Skip to main content Accessibility help
×
Home
Hostname: page-component-684899dbb8-ndjvl Total loading time: 0.693 Render date: 2022-05-21T00:30:00.107Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "useRatesEcommerce": false, "useNewApi": true }

Body iron is associated with cognitive executive planning function in college women

Published online by Cambridge University Press:  07 June 2012

Cynthia A. Blanton*
Affiliation:
USDA, ARS, Western Human Nutrition Research Center, University of California, Davis, CA, USA
Michael W. Green
Affiliation:
Nutrition and Behaviour Laboratory, School of Life and Health Sciences, Aston University, Birmingham, UK
Mary J. Kretsch
Affiliation:
USDA, ARS, Western Human Nutrition Research Center, University of California, Davis, CA, USA
*
*Corresponding author: C. A. Blanton, fax +1 208 282 3937, E-mail: blancynt@isu.edu
Rights & Permissions[Opens in a new window]

Abstract

Evidence of the relationship between altered cognitive function and depleted Fe status is accumulating in women of reproductive age but the degree of Fe deficiency associated with negative neuropsychological outcomes needs to be delineated. Data are limited regarding this relationship in university women in whom optimal cognitive function is critical to academic success. The aim of the present study was to examine the relationship between body Fe, in the absence of Fe-deficiency anaemia, and neuropsychological function in young college women. Healthy, non-anaemic undergraduate women (n 42) provided a blood sample and completed a standardised cognitive test battery consisting of one manual (Tower of London (TOL), a measure of central executive function) and five computerised (Bakan vigilance task, mental rotation, simple reaction time, immediate word recall and two-finger tapping) tasks. Women's body Fe ranged from − 4·2 to 8·1 mg/kg. General linear model ANOVA revealed a significant effect of body Fe on TOL planning time (P= 0·002). Spearman's correlation coefficients showed a significant inverse relationship between body Fe and TOL planning time for move categories 4 (r − 0·39, P= 0·01) and 5 (r − 0·47, P= 0·002). Performance on the computerised cognitive tasks was not affected by body Fe level. These findings suggest that Fe status in the absence of anaemia is positively associated with central executive function in otherwise healthy college women.

Type
Full Papers
Copyright
Copyright © The Authors 2012

Fe deficiency affects individuals of all ages and classes but to a larger extent infants, children and women of reproductive age(Reference Murray-Kolb1, Reference Stoltzfus2). Evidence of the relationship between Fe status and neuropsychological function in young women is beginning to accumulate(Reference Beard, Hendricks and Perez3Reference Murray-Kolb and Beard9). The WHO estimates that anaemia, the most severe form of Fe deficiency, affects 30 % of women of reproductive age(10), and in the USA, recent reports have indicated that approximately 15 % of females aged 20–49 years are Fe-deficient(Reference Cogswell, Looker and Pfeiffer11). These data emphasise the need to give attention to Fe deficiency in developed as well as developing countries. The present study focuses on women of childbearing age, in particular women in college. The prevalence of Fe deficiency in college women has been reported at 30–51 % when using a serum ferritin cut-off of < 20 μg/l(Reference Fordy and Benton5, Reference Grondin, Ruivard and Perreve12, Reference Wilcox, Wengreen and Schvaneveldt13) and 28–50 % when using criteria of serum ferritin of < 15 μg/l(Reference Houston14Reference Worthington-Roberts, Breskin and Monsen16), or absent or trace bone marrow Fe(Reference Scott and Pritchard17). Fe-deficiency anaemia has been observed in 16–18 % of female university undergraduate students(Reference Pastides18, Reference Sultan19). This population is of particular concern considering the cognitive demands of higher education.

Fe deficiency manifests as alterations in cognitive function, behaviour and mood(Reference Beard and Connor20Reference Lozoff, Jimenez and Smith22). Despite the high rate of Fe deficiency in women of reproductive age and evidence showing that maternal deficiency affects the fetus and child(Reference Christian, Murray-Kolb and Khatry23, Reference Murray-Kolb and Beard24), large gaps exist in this area of investigation. Additional studies in women of reproductive age are needed to elucidate the degree of Fe deficiency that is associated with negative neuropsychological outcomes. The impact of mild or moderate Fe deficiency on cognitive function has not been clearly described. Studies to date in women have found significant relationships only when Fe-deficiency anaemia was present. The present study explores the relationship between body Fe status, in the absence of anaemia, and neuropsychological function in young college women.

Experimental methods

Study design

The present study was performed at the United States Department of Agriculture, Western Human Nutrition Research Center located at the campus of the University of California, Davis, California. The study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving human subjects were approved by the university's Institutional Review Board of the Office of Human Research Protection. Written informed consent was obtained from all participants. The study comprised one laboratory visit, approximately 2 h in duration, preceded by two mornings of at-home saliva collections. On the test morning, women arrived at the laboratory after a 12 h fast and provided samples of blood and urine before height and weight were measured. Blood was tested as described below; urine was tested to confirm the absence of pregnancy. Participants then consumed completely a controlled snack of 18 g whole-grain crispbread crackers (Wasa), 100 g 2 %-fat cottage cheese and 500 ml bottled water. The snack was provided as a standardised countermeasure to negative effects of fasting on cognitive function(Reference Pivik and Dykman25). Following snack consumption, participants completed written questionnaires. At 60 min after completion of the snack, a battery of standardised cognitive tests was administered, lasting approximately 60 min. Saliva samples were collected immediately preceding cognitive testing and following completion of the sustained attention cognitive task.

