Next Article in Journal
Effects of Vitamin D Status and Supplements on Anthropometric and Biochemical Indices in a Clinical Setting: A Retrospective Study
Previous Article in Journal
Social Support, but Not Perceived Food Environment, Is Associated with Diet Quality in French-Speaking Canadians from the PREDISE Study
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Nutrients
Volume 11
Issue 12
10.3390/nu11123031
nutrients-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Evaluation of a Novel Tool for Screening Inadequate Food Intake in Age-Related Macular Degeneration Patients

by
Diana Tang
1,
Paul Mitchell
1,
Gerald Liew
1,
George Burlutsky
1,
Victoria Flood
2,3 and
Bamini Gopinath
1,*
1
Centre for Vision Research, Department of Ophthalmology and Westmead Institute for Medical Research, University of Sydney, Sydney, NSW 2006, Australia
2
Faculty of Health Sciences, University of Sydney, Sydney 2006, NSW 2151, Australia
3
Westmead Hospital, Western Sydney Local Health District, Westmead 2151, NSW 2151, Australia
*
Author to whom correspondence should be addressed.
Nutrients 2019, 11(12), 3031; https://doi.org/10.3390/nu11123031
Submission received: 11 November 2019 / Revised: 2 December 2019 / Accepted: 10 December 2019 / Published: 12 December 2019
Browse Figures
Review Reports Versions Notes

Abstract

:
Diet assessment tools provide valuable nutrition information in research and clinical settings. With growing evidence supporting dietary modification to delay development and progression of age-related macular degeneration (AMD), an AMD-specific diet assessment tool could encourage eye-care practitioners to refer patients in need of further dietary behavioural support to a dietitian and/or support network. Therefore, the aim of this study was to evaluate clinical use of a novel, short dietary questionnaire (SDQ-AMD) to screen for inadequate food intake in AMD patients by comparing it against a validated food frequency questionnaire (FFQ). Recruitment sources included Sydney-based private eye clinics and research databases (N = 155; 57% female; 78 ± 8 years). Scoring criteria based on the Australian Dietary Guidelines and dietary recommendations for AMD in literature were developed and applied to dietary data from the FFQ and SDQ-AMD. Bland–Altman plot of difference suggests agreement between the FFQ and SDQ-AMD as most mean difference scores were within the 95% CI (6.91, −9.94), and no significant bias between the scores as the mean score increased ((regression equation: y = 0.11x − 2.60) (95% CI: −0.058, 0.275, p-value = 0.20)). Scores were also significantly correlated (0.57, p ≤ 0.0001). The SDQ-AMD shows potential as a diet screening tool for clinical use, however, additional studies are warranted to validate the SDQ-AMD.

1. Introduction

Diet assessment methods provide valuable nutrition information in research and clinical settings. A number of tools are available for use, including recall surveys of usual intake (e.g., Food Frequency Questionnaire (FFQ)) or recent intake (e.g., 24 h recalls); weighed food records/diaries; and diet histories [1]. FFQs have been the preferred method in many large epidemiological studies for cost-effectiveness, option for self-administration, and representation of long-term usual intake [1]. One example of its use was in the Blue Mountains Eye Study (BMES), a landmark population cohort study investigating eye diseases in older Australians living in the Blue Mountains region. The study assessed usual dietary intake in the previous 12 months using a 145-item semiquantitative FFQ that had been validated against weighed food records [2,3,4]. In clinical practice however, a long FFQ may not be a viable tool due to the time-consuming and tedious nature of the questionnaire, especially amongst those with a vision impairment. Therefore, the development of a shorter tool such as a short recall tool that captures actual intake over a specified time period (e.g., 24 h, one week) may be more practical and appropriate in this setting [1].
In age-related macular degeneration (AMD), growing evidence supports that modifying risk factors such as poor diet can delay the development and progression of AMD [5]. Dietary recommendations for AMD also overlap with healthy eating practices that would benefit other common chronic conditions, including obesity, diabetes and cardiovascular diseases. However, the latest Australian National Health Survey (NHS) indicated that older adults continue to show poor diet adequacy, with 91.8% of adults over 65 years not meeting the dietary recommendations for fruit and vegetables [6]. This highlights a need for new approaches to encourage healthy diet practices in older Australians not only for the management of AMD but also for other chronic conditions. As eye-care practitioners are one of the first points of contact for a patient when it comes to AMD, research into developing a rapid and easy dietary tool for use by eye-care practitioners is warranted. A short dietary questionnaire (SDQ-AMD) that has been modified from a validated survey [7] could assist eye-care practitioners with screening patients’ overall diets for inadequate food intake and identifying those in need of dietary behavioural support via dietitian referral, where appropriate, and/or patient support networks such as the Macular Disease Foundation Australia (MDFA)—an Australian non-government organisation supporting those living with macular disease. As a multidisciplinary approach to healthcare has been widely encouraged for optimal patient outcomes, a similar approach is expected to benefit AMD patients [8]. The SDQ-AMD is currently used in a randomised controlled trial (RCT) to monitor dietary behaviours in AMD patients [9]. As this is a novel tool, reported food intake will be evaluated against the validated FFQ used in the BMES to assess comparability between recent actual intake and usual intake, respectively. Therefore, the aim of the current study is to evaluate the clinical use of the SDQ-AMD to screen for inadequate food intake by comparing it to the FFQ in AMD patients participating in this RCT.

2. Materials and Methods

2.1. Study Population

This study involved 155 participants who were enrolled in an RCT investigating the effectiveness of a telephone-delivered intervention to improve adherence to dietary recommendations for AMD management. The full protocol for the trial has been published previously [9] in Sydney, Australia. Recruitment occurred between June 2018 and July 2019, with participants recruited from Sydney-based private eye clinics, the University of Sydney and MDFA following online advertisements. Exclusion criteria were (a) lack of sufficient English fluency; (b) unwilling to participate in the 4 month intervention program; (c) inability to provide informed consent. The study was conducted in accordance to the Declaration of Helsinki, and the protocol for the RCT was approved by The University of Sydney Human Ethics Committee (Reference: HREC 2018/219).

2.2. Baseline Questionnaire

The self-administered baseline questionnaire was provided immediately after consent to participate was confirmed. It covered questions about general demographics, medical and surgical history, and visual function. Demographic questions collected information on ethnicity, marital status, living arrangements, level of education and employment status. Medical and surgical history questions provided an overview of the patient’s health status and any comorbidities, and other eye conditions like cataracts and glaucoma. Data was collected on AMD stage and lesions and the type and number of anti-VEGF injections patients received. The National Eye Institute Visual Function Questionnaire was also administered to assess general vision, difficulty with activities and other vision problems [10].

2.3. Food Frequency Questionnaire

The 145-item self-administered FFQ was included along with the baseline questionnaire, with an estimated completion time of 30–40 min. Participants were asked to reflect on their usual intake in the previous 12 months of all major food groups, beverages and discretionary foods. It was adapted from the validated FFQ [2,11,12] that was used in BMES for older adults to include modern food trends such as nut milk and coconut oil. Frequency options included “Never”; “Less than 1 per month”; “1–3 per month”; “1 per week”; “2–4 per week”; “5–6 per week”; “1 per day”; “2–3 per day”; “4+ per day”. These were converted to fractions of one day to determine average daily intakes, that is, the above frequencies became “0”, “0.02”, “0.07”, “0.14”, “0.43”, “0.79”, “1”, “2.5”, and “4”. Predefined portion sizes for each FFQ item were listed. Qualitative questions were also included in the FFQ to capture types and brands of particular food items, with free-response questions to specify the cereals and oils consumed, and multiple-choice options for margarine (e.g., olive spread, reduced fat, cholesterol-lowering) and butter (e.g., ordinary, reduced fat, dairy blend).
Preparation of FFQ data for statistical analyses involved converting frequency responses to grams consumed by multiplying the reported frequency and serve weight of the food item as listed in the FFQ or, if not specified, based on Australian Dietary Guidelines recommendations [13]. For free-response questions about cereals and oils used, the type and/or brands reported were manually matched to the Australian Food Composition Database [14] and linked to the frequency question to determine consumption values. Reported intakes were also adjusted to reflect seasonal variation based on Australian market data.

2.4. Short Dietary Questionnaire (SDQ-AMD)

Following completion of the FFQ, the 36-item SDQ-AMD was administered by the study coordinator to reduce reporting errors and/or misinterpretation of questions. The survey had an estimated completion time of 10 min and was adapted from a validated questionnaire to capture intake in the last week of key food groups linked to AMD management. Some food items asked average number of serves consumed per day (fruits, vegetables, water) or per week (meat, seafood, legumes, nuts, eggs). The remainder of food items were asked as times consumed per week (e.g., bread, alcohol, fats and oils), under the assumption that each time consumed roughly equated to one serve [15].

2.5. Statistical Analysis

Data from both FFQ and SDQ-AMD were entered into developed templates in REDCap and exported as Excel spreadsheets for statistical analysis in SAS version 9.4.
To allow consistent comparison between the FFQ and SDQ-AMD, scoring criteria were developed. Scores and cut points were justified based on the Australian Dietary Guidelines [13] and existing literature on nutrition and AMD links (Online Supplementary Table S1) [5,7,13,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48]. Food items included were total vegetables, dark green leafy vegetables, fruit, water, red meat, processed meat, fish/seafood, legumes, nuts, eggs, breads, cereals, select discretionary food and drinks, alcoholic beverages and fats and oil. To apply scores to FFQ data, the number of serves consumed for relevant food groups were calculated. For most food groups, calculations involved adding a participant’s total reported intake of food items within the same food group (e.g., adding up total grams of all listed fruits consumed) and dividing the total weight (in grams) by the standardised serve size as per the Australian Dietary Guidelines (e.g., 1 serve of any fresh fruit is approximately 150g) [13]. For food groups with food items of variable serve weights (e.g., specified discretionary food items), total energy (kJ) consumed of the food group was divided by the suggested serve size in kJ (e.g., 1 serve = 600 kJ). For alcohol, total alcohol intake in grams, rather than volume of alcohol consumed, was divided by one serve of alcohol (i.e., 10 g) [46]. For the SDQ-AMD, some additional data manipulation was required prior to applying score criteria, such as adjusting reported alcohol intake to reflect standard serves and converting reported weekly intake of specified discretionary items to daily intakes (Online Supplementary Table S1) [5,7,13,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48]. For both FFQ and SDQ-AMD, appropriate scores from the scoring criteria were allocated based on serving intake, and total scores were recorded out of 30. Agreement and bias between the SDQ-AMD and FFQ were determined using the Bland–Altman point of difference, using total scores between the SDQ-AMD and FFQ against the mean difference in scores from both surveys. Limits of agreement (LOA) were ± 2SD from the mean difference. Pearson correlation coefficients were calculated for selected food groups from SDQ-AMD and FFQ based on daily serving intake and stratified by age (<80 years and ≥80 years). SDQ-AMD data that were reported as serves or times per week were divided by 7 to determine daily intakes. Correlation between FFQ and SDQ-AMD scores was also calculated. Correlation coefficients < 0.30 were considered negligible [49], and statistical significance was indicated by p-value < 0.05. Descriptive statistics were also used to report participant characteristics and dietary intakes.

3. Results

Table 1 describes the study characteristics of the 155 participants. Most participants were Caucasian females (58%) and aged in their late 70s. Close to 95% of participants had a history of another medical condition, typically high blood pressure (63.2%) and arthritis (52.3%). Neovascular AMD was the most common type of AMD in this study (<80 years: 50%; ≥80 years: 61%), with cataracts also being a common eye condition affecting roughly 60% and 93% of participants <80 years and ≥80 years, respectively (Table 1).
Figure 1 briefly describes the diets of the study participants. Less than half of AMD patients in this study met the suggested recommendations in the scoring criteria for nuts (47.7%), processed meat (38.1%), fruit (34.2%), eggs (31%), lower GI cereals (30%), overall vegetables (14.2%), legumes (12.9%), dark green leafy vegetables (6.5%) and discretionary foods or drinks (5.8%). The SDQ was administered 33.9 days (SD = 50.5 days) after FFQ completion, and Table 2 compares the average reported intakes from FFQ and SDQ-AMD. The strongest correlation coefficients across both age groups were observed for fish/seafood, nuts and alcohol (Table 2). Dietary scores between the SDQ-AMD and FFQ were also significantly correlated (0.57, p ≤ 0.0001). Average total scores were 13.6 (± 4.5) and 14.6 (± 4.1) out of 30 from the FFQ and SDQ-AMD, respectively. The Bland−Altman plot of difference illustrates that most mean difference scores lie within the 95% confidence interval and there was no significant bias between the scores as the mean score increased ((regression equation: y = 0.11 x − 2.60) (95% CI: −0.058, 0.275, p-value = 0.20)) (Figure 2).

4. Discussion

To the best of our knowledge, this is the first study to evaluate the use of the novel SDQ-AMD as a screening tool to identify inadequate food intake in AMD patients. Taking approximately 10 min to complete, compared with 30–40 min for the FFQ, this study suggests that the SDQ-AMD would be more appropriate in a clinical setting.
Figure 1 shows the proportion of participants meeting the dietary recommendations. According to the latest NHS (2017–18) [6], which captured usual intake of fruit and vegetable serves per day with eight answer options ranging from “does not eat” to “6 serves or more”, overall adherence to fruit recommendations was higher in the general Australian population (65 years and over) than in the study participants (62.5% from NHS vs. 34.2% study participants), while consumption of recommended vegetable serves was comparably poor between groups (10.6% from NHS vs. 14.2% of study participants) [6]. Several factors contribute to inadequate dietary intakes in older adults, as well as those with AMD specifically. Individually, barriers including functional limitations (e.g., vision impairment), reduced appetite, limited dietary knowledge and cooking skills and low socioeconomic status [50] can limit the variety and amounts of food including fruits and vegetables consumed by older adults [51,52]. There are also limitations in clinical practice specific to AMD, including a lack of clear-cut guidelines on lifestyle and diet for patients and/or eye-care practitioners to follow; inadequate explanation and reinforcement by practitioners; and limited dietitian involvement and support [53,54,55].
The SDQ-AMD may help address some of these barriers as this study demonstrates that it could be appropriate for screening inadequate dietary intakes in AMD patients. Comparisons with the FFQ, as shown in Table 2, suggest that the SDQ-AMD has clinical relevance as the differences in reported average serve intakes between the two surveys were within 0.5 serve for most food groups with vegetables; included discretionary items, for example, biscuits and cakes; and water under-reported in the SDQ-AMD by approximately 1 serve on average. Association of reported intakes between surveys was determined using Pearson’s correlation coefficients, which ranged from 0.01 to 0.84. Just over half of the results showed negligible correlations (i.e., <0.30) [49]. On the other hand, a moderate positive correlation coefficient (0.57, p-value < 0.0001) was observed between survey total scores based on the scoring criteria (Online Supplementary Table S1) [5,7,13,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48]. As correlation coefficients only indicate associations, a Bland–Altman plot of difference (Figure 2) was used to assess agreement between the SDQ-AMD and FFQ [56]. This test illustrates reasonably good agreement as most mean difference scores fell within the LOA. The line of regression also suggests that any bias between surveys is small. This is a promising finding as the FFQ has been adapted for Australian diets and validated for older Australians against weighed food diaries [2]. However, compared with other validation studies [7,57,58], a significant limitation of this study is a lack of direct comparison with weighed food diaries. As the SDQ-AMD and FFQ are both recall-based tools, inherent recall bias may not be accounted for in this study, and agreement between the two surveys could be overestimated [1]. Moreover, a second limitation of this study is the inconsistent administration of the SDQ-AMD, taking an average 33.9 days (SD = 50.5 days) after FFQ completion. A number of factors contributed to the varying timeframe, including participant availability, provision of incorrect contact details and lack of a specified timeframe for SDQ-AMD completion in the protocol for the RCT. This inconsistency could affect the quality of the data due to increased variability of external factors such as seasonal food habits. Unlike the FFQ, that captures seasonal food habits through the 12 month recall, the short recall period of the SDQ-AMD makes it more susceptible to seasonal dietary habits. Therefore, future studies should aim to compare the SDQ-AMD against multiple weighed food diaries to reliably validate the SDQ-AMD and administer these tests with minimal time lag.
Despite the potential for recall bias and inconsistent administration of the SDQ-AMD, researchers attempted to improve data quality by minimising misinterpretation of questions when transitioning from the FFQ to SDQ-AMD by having the study coordinator administer the latter. This is because the self-administered FFQ consistently asked participants to reflect and report on their dietary intake in the previous 12 months, whereas the SDQ-AMD asked about intake in the last week, with questions changing from serves per day to serves per week to times per week. Although misinterpretation may also be a factor when completing the FFQ, it was successfully self-administered in the Blue Mountains Eye Study to report associations between diet and eye health, and time constraints and financial limitations did not allow for the study coordinator to administer it.
Administration of the SDQ-AMD has also highlighted the need for modifications that might help to further improve the tool, such as (i) including a question to account for dairy food consumption to ensure a more comprehensive profile of habitual diets, especially with recent evidence [59,60] suggesting a protective effect of dairy foods on AMD risk; and (ii) converting the paper-based SDQ-AMD to an online format to make it readily accessible and facilitate automatic score calculations for instant feedback. Developing a simple, user-friendly version is essential to improve adherence, as a previous study utilising a mobile food diary application had a high drop-out rate due to high participant burden and design flaws (i.e., not user-friendly or application freezing) [61].
Therefore, a dietary tool like the SDQ-AMD has potential to assist practitioners with screening AMD patients for inadequate food intake and identifying those in need of further dietary behavioural support. As eye-care practitioners are the first to diagnose AMD, practitioners should encourage their patients to improve modifiable risk factors. According to a study on the clinical practice of Australian optometrists (N = 283), two-thirds reported providing dietary advice, and approximately 50% recommended nutritional supplements particularly for AMD cases, while less than half would discuss smoking [62]. Optometrists appear to be more open to providing diet and nutritional supplement advice to their patients compared with ophthalmologists, while ophthalmologists were more likely to educate their patients about the eye-health implications of smoking and encourage smoking cessation [63]. Consistent advice about modifiable risk factors by eye-care practitioners is encouraged. In the case for diet, use of the SDQ-AMD could help with this, and the dietary patterns of our study participants justify the need for intervention. If used by eye-care practitioners, the SDQ-AMD should serve to only prompt discussions aimed at raising patient awareness about the importance of dietary associations with AMD, encourage appropriate referral to dietitians for further thorough assessment and education and/or referral to advocacy groups such as the MDFA for general resources and information around diet and AMD links.
We caution that our participants were predominantly receiving treatment for neovascular AMD, hence, these data are likely not representative of the general Australian population who are at risk of AMD development and/or have early stage AMD.

5. Conclusions

Study findings suggest that the SDQ-AMD has potential clinical use to screen for inadequate food intake in AMD patients. As a rapid and easy tool, it is more appropriate to administer in a clinical setting than the FFQ. Further studies are warranted to validate the SDQ-AMD, including comparison against weighed food diaries to account for recall bias.

Supplementary Materials

The following are available online at https://www.mdpi.com/2072-6643/11/12/3031/s1, Table S1: Scoring criteria for the FFQ and SDQ-AMD.

Author Contributions

Conceptualization, B.G.; Data Curation, D.T., Methodology, D.T., B.G. and V.F; Formal Analysis, D.T., G.B.; Writing—Original Draft Preparation, D.T.; Writing—Review & Editing, B.G., V.F., G.L., P.M. and G.B.; Supervision, B.G., V.F., G.L., P.M.; Funding Acquisition, B.G.; All authors provided critical revision of the manuscript and approved the final version.

Funding

The writing of this paper is supported by funding from National Health and Medical Research Council (NHMRC), grant number APP1150101.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Shim, J.-S.; Oh, K.; Kim, H.C. Dietary assessment methods in epidemiologic studies. Epidemiol. Health 2014, e2014009. [Google Scholar] [CrossRef]
  2. Barclay, W.; Flood, V.M.; Brand-Miller, J.C.; Mitchell, P. Validity of carbohydrate, glycaemic index and glycaemic load data obtained using a semi-quantitative food-frequency questionnaire. Public Health Nutr. 2008, 11. [Google Scholar] [CrossRef]
  3. Smith, W.; Mitchell, P.; Reay, E.M.; Webb, K.; Harvey, P.W.J. Validity and reproducibility of a self-administered food frequency questionnaire in older people. Aust. N. Z. J. Public Health 1998, 22, 456–463. [Google Scholar] [CrossRef]
  4. Flood, V.; Smith, W.T.; Webb, K.L.; Mitchell, P. Issues in assessing the validity of nutrient data obtained from a food-frequency questionnaire: Folate and vitamin B12 examples. 2004, 7, 751–756. [Google Scholar] [CrossRef] [Green Version]
  5. Broadhead, G.K.; Grigg, J.R.; Chang, A.A.; McCluskey, P. Dietary modification and supplementation for the treatment of age-related macular degeneration. Nutr. Rev. 2015, 73, 448–462. [Google Scholar] [CrossRef]
  6. Australian Bureau of Statistics. National Health Survey: First Results “Consumption of Fruit, Vegetables, and Sugar Sweetened and Diet Drinks—Australia. Table 12.3 Proportion of Persons”. 2018. Available online: https://www.abs.gov.au/ausstats/[email protected]/mf/4364.0.55.001 (accessed on 29 October 2019).
  7. Radd-Vagenas, S.; Fiatarone Singh, M.A.; Daniel, K.; Noble, Y.; Jain, N.; O’Leary, F.; Mavros, Y.; Heffernan, M.; Meiklejohn, J.; Guerrero, Y.; et al. Validity of the Mediterranean Diet and Culinary Index (MediCul) for Online Assessment of Adherence to the “Traditional” Diet and Aspects of Cuisine in Older Adults. Nutrients 2018, 10, 1913. [Google Scholar] [CrossRef] [Green Version]
  8. Tanaka, M. Multidisciplinary team approach for elderly patients. Geriatr. Gerontol. Int. 2003, 3, 69–72. [Google Scholar] [CrossRef]
  9. Tang, D.; Mitchell, P.; Flood, V.; Kifley, A.; Hayes, A.; Liew, G.; Gopinath, B. Dietary intervention in patients with age-related macular degeneration: Protocol for a randomised controlled trial. BMJ Open 2019, 9, e024774. [Google Scholar] [CrossRef] [Green Version]
  10. Mangione, C.M.; Lee, P.P.; Gutierrez, P.R.; Spritzer, K.; Berry, S.; Hays, R.D. Development of the 25-item National Eye Institute Visual Function Questionnaire. Arch. Ophthalmol. 2001, 119. [Google Scholar] [CrossRef]
  11. Flood, V.M.; Smith, W.; Rochtchina, E.; Wang, J.J.; Mitchell, P. Assembling a nutrient database for a large cohort study: Blue Mountains Eye Study. Food Aust. 2008, 60, 37–40. [Google Scholar]
  12. Willett, W.C.; Sampson, L.; Browne, M.L.; Stampfer, M.J.; Rosner, B.; Hennekens, C.H.; Speizer, F.E. The use of a self-administered questionnaire to assess diet four years in the past. Am. J. Epidemiol. 1988, 127, 188–199. [Google Scholar] [CrossRef]
  13. National Health and Medical Research Council. Eat for Health Australian Dietary Guidelines; National Health and Medical Research Council: Canberra, Australia, 2013.
  14. Food Standards Australia New Zealand. Australian Food Composition Database. 2019. Available online: http://www.foodstandards.gov.au/science/monitoringnutrients/afcd/Pages/foodsearch.aspx (accessed on 29 October 2019).
  15. Marks, K.; Rutishauser, I.H.E.; Riley, M. Monitoring Food Habits in the Australian Population Using Short Questions; Australian Food and Nutrition Monitoring Unit: Canberra, Australia, 2001. [Google Scholar]
  16. Nunes, S.; Alves, D.; Barreto, P.; Raimundo, M.; Da Luz Cachulo, M.; Farinha, C.; Lains, I.; Rodrigues, J.; Almeida, C.; Ribeiro, L.; et al. Adherence to a Mediterranean diet and its association with age-related macular degeneration. The Coimbra Eye Study-Report 4. Nutrition 2018, 51, 6–12. [Google Scholar] [CrossRef]
  17. Kim, E.K.; Kim, H.; Kwon, O.; Chang, N. Associations between fruits, vegetables, vitamin, A.; beta-carotene and flavonol dietary intake, and age-related macular degeneration in elderly women in Korea: The Fifth Korea National Health and Nutrition Examination Survey. Eur. J. Clin. Nutr. 2018, 72, 161–167. [Google Scholar] [CrossRef]
  18. Schröder, H.; Fitó, M.; Estruch, R.; Martinez-Gonzalez, M.A.; Corella, D.; Salas-Salvado, J.; Lamuela-Raventos, R.; Ros, E.; Salaverria, I.; Fiol, M.; et al. A short screener is valid for assessing Mediterranean diet adherence among older Spanish men and women. J. Nutr. 2011, 141, 1140–1145. [Google Scholar] [CrossRef] [Green Version]
  19. Bringmann, A.; Hollborn, M.; Kohen, L.; Wiedemann, P. Intake of dietary salt and drinking water: Implications for the development of age-related macular degeneration. Mol. Vis. 2016, 22, 1437–1454. [Google Scholar]
  20. Chan, J.; Knutsen, S.F.; Blix, G.G.; Lee, J.W.; Fraser, G.E. Water, other fluids, and fatal coronary heart disease: The Adventist health study. Am. J. Epidemiol. 2002, 155, 827–833. [Google Scholar] [CrossRef]
  21. Lieberman, H.R. Hydration and cognition: A critical review and recommendations for future research. J. Am. Coll. Nutr. 2007, 26, 555S–561S. [Google Scholar] [CrossRef]
  22. Wilson, M.-M.G.; Morley, J.E. Impaired cognitive function and mental performance in mild dehydration. Eur. J. Clin. Nutr. 2003, 2, S24. [Google Scholar] [CrossRef] [Green Version]
  23. Ministry of Health. Nutrient Reference Values: Water. 2014. Available online: https://www.nrv.gov.au/nutrients/water (accessed on 29 October 2019).
  24. Chong, E.W.-T.; Simpson, J.A.; Robman, L.D.; Hodge, A.M.; Aung, K.Z.; English, D.R.; Giles, G.G.; Guymer, R.H. Red Meat and Chicken Consumption and Its Association with Age-related Macular Degeneration. Am. J. Epidemiol. 2009, 169, 867–876. [Google Scholar] [CrossRef]
  25. Age-Related Eye Disease Study Research Group. A randomized, placebo-controlled, clinical trial of high-dose supplementation with vitamins C. and, E.; beta carotene, and zinc for age-related macular degeneration and vision loss: AREDS report no. 8. Arch. Ophthalmol. 2001, 119, 1417–1436. [Google Scholar] [CrossRef] [Green Version]
  26. Heart Foundation. Meat, Poultry and Seafood. 2019. Available online: https://www.heartfoundation.org.au/healthy-eating/food-and-nutrition/protein-foods/meat-poultry-and-seafood (accessed on 29 October 2019).
  27. Panagiotakos, D.; Kalogeropoulos, N.; Pitsavos, C.; Roussinou, G.; Palliou, K.; Chrysohoou, C.; Stefanadis, C. Validation of the MedDietScore via the determination of plasma fatty acids. Int. J. Food Sci. Nutr. 2009, 60, 168–180. [Google Scholar] [CrossRef]
  28. Sotos-Prieto, M.; Moreno-Franco, B.; Ordovás, J.M.; Leon, M.; Casasnovas, J.A.; Penalvo, J.L. Design and development of an instrument to measure overall lifestyle habits for epidemiological research: The Mediterranean Lifestyle (MEDLIFE) index. Public Health Nutr. 2015, 18, 959–967. [Google Scholar] [CrossRef]
  29. Morris, M.C.; Tangney, C.C.; Wang, Y.; Sacks, F.M.; Barnes, L.L.; Bennett, D.A.; Aggarwal, N.T. MIND diet slows cognitive decline with aging. Alzheimer’s Dement. 2015, 11, 1015–1022. [Google Scholar] [CrossRef] [Green Version]
  30. Ciccarone, E.; Di Castelnuovo, A.; Salcuni, M.; Siani, A.; Giacco, A.; Donati, M.B.; De Gaetano, G.; Capani, F.; Iacoviello, L.; Gendiabe, I. A high-score Mediterranean dietary pattern is associated with a reduced risk of peripheral arterial disease in Italian patients with Type 2 diabetes. J. Thromb. Haemost. 2003, 1, 1744–1752. [Google Scholar] [CrossRef]
  31. Goulet, J.; Lamarche, B.; Nadeau, G.; Lemieux, S.; Lemieux, S. Effect of a nutritional intervention promoting the Mediterranean food pattern on plasma lipids, lipoproteins and body weight in healthy French-Canadian women. Atherosclerosis 2003, 170, 115–124. [Google Scholar] [CrossRef]
  32. SanGiovanni, J.P.; Chew, E.Y.; Clemons, T.E.; Ferris, F.L., 3rd; Gensler, G.; Lindblad, A.S.; Milton, R.C.; Seddon, J.M.; Sperduto, R.D. The relationship of dietary lipid intake and age-related macular degeneration in a case-control study: AREDS Report No. 20. Arch. Ophthalmol. 2007, 125, 671–679. [Google Scholar] [CrossRef]
  33. Chiu, C.J.; Klein, R.; Milton, R.C.; Gensler, G.; Taylor, A. Does eating particular diets alter the risk of age-related macular degeneration in users of the Age-Related Eye Disease Study supplements? Br. J. Ophthalmol. 2009, 93, 1241–1246. [Google Scholar] [CrossRef]
  34. Kaushik, S.; Wang, J.J.; Flood, V.; Tan, J.S.L.; Barclay, A.W.; Wong, T.Y.; Brand-Miller, J.; Mitchell, P. Dietary glycemic index and the risk of age-related macular degeneration. Am. J. Clin. Nutr. 2008, 88, 1104–1110. [Google Scholar] [CrossRef]
  35. Chiu, C.-J.; Milton, R.C.; Klein, R.; Gensler, G.; Taylor, A. Dietary carbohydrate and the progression of age-related macular degeneration: A prospective study from the Age-Related Eye Disease Study. Am. J. Clin. Nutr. 2007, 86, 1210–1218. [Google Scholar] [CrossRef] [Green Version]
  36. Grains and Legumes Nutrition Council. Lifting the Lid on Legumes: The Benefits of Choosing Legumes. 2013. Available online: https://www.glnc.org.au/wp-content/uploads/2011/04/GLN_LiftingtheLid_WEB.pdf (accessed on 29 October 2019).
  37. Mohn, E.S.; Johnson, E.J. Lutein and Cognition Across the Lifespan. Nutr. Today 2017, 52, 183–189. [Google Scholar] [CrossRef]
  38. Lidder, S.; Webb, A.J. Vascular effects of dietary nitrate (as found in green leafy vegetables and beetroot) via the nitrate-nitrite-nitric oxide pathway. Br. J. Clin. Pharmacol. 2013, 75, 677–696. [Google Scholar] [CrossRef] [Green Version]
  39. Eisenhauer, B.; Natoli, S.; Liew, G.; Flood, V.M. Lutein and Zeaxanthin-Food Sources, Bioavailability and Dietary Variety in Age-Related Macular Degeneration Protection. Nutrients 2017, 9. [Google Scholar] [CrossRef]
  40. Gopinath, B.; Liew, G.; Tang, D.; Burlutsky, G.; Flood, V.M.; Mitchell, P. Consumption of eggs and the 15-year incidence of age-related macular degeneration. Clin. Nutr. 2019. [Google Scholar] [CrossRef] [PubMed]
  41. Heart Foundation. Eggs. Available online: https://www.heartfoundation.org.au/healthy-eating/food-and-nutrition/protein-foods/eggs (accessed on 29 October 2019).
  42. Australian Bureau of Statistics. Australian Health Survey: Nutrition First Results—Foods and Nutrients, 2011–12: Discretionary Foods. 2014. Available online: https://www.abs.gov.au/ausstats/[email protected]/Lookup/4364.0.55.007main+features12011-12 (accessed on 29 October 2019).
  43. Adams, M.K.; Chong, E.W.; Williamson, E.; Aung, K.Z.; Makeyeva, G.A.; Giles, G.G.; English, D.R.; Hopper, J.; Guymer, R.H.; Baird, P.N.; et al. 20/20—Alcohol and age-related macular degeneration: The Melbourne Collaborative Cohort Study. Am. J. Epidemiol. 2012, 176, 289–298. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Chong, E.W.; Kreis, A.J.; Wong, T.Y.; Simpson, J.A.; Guymer, R.H. Alcohol consumption and the risk of age-related macular degeneration: A systematic review and meta-analysis. Am. J. Ophthalmol. 2008, 145, 707–715. [Google Scholar] [CrossRef]
  45. Fraser-Bell, S.; Wu, J.; Klein, R.; Azen, S.P.; Varma, R. Smoking, alcohol intake, estrogen use, and age-related macular degeneration in Latinos: The Los Angeles Latino Eye Study. Am. J. Ophthalmol. 2006, 141, 79–87. [Google Scholar] [CrossRef]
  46. Australian Government Department of Health. How Much Alcohol Is Safe to Drink? 2019. Available online: https://www.health.gov.au/health-topics/alcohol/about-alcohol/how-much-alcohol-is-safe-to-drink. (accessed on 29 October 2019).
  47. Cougnard-Grégoire, A.; Merle, B.M.J.; Korobelnik, J.-F.; Rougier, M.-B.; Delyfer, M.-N.; Le Goff, M.; Samieri, C.; Dartigues, J.-F.; Delcourt, C. Olive Oil Consumption and Age-Related Macular Degeneration: The Alienor Study. PLoS ONE 2016, 11, e0160240. [Google Scholar] [CrossRef]
  48. Nidhi, B.; Mamatha, B.S.; Baskaran, V. Olive oil improves the intestinal absorption and bioavailability of lutein in lutein-deficient mice. Eur. J. Nutr. 2014, 53, 117–126. [Google Scholar] [CrossRef]
  49. Mukaka, M.M. Statistics corner: A guide to appropriate use of correlation coefficient in medical research. Malawi Med. J. 2012, 24, 69–71. [Google Scholar]
  50. Nicklett, E.J.; Kadell, A.R. Fruit and vegetable intake among older adults: A scoping review. Maturitas 2013, 75, 305–312. [Google Scholar] [CrossRef] [Green Version]
  51. U.S. Department for Agriculture and Center for Nutrition Policy and Promotion. Fruit and vegetable consumption by older Americans. Nutrition Insight 2007, 34, 143–156. [Google Scholar]
  52. Drewnowski, A.; Rehm, C.D. Vegetable Cost Metrics Show That Potatoes and Beans Provide Most Nutrients Per Penny. PLoS ONE 2013, 8, e63277. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Shah, S.U.; Pilli, S.; Telander, D.G.; Morse, L.S.; Park, S.S. Survey of patients with age-related macular degeneration: Knowledge and adherence to recommendations. Can. J. Ophthalmol. 2013, 48, 204–209. [Google Scholar] [CrossRef] [PubMed]
  54. Ng, W.T.; Goggin, M. Awareness of and compliance with recommended dietary supplement among age-related macular degeneration patients. Clin. Exp. Ophthalmol. 2006, 34, 9–14. [Google Scholar] [CrossRef] [PubMed]
  55. Hochstetler, B.S.; Scott, I.U.; Kunselman, A.R.; Thompson, K.; Zerfoss, E. Adherence to recommendations of the age-related eye disease study in patients with age-related macular degeneration. Retina 2010, 30, 1166–1170. [Google Scholar] [CrossRef] [PubMed]
  56. Martin Bland, J.; Altman, D.G. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986, 327, 307–310. [Google Scholar] [CrossRef]
  57. Radd-Vagenas, S.; Fiatarone Singh, M.A.; Inskip, M.; Mavros, Y.; Gates, N.; Wilson, G.C.; Jain, N.; Meiklejohn, J.; Brodaty, H.; Wen, W.; et al. Reliability and validity of a Mediterranean diet and culinary index (MediCul) tool in an older population with mild cognitive impairment. Br. J. Nutr. 2018, 120, 1189–1200. [Google Scholar] [CrossRef]
  58. Popkin, B.M.; D’Anci, K.E.; Rosenberg, I.H. Water, hydration, and health. Nutr. Rev. 2010, 68, 439–458. [Google Scholar] [CrossRef]
  59. Review of Ophthalmology. Calcium Intake and Age-Related Macular Degeneration. 2019. Available online: https://www.reviewofophthalmology.com/article/calcium-intake-and-agerelated-macular-degeneration (accessed on 29 October 2019).
  60. Gopinath, B.; Flood, V.M.; Louie, J.C.Y.; Wang, J.J.; Burlutsky, G.; Rochtchina, E.; Mitchell, P. Consumption of dairy products and the 15-year incidence of age-related macular degeneration. Br. J. Nutr. 2014, 111, 1673–1679. [Google Scholar] [CrossRef] [Green Version]
  61. Ali, Z.C.; Silvioli, R.; Rajai, A.; Aslam, T.M. Feasibility of Use of a Mobile Application for Nutrition Assessment Pertinent to Age-Related Macular Degeneration (MANAGER2). Transl. Vis. Sci. Technol. 2017, 6. [Google Scholar] [CrossRef] [Green Version]
  62. Downie, L.E.; Keller, P.R. The Self-Reported Clinical Practice Behaviors of Australian Optometrists as Related to Smoking, Diet and Nutritional Supplementation. PLoS ONE 2015, 10, e0124533. [Google Scholar] [CrossRef] [PubMed]
  63. Martin, L. Targeting modifiable risk factors in age-related macular degeneration in optometric practice in Sweden. Clin. Optom. 2017, 9, 77–83. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Nutrients 11 03031 g001 550
Figure 1. Proportion of participants meeting the recommendations for consumption of key food groups based on scoring criteria (Online Supplementary Table S1) [5,7,13,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48].
Figure 1. Proportion of participants meeting the recommendations for consumption of key food groups based on scoring criteria (Online Supplementary Table S1) [5,7,13,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48].
Nutrients 11 03031 g001
Nutrients 11 03031 g002 550
Figure 2. Bland–Altman plot of difference in score between the FFQ and SDQ-AMD versus the mean score of both methods. The solid line illustrates the mean difference (−1.5), and dotted lines represent the 95% confidence interval, with the upper and lower LOA (6.9 and −9.9, respectively) reflecting ± 2SD from the mean difference. Regression equation: y = 0.11 x − 2.60 (95% CI: −0.058, 0.275, p-value = 0.20). LOA: limits of agreement.
Figure 2. Bland–Altman plot of difference in score between the FFQ and SDQ-AMD versus the mean score of both methods. The solid line illustrates the mean difference (−1.5), and dotted lines represent the 95% confidence interval, with the upper and lower LOA (6.9 and −9.9, respectively) reflecting ± 2SD from the mean difference. Regression equation: y = 0.11 x − 2.60 (95% CI: −0.058, 0.275, p-value = 0.20). LOA: limits of agreement.
Nutrients 11 03031 g002
Table
Table 1. Participant characteristics stratified by age (<80 years, n = 86; ≥80 years, n = 69).
Table 1. Participant characteristics stratified by age (<80 years, n = 86; ≥80 years, n = 69).
<80 Years≥80 Years
Age (years)78 (5.7)86 (3.5)
Females53 (60.9)36 (52.9)
Caucasian ethnicity66 (79.5)61 (93.8)
Lives alone (%)13 (14.9)23 (33.8)
History of other medical conditions (%):
  Heart attack (%)7 (8.0)10 (14.7)
  Angina (%)4 (4.6)2 (2.9)
  Other cardiac (%)21 (24)21 (30.9)
  Stroke/Transient Ischemic Attack (%)3 (3.4)12 (17.6)
  High blood pressure (%)56 (64.4)42 (61.8)
  High cholesterol (%)42 (48.3)37 (54.4)
  Diabetes/pre-diabetes (%)21 (24.1)15 (22.1)
  Kidney disease (%)6 (6.9)4 (5.9)
  Arthritis (%)40 (46.0)41 (60.3)
  Other illness or major operation (%)54 (62.1)45 (66.2)
History of cataracts (%)52 (59.8)63 (92.6)
History of glaucoma (%)9 (10.3)14 (20.6)
Type of AMD by eyesN = 140N = 114
No AMD (%)28 (20)22 (19)
Early AMD (%)6 (4.3)0 (0.0)
Dry AMD (%)24 (17)8 (7.0)
Wet AMD (%)70 (50)69 (61)
Dry and Wet (%)12 (8.6)15 (13)
Eyes with wet AMD, receiving treatment withN = 79N = 80
Eylea (%)54 (68)52 (65)
Lucentis (%)24 (30)28 (35)
Avastin (%)1 (1.3)0 (0.0)
All data are presented as n (%) or mean (SD). AMD: age-related macular degeneration.
Table
Table 2. Comparison of the FFQ and SDQ-AMD in terms of average serves consumed per day, stratified by age (<80 years, n = 86; ≥80 years, n = 69).
Table 2. Comparison of the FFQ and SDQ-AMD in terms of average serves consumed per day, stratified by age (<80 years, n = 86; ≥80 years, n = 69).
Food GroupAge (years)FFQ Daily Mean Intake (SD)SDQ-AMD Daily Mean Intake (SD)Correlation Coefficient *p-Value **
Fruits<801.79 (1.72)1.84 (1.06)0.190.08
≥801.87 (2.80)1.79 (1.07)0.330.01
Vegetables<803.20 (1.92)2.25 (1.32)0.150.18
≥803.09 (2.07)1.91 (1.10)0.090.45
Dark green leafy <800.11 (0.15)0.16 (0.24)0.180.09
vegetables≥800.12 (0.17)0.16 (0.29)0.31<0.01
Red meat<800.37 (0.35)0.28 (0.22)0.160.13
≥800.37 (0.27)0.36 (0.23)0.31<0.01
Processed meat<800.32 (0.35)0.17 (0.23)0.28<0.01
≥800.37 (0.40)0.19 (0.23)0.010.93
White Meat<800.29 (0.35)0.26 (0.18)0.29<0.01
≥800.25 (0.22)0.23 (0.19)0.140.24
Fish/Seafood<800.33 (0.28)0.28 (0.29)0.35<0.001
≥800.38 (0.35)0.24 (0.17)0.58<0.0001
Eggs<800.35 (0.25)0.46 (0.33)0.32<0.01
≥800.29 (0.28)0.37 (0.27)0.50<0.0001
Legumes<800.14 (0.24)0.11 (0.17)0.270.01
≥800.11 (0.18)0.10 (0.14)0.070.57
Nuts<800.65 (1.00)0.55 (0.57)0.48<0.0001
≥800.62 (1.02)0.37 (0.49)0.54<0.0001
Low GI<801.51 (1.50)1.01 (0.86)0.270.01
≥801.34 (1.02)1.16 (0.74)0.50<0.0001
High GI<800.42 (0.54)0.30 (0.49)0.240.02
≥800.56 (0.72)0.36 (0.56)0.36<0.01
Biscuits and cakes, ice cream, sugary drinks, takeaway, processed potato<80 2.01 (1.47) 1.10 (0.82) 0.43 <0.0001
≥802.47 (1.63)1.34 (1.18)0.260.03
Water<805.58 (2.19)4.86 (2.58)0.110.32
≥805.71 (1.83)4.31 (2.06)0.46<0.0001
Alcohol<800.89 (1.69)0.65 (1.40)0.84<0.0001
≥800.69 (1.01)0.47 (0.82)0.81<0.0001
* Correlation coefficients <0.30 considered negligible [49] ** Significance indicated by p-value < 0.05. FFQ: food frequency questionnaire. SDQ-AMD: short dietary questionnaire.

Share and Cite

MDPI and ACS Style

Tang, D.; Mitchell, P.; Liew, G.; Burlutsky, G.; Flood, V.; Gopinath, B. Evaluation of a Novel Tool for Screening Inadequate Food Intake in Age-Related Macular Degeneration Patients. Nutrients 2019, 11, 3031. https://doi.org/10.3390/nu11123031

AMA Style

Tang D, Mitchell P, Liew G, Burlutsky G, Flood V, Gopinath B. Evaluation of a Novel Tool for Screening Inadequate Food Intake in Age-Related Macular Degeneration Patients. Nutrients. 2019; 11(12):3031. https://doi.org/10.3390/nu11123031

Chicago/Turabian Style

Tang, Diana, Paul Mitchell, Gerald Liew, George Burlutsky, Victoria Flood, and Bamini Gopinath. 2019. "Evaluation of a Novel Tool for Screening Inadequate Food Intake in Age-Related Macular Degeneration Patients" Nutrients 11, no. 12: 3031. https://doi.org/10.3390/nu11123031

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop