin concentrations. Compared to previous studies, the prevalence of iron deficiency anaemia is low [9, 10]. The present study identified biochemical differences between males and females (Table 1), these findings are consistent with a previous Chinese study which identified serum ferritin and serum haemoglobin to be significantly higher in males (p < 0.001) [9]; in this study, average serum ferritin for males and females was 123.0ug/l and 26.7ug/l respectively. Lower iron stores in females may be attributed to increased iron losses during menstrual years [11].

Differences in protein consumption between males and females (Table 3) are consistent with the previous literature, which found that males consumed significantly more protein than females (p < 0.001) [12, 13] Higher protein consumption in males may be due to general differences in size and therefore energy intake between sex [9, 12].

The present study found a positive association between serum ferritin and protein intake (p < 0.05) (Table 4), these findings are similar to previous studies where consumption of high protein foods such as red meat or pork was significantly associated with higher iron status [9, 13]. For the cohort of this study, this association may contribute to the higher serum ferritin seen in males, as they have been found to consume more protein [9, 11, 13]. In alignment with previous studies [14, 15], this present investigation found that iron intake was not significantly associated with serum ferritin (Table 4), this is likely as dietary iron is not immediately stored as ferritin [8], leading to a non-significant association.

The present study included an analysis of participants’ serum B12 and folate concentrations as both nutrients play important roles in haemoglobin synthesis and are therefore potential confounders, as inadequacies in either nutrient may lead to an observation of low or inadequate haemoglobin concentration [7]. Both females and males within the cohort of the present study showed normal serum B12 and serum folate within the ranges of 6nmol/l – 45.5nmol/l for serum B12 and 2-20ng/mL [7] for serum folate, thus, limiting the risk of confounding.

Strengths & Limitations 

While there is general focus on iron status of women within the literature, the present study offers an  investigation of the influence of dietary factors on iron status in both males and females. The sample size of the present study is also relatively large, which contributes to the statistical power of this present study. Furthermore, the present study declares no bias.

There are several limitations in the present study. Importantly, the assumption of normality of the dependent variable of the multivariable regression analysis was violated which may significantly impact the results of this study. It is also worth noting that the Goldberg cut-offs used to identify mis-reporters are not a gold standard method and has been shown to increase bias and lead to a loss of statistical power [16].

As the present study is a cross-sectional analysis it may not be an accurate depiction of protein intake or iron status of adults aged 19 – 64 years living in the United Kingdom, furthermore, participants’ diet variety particularly protein intake from specific foods or food groups was not investigated in this study. Additionally as this study aimed to investigate the association between protein intake and serum ferritin in both sexes analysis of the association of protein intake on cohort characteristics were not undertaken. Further research should be undertaken to replicate the findings of this present study to further investigate the relationship between protein intake and iron status. There should be a greater focus on identifying diet variety, especially pertaining to sources of protein and their relationship to iron status biomarkers.

Conclusion

The present study establishes an association between protein intake and serum ferritin concentrations that is significantly different between sexes. This study also observed other, stronger associations between cohort characteristics and serum ferritin, therefore further research must be undertaken to determine the true effect of protein intake on iron status in the context of serum ferritin.

References

1. Office for National Satistics (2005) National Diet and Nutrition Survey: Adults Aged 19 to 64 Years, 2000–2001 [data collection]. UK Data Service, SN: 5140. http://dx.doi.org.ezproxy.lib.monash.edu.au/10.5255/UKDA-SN-5140- 1
2. British Nutrition  Foundation. United Kingdom: British Nutrition Foundation; 2016.Nutrition Requirements; 2016 [Date cited of part 2020 Jun 10]; [Nutrition Requirements]. Available from: https://www.nutrition.org.uk/attachments/article/234/Nutrition%20Requirements_Revised%20Oct%202016.pdf
3. Henry CJK. Basal metabolic rate studies in humans: measurement and development of new equations. Pub. Health Nutr. 2005 Oct [cited 2020 Jun 10]. 8(7): 1133-1152. Available from:  https://search-proquest-com.ezproxy.lib.monash.edu.au/docview/68777581?accountid=12528&rfr_id=info%3Axri%2Fsid%3Aprimo
4. Goldberg GR, Black AE, Jebb SA, Cole TJ, Murgatroyd PR, Coward WA, Prentice AM. Critical evaluation of energy intake data using fundamental principles of energy physiology. Derivation of cut-off values to identify under-recording. Eur J Clin Nutr 1991; 45: 569 ± 581.
5. IBM Corp. IBM SPSS Statistics for Windows [Internet]. Version 25.0, Armonk, NY: IBM Corp.
6. British Nutrition Foundation [Internet]. UK: British Nutrition Foundation; 2018. Protein; 2018 [Date cited of part 2020 Jun 10]; [Nutrition Science]. Available from: https://www.nhs.uk/common-health-questions/lifestyle/what-is-the-body-mass-index-bmi/
7. Higgins C. Defeciency Testing for Iron, Vitamin B12 and Folate. Nurs. Times [Internet]. 1995 Jun [cited 2020 Jun 10];6;91(22):39-39. Available from: https://pubmed.ncbi.nlm.nih.gov/7603852/
8. Stoltzfus J. Defining Iron – Defeciency Anemia in Public Health Terms: A time for Reflection. J. Nutr. 2001 Feb [cited 2020 Jun 10]. 131(2):565S-567S. Available from: https://academic.oup.com/jn/article/131/2/565S/4686820
9. Hu JP, Ley SH. Associations of Dietary, Lifestyle and Sociodemographic factors with iron status in Chinese adults: a cross-sectional study in China Health and Nutrition Survey. Am J. Clin Nutr. 2016 Dec [cited 2020 Jun 10]. 105(2):503-512. Available from: https://academic.oup.com/ajcn/article/105/2/503/4633938
10. Rojo RB. Toxqui L. Influence of diet, Menstruation and Genetic factors on iron status: A Cross-Sectional Study in Spanish Women of Childbearing Age. Int, J. Mol. Sci. 2014 Mar [cited 2020 Jun 11]. 15(3):4077-4087. Available from: https://www.mdpi.com/1422-0067/15/3/4077
11. Heitmann BL, Miman N. The relationship between dietary intake, corrected for reporting error and serum ferritin in Danish women aged 35-65 years. British J. Nutr. 1996 Jun [cited 2020 Jun 11]. 75(6)905-913. https://www-cambridge-org.ezproxy.lib.monash.edu.au/core/journals/british-journal-of-nutrition/article/relationship-between-dietary-iron-intake-corrected-for-diet-reporting-error-and-serum-ferritin-in-danish-women-aged-3565-years/1DF8459CAD54B4C96BE068DF65A87925
12. Jackson AA, Margetts BM. Protein Intakes in the adult population of the UK. Int. J. Food Sci. Nutr. 1993 Jul [cited 2020 Jun 11]. 44(2):95-104. Available from: https://www.tandfonline.com/doi/abs/10.3109/09637489309017428
13. He J. Shen X. Associations of predominantly Plant-Based diets and iron status in Chinese adults: a cross sectional analysis. British J. Nutr. 2016 Nov [cited 2020 Jun 11]. 116(9):1621-1632. Available from: https://www.cambridge.org/core/journals/british-journal-of-nutrition/article/association-between-predominantly-plantbased-diets-and-iron-status-in-chinese-adults-a-crosssectional-analysis/A8DF7A46BFCE4F939E681D65EEE6B58D
14. Young I, Parker HM, Rangan A. Association between haeme and non-haeme Iron intake and Serum Ferritin in Healthy Young Women. Nutrients. 2018 Jan [cited 2020 Jun 11]. 10(1):81. Available from: https://www.mdpi.com/2072-6643/10/1/81/htm
15. Preziosi P, Hercberg S. Iron status of a healthy French population: Factors determining biochemical markers. Ann. Nutr. Metab. 1994 [cited 2020 Jun 11]. 1994(38):192-202. Available from: https://www.karger.com/Article/Abstract/177811
16. Tooze JA, Smith SM. The accuracy of the Goldberg method for classifying misreporters of energy intake on a food frequency questionnaire and 24-hour recalls: Comparison with doubly labeled water. Eur J. Clin. Nutr. 2011 Nov [cited 2020 Jun 11]. 66(5):569-576. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3319469/

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