Participants

Healthy, female undergraduate college students enrolled at the University of California, Davis were recruited over an 18-month period using printed and electronic advertisements. A telephone screening questionnaire was administered on initial contact and qualified women proceeded to an in-laboratory health screening that included anthropometric measurements, blood and urine testing, and self-administered questionnaires. Exclusion criteria included the following: age < 19 or >30 years; BMI >29·9 kg/m2; current pregnancy or pregnancy within the previous year; current lactation; hormonal contraceptive use; smoking; regular high-intensity exercise level; dieting for weight loss; recent history of eating disorders; inflammatory or endocrine disorders; chronic infections; anaemia (Hb < 120 g/l); vitamin B12 or folic acid deficiency; haemolytic anaemia; polycythaemia; haemoglobinopathies; race other than Caucasian; excess alcohol consumption or use of recreational drugs, prescription drugs or herbal preparations that could interfere with Fe absorption and/or affect mental performance. Caucasian, non-obese participants were selected to avoid introducing the confounding effects of race and obesity, respectively, on haematology(Reference Beutler and West26, Reference Neymotin and Sen27). Women were selected because Fe deficiency is less prevalent in men(Reference Fordy and Benton5) and were tested during the luteal phase of their menstrual cycle in order to control for cycle-related changes in cognitive function(Reference Sherwin28) and haematology(Reference Kim, Yetley and Calvo29). Women's progression through the recruitment process is depicted in Fig. 1. The final number of research participants was forty-two.

Fig. 1 Flow diagram of volunteer progression through recruitment and participation. * Health screening consisted of measurements of height, weight, blood pressure, pulse, temperature, a fasting blood draw, a spot urine collection, and completion of six written questionnaires related to health history, physical activity, psychological state and eating behaviour.

Data collection

For the details on haematological and salivary methods, anthropometric measurements, and questionnaires, see Supplementary Material available online at http://www.journals.cambridge.org/bjn

Assessment of iron

Blood from overnight-fasted participants was analysed for complete blood count, serum ferritin and serum soluble transferrin receptor. Serum Fe was measured to test for associations with cognitive function that we have previously reported(Reference Kretsch, Fong and Green8). Body Fe was calculated using the following equation: body Fe (mg/kg body weight) = (log(transferrin receptor/ferritin) − 2·8229)/0·1207(Reference Cook, Flowers and Skikne30). Body Fe was selected as the indicator of Fe status in the present study due to its ability to detect slight differences in Fe levels in non-anaemic individuals that could otherwise be missed by methods of classification utilising multiple defined cut-off values(Reference Zimmermann31). Furthermore, using body Fe as an assessment measure is consistent with the recent adoption of body Fe as an Fe status indicator for the US National Health and Nutrition Examination Surveys(Reference Lynch32, Reference Mei, Cogswell and Looker33) and its recommendation for use by the WHO(34).

Assessment of cognitive performance

Each participant was tested individually, in a session lasting approximately 1 h, under uniform lighting conditions inside a soundproof chamber. A research staff member (C. A. B.) trained in administering the standardised cognitive battery and blinded to participants’ Fe status conducted all testing. The staff member was present with the participant in the chamber throughout testing. The mostly computerised but standardised assessment battery, described in Green et al. (Reference Green, Elliman and Kretsch35), measures multiple modalities of cognitive function and is comprised of the following tasks, presented in the same order across the sessions: planning and working memory (Tower of London (TOL)); sustained attention (Bakan vigilance task); spatial reasoning ability (mental rotation); simple reaction time; immediate verbal memory (verbal free recall); motor performance (two-finger tapping task). A manual version of the TOL task(Reference Shallice36) was the only non-computerised cognitive test used in the present study.

Tower of London

The TOL task was comprised of three movable discs (one green, one white and one black) that sit on three pegs mounted on a wooden board. Participants were required to move the discs from a standard initial position to a goal position within a set number of moves. There were two goal positions that could be achieved in a minimum of two moves, two that could be achieved in three moves, four that could be achieved in four moves, and four that could be achieved in five moves. Participants were instructed to plan mentally the entire sequence of moves before executing the sequence. A stopwatch was used to measure planning and total time for each trial. Planning time began when the participant was presented with the diagrammed goal position and ended when the participant first touched a disc. Total time encompassed planning through achievement of the goal position. Participants successfully completed three practice trials before beginning the experimental trials.

Bakan task

This sustained attention task was a 6 min visual analogue of the procedure developed by Bakan(Reference Bakan37) in which participants were presented with a continuous stream of single numbers (1–9) that appeared one at a time in the centre of the computer screen. Participants were asked to press a response key as soon as they detected a target sequence of three consecutive odd or even numbers.

Mental rotation

This task assessed the visual–spatial component of working memory. Participants were presented with a number of pairs of random geometric forms, with each pair being presented simultaneously. The task was to mentally rotate the form on the right side of the screen to make it identical in orientation to the form on the left side of the screen. Participants then were to decide whether the form on the right side was a duplicate or a mirrored image of the form on the left. They were instructed to press the 1 or 2 key on a keyboard to indicate duplicate or mirrored image, respectively.

Simple reaction time

Participants were presented with 100 reaction time trials with instructions to press the space bar on a keyboard as quickly as they detected a single star appearing in the centre of the computer screen.

Verbal free recall

Participants were presented with two lists of twenty words each, one list being presented at 1 s per word and the other at 2 s per word. Immediately after the presentation of each list, participants were allowed 4 min to recall as many words as possible.

Two-finger tapping

This task of motor speed required participants to alternatively tap the 1 and 2 keys of the keyboard as quickly as possible, using the index and second finger of their dominant hand.

Assessment of covariates

Serum C-reactive protein was measured to test for inflammatory status, which can alter Fe status indicators(Reference Hulthen, Lindstedt and Lundberg38). Serum plasma Zn was measured to test for associations with cognitive function that we have previously reported(Reference Kretsch, Fong, Penland, Roussel, Anderson and Favrier39). Salivary cortisol was measured to assess the association of corticosteroid levels, an index of stress, with cognitive performance(Reference Green, Elliman and Kretsch35). Saliva samples were obtained using the Salivette device (Sarstedt), consisting of a cotton swab and plastic vial. On each of the 2 d preceding the laboratory appointment, participants were instructed to collect two saliva samples at home: the first upon waking from night-time sleep and the second, 30 min after collecting the first sample. During the laboratory appointment, two additional saliva samples were collected: the first immediately preceding cognitive testing and the second, 30 min after completion of the Bakan task. The mean cortisol level of samples from days 1 and 2 preceding cognitive testing was calculated for each time point for each participant.

Participants completed five questionnaires during the 60 min interim between snack consumption and cognitive testing: the Beck Depression Inventory-II(Reference Beck, Ward and Mendelson40); the Perceived Stress Scale (PSS10)(Reference Cohen, Kamarck and Mermelstein41); the State-Trait Anxiety Inventory(Reference Spielberger, Gorsuch and Luschene42); the Block Food-Frequency Questionnaire (Block 98 FFQ)(Reference Block, Hartman and Dresser43, 44); a menstrual information questionnaire. Questionnaires were self-administered but checked for completeness upon submission.

Statistical analyses

Statistical analyses were performed using IBM SPSS version 19.0 (2010). The logarithm (base 10) of planning time for the four levels (two, three, four and five moves) of the TOL task was compared using a general linear model. Log-transformed data were used since the raw data were significantly right-skewed. The continuous between-subjects factor was body Fe. The within-subjects factors were the four TOL levels and the interaction of TOL level and body Fe. This type of analysis is a repeated-measures ANOVA with a covariate allowing for different slopes for the different levels of the within-subjects factor. For tests of correlation, variables for Fe status and the computerised tasks were examined for normality using the Kolmogorov–Smirnov test with the Lilliefors correction. Pearson's product–moment correlation coefficient is reported when both variables fit a normal distribution. Spearman's rank correlation coefficient is reported when either one or both of the variables do not fit a normal distribution. Correlations between salivary cortisol levels and each of the following were also examined: body Fe; cognitive test scores; behavioural questionnaire scores. The significance level was set at P< 0·05 and quoted levels are two-sided.

Results

Demographic and haematological measurements

Body Fe ranged from − 4·2 to 8·1 mg/kg (Table 1), with five women (12 % of the participants) having values < 0 mg/kg. Of the study participants, seven women, including the five with negative body Fe, had abnormal serum ferritin ( < 12 μg/l)(Reference Sauberlich45) and eight women had abnormal transferrin receptor (>8·3 mg/l) levels. Serum Fe was below normal ( < 500 μg/l)(Reference Sauberlich45) in four women, two of whom had negative body Fe. The results are shown in Table 1.

Table 1 Sample characteristics (n 42)(Mean values, standard deviations, medians, quartiles and ranges)

Q1, quartile 1; Q3, quartile 3; BDI-II, Beck Depression Inventory-II; STAI, State-Trait Anxiety Inventory.

* The STAI(Reference Spielberger, Gorsuch and Luschene42) consists of separate self-report scales that measure the intensity and frequency of state (at this moment) and trait (general) anxiety. Both scales were administered before cognitive testing and the state scale was repeated 30 min after completion of the Bakan task.

Cognitive performance

Tower of London

The analysis revealed significant main effects of body Fe (F(1,40) = 11·1, P= 0·002) and TOL move category (F(2·5,99) = 60·1, P= 0·0001) on planning time. The interaction of body Fe and TOL move category on planning time approached but did not reach significance (P= 0·057). The estimated slope of body Fe against planning time became steeper as task difficulty increased; that is, as the target move category advanced from 2 to 5, body Fe had a more pronounced effect on planning speed (B= − 0·02, − 0·02, − 0·04, − 0·05; P= 0·07, 0·07, 0·01, 0·002 for categories 2, 3, 4 and 5, respectively). Spearman's correlations showed significant inverse relationships between body Fe and planning time for move categories 4 and 5 (Table 2; Fig. 2). Correlations for move categories (2 and 3) approached but did not reach significance (P= 0·07 for both). Serum ferritin and serum Fe were also significantly negatively correlated with planning time for target move categories 4 and 5. Serum transferrin receptor level, which rises in Fe deficiency, was significantly positively correlated with planning time for move categories 2 and 4. In contrast to planning time associations, Fe status as measured by body Fe, transferrin receptor or ferritin did not affect the participants’ total task execution time or their ability to complete TOL tasks within the allowed number of moves.

Table 2 Relationship between iron status measures and Tower of London (TOL) planning time (n 42 women)†

TfR, serum soluble transferrin receptor.

P< 0·05; ** P< 0·01.

Spearman's correlation coefficients for associations between Fe status measures and TOL planning times for target move categories 2–5.

Fig. 2 Scatter plot and regression equation of the relationship between log Tower of London planning time for five-move level and body iron (n 42). Slope: − 0·047; SE slope: 0·01, P= 0·001; adjusted R2= 0·205.

Plasma Zn level was not correlated with performance on the TOL task or any computerised cognitive test.

Bakan vigilance task

Fe status was not significantly correlated with the number of correct or incorrect hits per min. There was a significant effect of block on the number of correct (P= 0·0001) and incorrect (P= 0·01) responses for all women; that is, across progressive blocks 1–6, women made fewer correct hits and more incorrect hits.

Mental rotation

Fe status did not have a significant effect on the number of correct decisions made across rotation angle blocks.

Simple reaction time

Reaction time to a stimulus was not correlated with Fe status. There was a significant (P= 0·0001) effect of block on reaction time: across blocks 1–5, reaction time slowed for all women.

Verbal free recall

Recall of words presented for 1 and 2 min was not affected by Fe status. There was a significant (P= 0·03) effect of presentation time on recall: women recalled more words presented for 2 v. 1 s.

Tapping

Body Fe, transferrin receptor and ferritin were not significantly correlated with two-finger tapping speed. A significant correlation was found between serum Fe and tapping time. That is, higher serum Fe was associated with slower tapping speed (r 0·35, P= 0·04). There was a significant effect of trial block (P= 0·0001), with progressive slowing in speed over the course of the task. This rate of slowing was not affected by Fe status.

Covariates

Serum C-reactive protein concentrations were within normal limits for all participants, supporting the absence of a confounding effect of inflammatory or infectious processes on the interpretation of Fe status measures. Plasma Zn was below normal ( < 8·5 μg/l)(Reference Sauberlich45) in twenty-seven women (64 % of the participants).

Salivary cortisol levels measured on the 2 d before cognitive testing and during the test session were not affected by Fe status. Salivary cortisol levels increased significantly (P= 0·04) in all women, regardless of Fe status, between waking and 30 min after waking during at-home measurements (data not shown). A significant (P= 0·03) decrease in salivary cortisol between baseline and 30 min post-Bakan task on the cognitive testing day was seen in all women without regard to Fe status.

Menstrual cycle information provided by women (usual cycle length and flow rate) showed no relationship with Fe status. Women self-reported similar (within normal range) levels of perceived stress and trait anxiety; however, state (i.e. at this moment) anxiety measured by the State-Trait Anxiety Inventory immediately before the cognitive test session was significantly positively correlated with body Fe (r 0·30, P= 0·03). No other psychological questionnaire score showed a significant association with body Fe. The scores are listed in Table 1.

During the study, seven FFQ measured energy intakes outside the range and were excluded from the analyses. There were no significant correlations between body Fe and any dietary component analysed on an absolute or energy-adjusted (per 4186 kJ (1000 kcal)) basis (data not shown). Nor did the number of daily servings of any food group, including that containing meat, beans/legumes and eggs (Fe-rich foods), or use of dietary supplements differ according to Fe status.

Discussion

The present study contributes new information on the degree of Fe deficiency associated with negative neuropsychological effects in women of reproductive age. We report significant slowing in planning speed during a test of central executive function in college women with reduced body Fe but not anaemia. College women experience Fe depletion at a higher rate than the general population of pre-menopausal women(Reference Fordy and Benton5, Reference Houston14, Reference Looker, Dallman and Carroll46); therefore, it is important to investigate the impact of low Fe status on cognitive function in this subgroup in whom optimal cognition is critical to academic success. The present findings are consistent with the results from previous studies(Reference Beard, Hendricks and Perez3Reference Murray-Kolb and Beard9) performed in women of reproductive age and extend those findings to young college women without anaemia. Kretsch et al. (Reference Kretsch, Fong and Green8) observed an association between performance on a test of sustained attention and a decline in Hb and transferrin saturation in obese women consuming a 50 % energy-restricted diet supplemented to provide a complete nutritional profile. Interestingly, the present study did not observe an impact of body Fe on the performance of this same task of sustained attention. Reasons for the dissimilar findings might be related to differences between participant populations, particularly in the level of tissue Fe stores. Neither group of women had Hb values < 120 g/l, but body Fe was not measured in the previous study, making comparisons of Fe status between the groups difficult.

Murray-Kolb & Beard(Reference Murray-Kolb and Beard9) demonstrated statistically significant differences in scores on the computerised cognitive abilities tests(Reference Detterman47) between the Fe-sufficient and anaemic groups, but not between the Fe-sufficient and Fe-deficient non-anaemic groups. However, a significant trend for coincident declines in cognitive test performance and Fe status category was observed with the Fe-deficient non-anaemic group scores intermediate to the Fe-sufficient and Fe-deficient anaemic groups. The fact that cognitive performance on the computerised test batteries was not significantly affected by Fe status in both the present study and that conducted by Murray-Kolb & Beard suggests that many components of cognition remain intact in preclinical Fe deficiency. It is perhaps important that the present study revealed lower body Fe-related impairment only in planning time for the TOL task, a measure of central executive function. This might indicate a particular sensitivity of the TOL task to detect subtle changes in working memory planning speed, although the finding warrants replication.

Sensitivity of the TOL task to capture alterations in planning ability in relation to exposure to Fe deficiency is supported by a study by Lukowski et al. (Reference Lukowski, Koss and Burden48). Male and female participants (n 114) were followed from age 12 to 23 months, with measurements of Fe status repeated at ages 5, 11–14 and 19 years, and cognitive function tested at age 19 years. In the follow-up, 95 % of the 19-year-old participants were Fe-sufficient. Participants who as infants had Fe-deficiency anaemia or Fe deficiency that did not correct after 3 months of Fe therapy showed impaired performance on tests of planning, recognition memory, inhibitory control and set-shifting at age 19 years. Planning ability was assessed using Stockings of Cambridge, a computerised form of the TOL. Similar to the present results, group differences in planning ability were found for the highest difficulty (five-move) problems. Together, these findings support both a long-term developmental effect and an acute effect of Fe deficiency on TOL/Stockings of Cambridge performance.

Fe status was assessed in the present study using a calculation of body Fe based on the ratio of serum transferrin receptor:ferritin(Reference Cook, Flowers and Skikne30). Whereas anaemia screening in populations is commonly performed using multiple haematological measurements and defined cut-off values, this more recently developed method expresses body Fe per unit body weight, thus improving the precision of evaluating Fe status in individuals. Body Fe was recently adopted as an Fe status indicator for the US National Health and Nutrition Examination Surveys(Reference Lynch32, Reference Mei, Cogswell and Looker33) and has been recommended for use by the WHO(34). Body Fe measurement has shown sensitivity in identifying slight differences in Fe status that are not detected by other blood tests. In an Fe supplementation intervention trial in pregnant Jamaican women, body Fe was significantly higher in women provided with 100 v. 50 mg iron sulphate daily for 3 months; however, no group differences were seen when assessed by Hb or transferrin receptor measures(Reference Cook, Flowers and Skikne30). Body Fe calculation has also shown sensitivity in detecting gradual improvements in tissue Fe resulting from 6 months of 10 mg/d Fe fortification(Reference Cook, Flowers and Skikne30). In the present study, the occurrence of Fe deficiency, defined as body Fe < 0 mg/kg, was 12 %. This is similar to the prevalence of 9·2 (sd 1·6) % of non-pregnant women aged 20–49 years with < 0 mg/kg body Fe observed in the US National Health and Nutrition Examination Survey 2003–2006(Reference Cogswell, Looker and Pfeiffer11). The present finding contrasts substantially, however, with reported Fe deficiency rates of 30–50 % in college women(Reference Fordy and Benton5, Reference Grondin, Ruivard and Perreve12Reference Scott and Pritchard17) when serum ferritin serves as the index of Fe status. The lower estimation of Fe deficiency by body Fe v. the ferritin model (where Fe deficiency is defined as two or all three abnormal concentrations of ferritin, transferrin saturation or erythrocyte protoporphyrin) has been reported previously(Reference Cogswell, Looker and Pfeiffer11, Reference Mei, Cogswell and Looker33) and is thought to result from greater sensitivity of the body Fe measure(Reference Lynch32).

Salivary cortisol was measured in the present study in order to test whether Fe status-related changes in cognitive function were correlated with stress, but no significant effect of Fe status on cortisol levels was observed. Neither was there a consistent pattern across behavioural questionnaire scores showing a relationship between body Fe and stress or depression. Nonetheless, this is an area of study worthy of pursuit since Fe treatment shows efficacy in alleviating self-reported fatigue in Fe-depleted pre-menopausal women(Reference Krayenbuehl, Battegay and Breymann49). Fatigue manifests in part as cognitive impairment(Reference Dawson50).

Food-frequency measurements were collected to determine whether dietary intake was related to body Fe. A lower intake of Fe-rich foods by women with lower v. higher body Fe might be expected, but no differences in any nutrient measure were found. Nor was Fe supplementation different across the participant groups since this was an exclusionary study criterion. It is possible that dietary behaviours that influence Fe absorption, such as co-ingestion of enhancers and inhibitors of Fe, were not detected by the questionnaire. Peneau et al. (Reference Peneau, Dauchet and Vergnaud51) found a positive association between serum ferritin and intakes of fibre-poor fruits, vegetables and juices in pre-menopausal women. Notably, these investigators measured dietary intake using multiple 24 h recalls over a 2-year period, which yielded considerably greater detail on dietary habits than the FFQ used in the present study. Yokoi et al. (Reference Yokoi, Alcock and Sandstead52) also reported that dietary beef intake was a positive predictor and bran breakfast cereal consumption was a negative predictor of serum ferritin.

The strengths of the present study include the use of body Fe to measure Fe status across a continuum and selection of a participant population similar in factors known to affect cognitive task performance including education level, age, sex, drug and alcohol use, and menstrual phase(Reference Sherwin28, Reference Bell, Willson and Wilman53Reference Schweizer and Vogel-Sprott57). The study's design also minimised confounding by testing all participants at the same time in the morning and under standard conditions of feeding status (overnight fast followed by a standard snack before testing) and test administrator (C. A. B.) using a standardised cognitive test battery. However, these defined participant characteristics and experimental design features limit the findings’ scope of inference. This was an observational study intended to build existing knowledge, and its results neither imply causation nor claim to be definitive. Further, the sample size was modest and included only a small percentage of women with a tissue Fe deficit. Another important consideration is that only non-obese women were included in the present study. While this inclusion criteria eliminated the confounding effect of obesity on Fe status assessment(Reference Cheng, Bryant and Cook58), it does not allow application of the study's findings to an estimated 6 % of college women classified as obese(Reference Morrell, Lofgren and Burke59). Lastly, the ecological validity of standardised cognitive tests, such as the TOL, remains unconfirmed(Reference Van der Elst, Van Boxtel and Van Breukelen60). This question is being actively investigated with the use of virtual environments(Reference Kalpouzos, Eriksson and Sjolie61, Reference Parsons, Courtney and Arizmendi62). Virtual reality employed as an assessment tool would seem to lend itself to elucidating the real-life functional implications of reduced Fe status.

In conclusion, the present study showed a significant association between cognitive planning ability and body Fe while performing the TOL task of central executive function in non-anaemic college women. This finding extends the relatively recent recognition that Fe deficiency with anaemia impairs cognition in women of reproductive age(Reference Beard, Hendricks and Perez3, Reference Murray-Kolb and Beard9) to the possibility that reduced Fe status without anaemia can also underlie this functional impairment. Confirmation that preclinical Fe deficiency is indeed causative is contingent upon reversal of cognitive impairment with body Fe repletion. The present study together with our earlier findings(Reference Kretsch, Fong and Green8) and those of other investigators(Reference Beard, Hendricks and Perez3Reference Khedr, Hamed and Elbeih7, Reference Murray-Kolb and Beard9) challenge the paradigm that deleterious functional consequences of Fe deficiency occur primarily in developing children and adolescents and not in adults.

Acknowledgements

This study was supported by United States Department of Agriculture Agricultural Research Service Cooperative Research Information System (ARS CRIS) 5306-51520-006-00D. M. J. K. and M. W. G. were responsible for the conception and design of the study, and writing of the manuscript. C. A. B. was responsible for the collection of the data, the analysis of the data, and the writing of the manuscript. None of the authors had any conflict of interest.

References

1Murray-Kolb, LE (2011) Iron status and neuropsychological consequences in women of reproductive age: what do we know and where are we headed? J Nutr 141, 747S755S.CrossRefGoogle ScholarPubMed
2Stoltzfus, RJ (2011) Iron interventions for women and children in low-income countries. J Nutr 141, 756S762S.CrossRefGoogle ScholarPubMed
3Beard, JL, Hendricks, MK, Perez, EM, et al. (2005) Maternal iron deficiency anemia affects postpartum emotions and cognition. J Nutr 135, 267272.CrossRefGoogle ScholarPubMed
4Foley, D, Hay, DA & Mitchell, RJ (1986) Specific cognitive effects of mild iron deficiency and associations with blood polymorphisms in young adults. Ann Hum Biol 13, 417425.CrossRefGoogle ScholarPubMed
5Fordy, J & Benton, D (1994) Does low iron status influence psychological functioning? J Hum Nutr Diet 7, 127133.CrossRefGoogle Scholar
6Groner, JA, Holtzman, NA, Charney, E, et al. (1986) A randomized trial of oral iron on tests of short-term memory and attention span in young pregnant women. J Adolesc Health Care 7, 4448.CrossRefGoogle ScholarPubMed
7Khedr, E, Hamed, SA, Elbeih, E, et al. (2008) Iron states and cognitive abilities in young adults: neuropsychological and neurophysiological assessment. Eur Arch Psychiatry Clin Neurosci 258, 489496.CrossRefGoogle ScholarPubMed
8Kretsch, MJ, Fong, AK, Green, MW, et al. (1998) Cognitive function, iron status, and hemoglobin concentration in obese dieting women. Eur J Clin Nutr 52, 512518.CrossRefGoogle ScholarPubMed
9Murray-Kolb, LE & Beard, JL (2007) Iron treatment normalizes cognitive functioning in young women. Am J Clin Nutr 85, 778787.CrossRefGoogle ScholarPubMed
10WHO (2008) Worldwide Prevalence of Anaemia 1993–2005. Geneva: World Health Organization.Google Scholar
11Cogswell, ME, Looker, AC, Pfeiffer, CM, et al. (2009) Assessment of iron deficiency in US preschool children and nonpregnant females of childbearing age: National Health and Nutrition Examination Survey 2003–2006. Am J Clin Nutr 89, 13341342.CrossRefGoogle ScholarPubMed
12Grondin, MA, Ruivard, M, Perreve, A, et al. (2008) Prevalence of iron deficiency and health-related quality of life among female students. J Am Coll Nutr 27, 337341.CrossRefGoogle ScholarPubMed
13Wilcox, C, Wengreen, H & Schvaneveldt, NB (2004) Iron deficiency in young-adult women attending Utah State University. J Am Diet Assoc 104, Suppl. 2, A-27.Google Scholar
14Houston, MS (1997) Lifestyle and dietary practices influencing iron status in university women. Nutr Res 17, 922.CrossRefGoogle Scholar
15Mehta, BC (2004) Iron deficiency among nursing students. Indian J Med Sci 58, 389392.Google ScholarPubMed
16Worthington-Roberts, BS, Breskin, MW & Monsen, ER (1988) Iron status of premenopausal women in a university community and its relationship to habitual dietary sources of protein. Am J Clin Nutr 47, 275279.CrossRefGoogle Scholar
17Scott, DE & Pritchard, JA (1967) Iron deficiency in healthy young college women. JAMA 199, 897900.CrossRefGoogle ScholarPubMed
18Pastides, H (1981) Iron deficiency anemia among three groups of adolescents and young adults. Yale J Biol Med 54, 265271.Google ScholarPubMed
19Sultan, AH (2007) Anemia among female college students attending the University of Sharjah, UAE. J Egypt Public Health Assoc 82, 261271.Google ScholarPubMed
20Beard, JL & Connor, JR (2003) Iron status and neural functioning. Annu Rev Nutr 23, 4158.CrossRefGoogle ScholarPubMed
21Lozoff, B, Beard, J, Connor, J, et al. (2006) Long-lasting neural and behavioral effects of iron deficiency in infancy. Nutr Rev 64, S34S43, (discussion S72–S91).CrossRefGoogle ScholarPubMed
22Lozoff, B, Jimenez, E & Smith, JB (2006) Double burden of iron deficiency in infancy and low socioeconomic status: a longitudinal analysis of cognitive test scores to age 19 years. Arch Pediatr Adolesc Med 160, 11081113.CrossRefGoogle ScholarPubMed
23Christian, P, Murray-Kolb, LE, Khatry, SK, et al. (2010) Prenatal micronutrient supplementation and intellectual and motor function in early school-aged children in Nepal. JAMA 304, 27162723.CrossRefGoogle ScholarPubMed
24Murray-Kolb, LE & Beard, JL (2009) Iron deficiency and child and maternal health. Am J Clin Nutr 89, 946S950S.CrossRefGoogle ScholarPubMed
25Pivik, RT & Dykman, RA (2007) Event-related variations in alpha band activity during an attentional task in preadolescents: effects of morning nutrition. Clin Neurophysiol 118, 615632.CrossRefGoogle ScholarPubMed
26Beutler, E & West, C (2005) Hematologic differences between African-Americans and whites: the roles of iron deficiency and alpha-thalassemia on hemoglobin levels and mean corpuscular volume. Blood 106, 740745.CrossRefGoogle ScholarPubMed
27Neymotin, F & Sen, U (2011) Iron and obesity in females in the United States. Obesity (Silver Spring) 19, 191199.CrossRefGoogle ScholarPubMed
28Sherwin, BB (2003) Estrogen and cognitive functioning in women. Endocr Rev 24, 133151.CrossRefGoogle ScholarPubMed
29Kim, I, Yetley, EA & Calvo, MS (1993) Variations in iron-status measures during the menstrual cycle. Am J Clin Nutr 58, 705709.CrossRefGoogle ScholarPubMed
30Cook, JD, Flowers, CH & Skikne, BS (2003) The quantitative assessment of body iron. Blood 101, 33593364.CrossRefGoogle ScholarPubMed
31Zimmermann, MB (2008) Methods to assess iron and iodine status. Br J Nutr 99, Suppl. 3, S2S9.CrossRefGoogle ScholarPubMed
32Lynch, S (2011) Improving the assessment of iron status. Am J Clin Nutr 93, 11881189.CrossRefGoogle ScholarPubMed
33Mei, Z, Cogswell, ME, Looker, AC, et al. (2011) Assessment of iron status in US pregnant women from the National Health and Nutrition Examination Survey (NHANES), 1999–2006. Am J Clin Nutr 93, 13121320.CrossRefGoogle ScholarPubMed
34WHO/CDC (2004) Assessing the iron status of populations. In A Report of a Joint World Health Organization/Centers for Disease Control and Prevention Technical Consultation on the Assessment of Iron Status at the Population Level. Geneva: WHO/CDC.Google Scholar
35Green, MW, Elliman, NA & Kretsch, MJ (2005) Weight loss strategies, stress, and cognitive function: supervised versus unsupervised dieting. Psychoneuroendocrinol 30, 908918.CrossRefGoogle ScholarPubMed
36Shallice, T (1982) Specific impairments of planning. Philos Trans R Soc Lond B Biol Sci 298, 199209.CrossRefGoogle ScholarPubMed
37Bakan, P (1959) Extroversion–introversion and improvement in an auditory vigilance task. Br J Psych 50, 325332.CrossRefGoogle Scholar
38Hulthen, L, Lindstedt, G, Lundberg, PA, et al. (1998) Effect of a mild infection on serum ferritin concentration–clinical and epidemiological implications. Eur J Clin Nutr 52, 376379.CrossRefGoogle ScholarPubMed
39Kretsch, MJ, Fong, AK, Penland, JG, et al. (2000) Cognitive effects of adaptation to a low zinc diet in healthy men. In Trace Elements in Man and Animals [Roussel, AM, Anderson, RA and Favrier, AE, editors]. New York, NY: Kluwer Academic/Plenum.Google Scholar
40Beck, AT, Ward, CH, Mendelson, M, et al. (1961) An inventory for measuring depression. Arch Gen Psychiatry 4, 561571.CrossRefGoogle ScholarPubMed
41Cohen, S, Kamarck, T & Mermelstein, R (1983) A global measure of perceived stress. J Health Soc Behav 24, 385396.CrossRefGoogle ScholarPubMed
42Spielberger, CD, Gorsuch, RL, Luschene, R, et al. (1983) Manual for the State-Trait Anxiety Inventory. Palo Alto, CA: Consulting Psychologists Press.Google Scholar
43Block, G, Hartman, AM, Dresser, CM, et al. (1986) A data-based approach to diet questionnaire design and testing. Am J Epidemiol 124, 453469.CrossRefGoogle Scholar
44 NutritionQuest. (2011) NutritionQuest. http://www.nutritionquest.com/assessment/list-of-questionnaires-and-screeners (accessed 21 May 2012).Google Scholar
45Sauberlich, HE (1999) Assessment of Nutritional Status, 2nd ed.Boca Raton: CRC Press.Google Scholar
46Looker, A, Dallman, P, Carroll, M, et al. (1997) Prevalence of iron deficiency in the United States. JAMA 277, 973976.CrossRefGoogle ScholarPubMed
47Detterman, DK (1990) CAT. Computerized cognitive abilities tests for research and teaching. Micro Psychol Network 4, 5162.Google Scholar
48Lukowski, AF, Koss, M, Burden, MJ, et al. (2010) Iron deficiency in infancy and neurocognitive functioning at 19 years: evidence of long-term deficits in executive function and recognition memory. Nutr Neurosci 13, 5470.CrossRefGoogle Scholar
49Krayenbuehl, PA, Battegay, E, Breymann, C, et al. (2011) Intravenous iron for the treatment of fatigue in nonanemic, premenopausal women with low serum ferritin concentration. Blood 118, 32223227.CrossRefGoogle ScholarPubMed
50Dawson, D (2012) Fatigue research in 2011: from the bench to practice. Accid Anal Prev 45, 15.CrossRefGoogle Scholar
51Peneau, S, Dauchet, L, Vergnaud, AC, et al. (2008) Relationship between iron status and dietary fruit and vegetables based on their vitamin C and fiber content. Am J Clin Nutr 87, 12981305.CrossRefGoogle ScholarPubMed
52Yokoi, K, Alcock, NW & Sandstead, HH (1994) Iron and zinc nutriture of premenopausal women: associations of diet with serum ferritin and plasma zinc disappearance and of serum ferritin with plasma zinc and plasma zinc disappearance. J Lab Clin Med 124, 852861.Google ScholarPubMed
53Bell, EC, Willson, MC, Wilman, AH, et al. (2006) Males and females differ in brain activation during cognitive tasks. Neuroimage 30, 529538.CrossRefGoogle ScholarPubMed
54Glymour, MM, Kawachi, I, Jencks, CS, et al. (2008) Does childhood schooling affect old age memory or mental status? Using state schooling laws as natural experiments. J Epidemiol Community Health 62, 532537.CrossRefGoogle ScholarPubMed
55Jager, G, de Win, MM, van der Tweel, I, et al. (2008) Assessment of cognitive brain function in ecstasy users and contributions of other drugs of abuse: results from an FMRI study. Neuropsychopharmacol 33, 247258.CrossRefGoogle ScholarPubMed
56Kumar, A, Rakitin, BC, Nambisan, R, et al. (2008) The response-signal method reveals age-related changes in object working memory. Psychol Aging 23, 315329.CrossRefGoogle ScholarPubMed
57Schweizer, TA & Vogel-Sprott, M (2008) Alcohol-impaired speed and accuracy of cognitive functions: a review of acute tolerance and recovery of cognitive performance. Exp Clin Psychopharmacol 16, 240250.CrossRefGoogle ScholarPubMed
58Cheng, HL, Bryant, C, Cook, R, et al. (2012) The relationship between obesity and hypoferraemia in adults: a systematic review. Obes Rev 13, 150161.CrossRefGoogle ScholarPubMed
59Morrell, JS, Lofgren, IE, Burke, JD, et al. (2012) Metabolic syndrome, obesity, and related risk factors among college men and women. J Am Coll Health 60, 8289.CrossRefGoogle ScholarPubMed
60Van der Elst, W, Van Boxtel, MP, Van Breukelen, GJ, et al. (2008) A large-scale cross-sectional and longitudinal study into the ecological validity of neuropsychological test measures in neurologically intact people. Arch Clin Neuropsychol 23, 787800.CrossRefGoogle ScholarPubMed
61Kalpouzos, G, Eriksson, J, Sjolie, D, et al. (2010) Neurocognitive systems related to real-world prospective memory. PLoS One 5, e13304.CrossRefGoogle ScholarPubMed
62Parsons, TD, Courtney, CG, Arizmendi, B, et al. (2011) Virtual Reality Stroop Task for neurocognitive assessment. Stud Health Technol Inform 163, 433439.Google ScholarPubMed
63Beck, AT, Steer, RA & Brown, GK (1996) Manual for the Beck Depression Inventory-II. San Antonio, TX: The Psychological Corporation.Google Scholar
Figure 0

Fig. 1 Flow diagram of volunteer progression through recruitment and participation. * Health screening consisted of measurements of height, weight, blood pressure, pulse, temperature, a fasting blood draw, a spot urine collection, and completion of six written questionnaires related to health history, physical activity, psychological state and eating behaviour.

Figure 1

Table 1 Sample characteristics (n 42)(Mean values, standard deviations, medians, quartiles and ranges)

Figure 2

Table 2 Relationship between iron status measures and Tower of London (TOL) planning time (n 42 women)†

Figure 3

Fig. 2 Scatter plot and regression equation of the relationship between log Tower of London planning time for five-move level and body iron (n 42). Slope: − 0·047; SE slope: 0·01, P= 0·001; adjusted R2= 0·205.

Supplementary material: PDF

Blanton supplementary material

Appendix

Download Blanton supplementary material(PDF)
PDF 105 KB
You have Access
29
Cited by

Save article to Kindle

To save this article to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Body iron is associated with cognitive executive planning function in college women
Available formats
×

Save article to Dropbox

To save this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. Find out more about saving content to Dropbox.

Body iron is associated with cognitive executive planning function in college women
Available formats
×

Save article to Google Drive

To save this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Google Drive account. Find out more about saving content to Google Drive.

Body iron is associated with cognitive executive planning function in college women
Available formats
×
×

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *