Food fortification with calcium and vitamin D: impact on health outcomes
Abstract
This is a protocol for a Cochrane Review (Intervention). The objectives are as follows:
To assess the impact of food fortification with vitamin D and calcium on health outcomes.
Background
Vitamin D is a family of fat‐soluble molecules that are important micronutrients for humans with its two forms: vitamin D2 and vitamin D3 playing a central role in bone growth by increasing the uptake of calcium from the gut. Calcium and vitamin D work together to protect the bones. Lately, increasing scientific evidence has also linked vitamin D deficiency to many infectious and non‐infectious chronic diseases including hypertension, diabetes, and cardiovascular diseases (Chiu 2004; Cigolini 2006; Ford 2005; Targher 2006; Zittermann 2006).
Vitamin D can be acquired through three main channels: through the skin, from the diet, and from supplements. Humans can obtain both vitamin D2 and D3 from their diet, with fish liver oils, eggs and milk being particularly rich. But most of our vitamin D is obtained in the form of vitamin D3, synthesized directly when our skin is exposed to sunlight. This powers a photochemical reaction in which a derivative of cholesterol is converted into pre‐vitamin D3, which is then transformed into vitamin D3 by the heat of the skin (Wagner 2008). Both vitamin D2 and D3 do not dissolve in water and so are bound to carrier proteins, mainly vitamin D binding protein, before they can be transported in the blood to the liver. Here, they are converted to 25D (25‐hydroxyvitamin D), which is the major circulating form of vitamin D. In the kidneys, 25D is converted to 1,25D (1,25‐dihydroxyvitamin D) via the action of an enzyme known as 1‐hydroxylase (Holick 2008; White 2008). This is the active form of vitamin D in the body and is thus the true hormonal form of vitamin D. It binds to the vitamin D receptor (VDR), which is found on the nuclear membrane of many cells (Walker 2009).
Vitamin D levels in the body are best measured using the concentration of 25D in blood serum. Generally in adults, normal (sufficient) concentrations are greater than 30 to 32 ng/mL (75 to 80 nM). People with levels below 20 ng/mL (50 nM) are deficient, while those with between 20 ng/mL (50 nM) and 30 to 32 ng/mL (75 to 80 nM) are termed vitamin D insufficient (Walker 2009). However, there is a lack of consensus as evidence exists that biochemical and skeletal sequelae of vitamin D deficiency may actually manifest at a higher cutoff of 30 to 35 ng/mL (Bischoff 2003; Dawson 1995; El‐Hajj Fuleihan 2001; Heaney 2003; Hollis 2005). It is estimated that around 1 billion people in the world may be vitamin D insufficient or deficient. Lack of sunlight (especially during winter months), vegetarian diet, a dark pigmented skin (as melanin acts as a natural sunscreen), increased pollution, and wearing long‐sleeved garments or clothes completely covering the body are the major risk factors (Williams 2008). Modern recommendations to avoid the sun to prevent skin cancers may also be contributing to vitamin D deficiency (Misra 2008).
Over the past 20 years, much attention has been paid in recognizing vitamin D insufficiency and deficiency in populations worldwide. Vitamin D deficiency is prevalent in many developing countries, even those with abundant sunlight such as Turkey (Ozgur 1996), Iran (Salimpour 1975), Saudi Arabia (Elidrissy 1984), India (Ghai 1991; Wayse 2004), China (Du 2001; Zhao 1991; Zhao 1992), Algeria (Garabedian 1991), and Nigeria (Akpede 1999; Akpede 2001). A study on the health status of children in low‐ and middle‐income countries reported that 73.1% of underprivileged children were 25D deficient (<8 ng/mL) and 23.1% were 25D insufficient (8 to 15 ng/mL) (Manaseki‐Holland 2008). A high occurrence of insufficiency or deficiency of vitamin D has also been reported in many other industrialised countries (Prentice 2008), such as US (Mansbach 2009), UK (Lawson 1999), Greece (Nicolaidou 2006), Finland (Lehtonen‐Veromaa 1999), Canada (Ward 2007), and New Zealand (Grant 2009).
Vitamin D fortified foods are the major dietary sources of vitamin D, but it might not be consumed in the appropriate amount and is not available in all countries. Consumption of low fat food might also impair the absorption of vitamin D as it is a fat soluble vitamin. Breast milk can be a poor source of vitamin D if the mother is deficient.
Vitamin D deficiency has various manifestations in different age groups.
In children: In growing children this deficiency can lead to rickets, which is characterised by weak and deformed bones. Vitamin D deficiency has been associated with tuberculosis (TB), influenza and other respiratory infections. Many epidemiological studies have recently been conducted in children to observe the link between inadequate vitamin D concentrations and respiratory infections, including TB (Karatekin 2009; McNally 2009; Muhe 1997; Najada 2004; Nnoaham 2008; Roth 2009; Salimpour 1975; Wayse 2004; Williams 2008). A recent small randomised controlled trial of vitamin D supplementation among children with pneumonia was associated with a reduction in repeat episodes of pneumonia (Manaseki‐Holland 2010).
In general population: Vitamin D has also been linked with various other inflammatory and long‐term diseases, including cardiovascular diseases (myocardial infarction), multiple sclerosis, asthma, rheumatoid arthritis, type 1 and type 2 diabetes (Wagner 2008), and cancers such as breast, ovarian, colorectal, and prostate (Cavalier 2009).
Pregnant women: During pregnancy, maternal 1,25(OH)2D requirements can increase up to four‐ to five‐fold to facilitate the availability of extra calcium required for fetal skeletal growth (Pérez‐López 2007). Approximately, 25‐30 g of calcium is transferred to the fetus by the end of pregnancy with the majority of this occurring in the last trimester (Abrams 2007; Salle 2000). Calcium levels in the third trimester fetus are higher than in the maternal plasma with maternal total serum calcium concentrations declining as the pregnancy progresses (Salle 2000), highlighting the role of active transport across the placenta.
In elderly population: Calcium is required for the proper functioning of heart, muscles, nerves and blood clotting. Inadequate calcium intake significantly contributes to the development of osteoporosis. Many studies have shown that low calcium intake is associated with low bone mass and high fracture rates. Hip fractures are the most serious and costly fractures among older persons (Cooper 1998). About 90% of hip fractures involve falls. Fractures caused by falls occur in about 5% of elderly persons each year, 1‐2% involving the hip. Long‐term vitamin D and calcium supplementation in elderly person is associated with reduced non vertebral fractures (Chapuy 1994; Dawson‐Hughes 1997). However, a recent meta‐analysis of prospective studies and randomised controlled trials found that calcium intake and calcium supplements were not associated with a lower risk for hip fractures (Bischoff‐Ferrari 2007).
Several strategies have been employed to supplement micronutrients to women and children, and these includes dietary modification and education, oral supplementation, fortification (home and commercial) and sprinkles. Food fortification is one of the strategies that can be used to prevent vitamin and mineral deficiencies. Fortification of foods and staples has been practiced in developed countries for over a century and is now increasing in many middle income countries. Today in many countries of Africa, Asia and Latin America, various foods including flour, salt, sugar, noodles, milk and chocolate powders are being fortified with various micronutrients. The food vehicles commonly used can be categorized in three broader categories: (1) staples (Wheat, rice, oils); (2) condiments (salt, soy sauce, sugar); and (3) processed commercial foods (noodles, infant complementary foods, beverages, dairy products).
How the intervention might work
In infectious diseases
The precise molecular mechanisms by which vitamin D helps defend against infectious disease are now being elucidated. It has become clear that 1,25 D plays a role not only in calcium homeostasis and bone metabolism, but also in the integrity of the innate immune system (Bhutta 2008; Wagner 2008). Acting via the VDR, 1,25 D alters the activity of many immune system cells, including macrophages, regulatory T cells and natural killer cells.
For bones
Vitamin D, in addition to its effects on calcium homeostasis, binds to specific receptors on skeletal muscle for 1,25‐dihydroxyvitamin D (Costa 1986; Haddad 1976). The hormonal form of vitamin D3, i.e. 1,25‐dihydroxyvitamin D3, acts through a nuclear receptor to carry out its many functions, including calcium absorption, phosphate absorption in the intestine, calcium mobilization in bone, and calcium reabsorption in the kidney (DeLuca 2004).
During pregnancy
The serum 1,25(OH)2D concentrations increase 50‐100% over the nonpregnant state during the second trimester and by 100% during the third trimester. Such increases could be explained by increasing synthesis and/or decreasing catabolism of 1,25(OH)2D. There is an increased expression of 1a‐hydroxylase and VDR genes and high levels of 1a‐hydroxylase (Zehnder 2002) in human placental and decidual tissues during the first and early second trimesters. Decreased catabolism may also contribute to higher placental levels of 1,25(OH)2D, as there is some evidence of specific epigenetic down regulation of the CYP24A1 (24‐hydroxylase) gene (Novakovic 2009) in the placenta.
Other diseases
The well‐known calcitropic functions of 1,25(OH)2D include the physiological regulation of calcium transport and bone mineralization by increasing intestinal calcium absorption (Holick 2008), suppressing parathyroid secretion (Bikle 2009) and promoting mineralization of the skeleton (DeLuca 1998). It is now being increasingly recognized that vitamin D has important non‐calcitropic actions, involving VDR activation by locally produced 1,25(OH)2D in a number of tissues in a paracrine and autocrine manner. The pleiotropic effects of 1,25(OH)2D include stimulation of insulin production (Chiu 2004), thyroid‐stimulating hormone secretion (Smith 1989) and improvement of myocardial contractility.
How is the food fortified
Food fortification is the process of adding micronutrient (essential trace elements and vitamins) to food. According to World Health Organization and Food and Agricultural Organization of the United Nations (FAO) fortification is done to increase the content of an essential micronutrient, i.e. vitamins and minerals (including trace elements) in a food irrespective of whether the nutrients were originally in the food before processing or not, so as to improve the nutritional quality of the food supply and to provide a public health benefit with minimal risk to health (Allen 2006). However, mandatory fortification is where food manufacturers are required to add a certain mineral or vitamin to a specified food in a certain quantity directly at mills. Since its origin, fortification programs have taken different forms:
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Mass (or universal) fortification involves fortifying foods that are widely consumed by the general population.
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Targeted fortification involves fortifying a food eaten by a specific subgroup of the population that has a particular need, for example, complementary food for young children.
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Market‐driven (or industry‐driven) fortification involves the food industry choosing to fortify, within regulatory limits set by the government.
Impact of fortification at population level
Food fortification is an attractive public health strategy and a number of programs have been initiated especially in the developed countries. Surprisingly this has not been systematically evaluated to assess the impact (Allen 2006). Much of the putative benefits of fortification are derived from supplementation trials, frequently small scale and focused and extrapolation to benefits in a fortification mode, where the vehicle and dosage differs greatly, is difficult. Food fortification can be a potentially cost‐effective public health intervention and target a larger population through a single strategy.
Why it is important to do this review
There are many reviews on vitamin D supplementation in different population groups but few on Vitamin D fortification. The recent review on Vitamin D fortification have focused on specific age groups like children (aged 6months to 5 years) (Eichler 2012) or adults only (Black 2012). In this review, we will attempt to quantify the impacts of Vitamin D and calcium fortification (in combination or alone) on all age groups in developed as well as developing countries. This will not only systematically analyse the existing data but also pave way for effective implementation and scale‐up.
Objectives
To assess the impact of food fortification with vitamin D and calcium on health outcomes.
Methods
Criteria for considering studies for this review
Types of studies
We will include:
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Randomised controlled trials (RCTs)
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Quasi‐randomised trials
We will also include cluster randomised controlled trials. No language or publication status restrictions will be applied. We will attempt to obtain translations where possible if the review team is unable to translate particular papers.
Types of participants
We will include studies that has assessed the effectiveness of food fortification in general population (including men, women and children).
Food fortification studies from both developing and developed countries will be included.
Types of interventions
Intervention: Vitamin D and/or Calcium fortification with any food vehicle
The following comparisons will be included:
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vitamin D versus no fortification;
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calcium versus no fortification;
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vitamin D plus calcium versus no fortification;
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vitamin D plus calcium versus vitamin D alone; and
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vitamin D plus calcium versus calcium alone.
Types of outcome measures
Primary outcomes
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Rickets (osteomalacia)
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Morbidity (such as infectious diseases like pneumonia, sepsis, diarrhoea, TB)
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All‐cause mortality rate
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Cause‐specific mortality (as defined by authors) due to pneumonia, diarrhoea, TB, malaria
Secondary outcomes
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Serum 25‐OH D3 concentration
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Bone mineral density
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Bone resorption markers
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Anthropometric markers
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Alkaline phosphatase level
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Serum parathyroid hormone concentration
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Potential adverse outcomes (e.g. hypercalcaemia, kidney stones or any other reported in papers)
Search methods for identification of studies
Electronic searches
We will search the following electronic databases for primary studies. The search period will be from 1990 to date.
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Cochrane Central Register of Controlled Trials (CENTRAL, The Cochrane Library), including the Cochrane Effective Practice and Organisation of Care (EPOC) Group Specialised Register and Cochrane Public Health Specialised Register
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MEDLINE and MEDLINE(R) In‐Process
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Pubmed
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EMBASE
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CINAHL
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PsycINFO
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ERIC
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LILACS
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Science Citation Index and Social Sciences Citation Index
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Current Controlled Trials (http://www.controlled‐trials.com/)
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Food Science and Technology Abstracts
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AgriCOLA
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Global Index Medicus ‐ AFRO
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EMRO
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PAHO (Pan American Health library)
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WHOLIS (WHO Library)
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WPRO
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IMSEAR (Index Medicus for the South‐East Asian Region)
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3ie Database of Impact studies
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EPPI centre databases
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Dopher and TROPHI
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Grey Literature through Google
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System for Information on Grey Literature in Europe (SIGLE)
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Index to Conference Proceedings
The MEDLINE search strategy will be adapted for use in the other databases using the appropriate controlled vocabulary as applicable. We will hand search the journals and the proceedings of major relevant conferences. We will also hand search the journals that the included studies most frequently appear in. The top five journals (according to the number of included studies provided) will be searched for the last 12 months.
Searching other resources
Reference lists of included studies and relevant systematic reviews identified will be examined for additional papers to consider for inclusion. Authors of relevant papers will be contacted regarding any further published or unpublished work.
Data collection and analysis
Selection of studies
Two review authors (RAS and ZSL) will independently assess for inclusion all the potential studies we identify as a result of the search strategy. We will resolve any disagreement through discussion or, if required, we will consult a third author (ZAB).
Data extraction and management
We will design a form to extract data. For eligible studies, two authors (RAS and ZSL) will extract the data using the agreed form. We will resolve discrepancies through discussion or, if required, we will consult a third author (ZAB). We will enter data into the Cochrane Collaboration statistical software, Review Manager 2011, and check for accuracy. When information regarding any of the above is unclear, we will attempt to contact authors of the original reports to provide further details.
We will use the PROGRESS (place, race, occupation, gender, religion, education, socio‐economic status, social status) checklist to record whether or not outcome data have been reported by socio‐demographic characteristics known to be important from an equity perspective.We will also record whether or not studies included specific strategies to address diversity or disadvantage.
Assessment of risk of bias in included studies
Two review authors (JKD and RAS) will independently assess risk of bias for each study using the criteria outlined in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011). We will resolve any disagreement by discussion or by involving a third assessor (ZAB).
Random sequence generation (checking for possible selection bias)
We will describe for each included study the method used to generate the allocation sequence in sufficient detail to allow an assessment of whether it should produce comparable groups. We will assess the method as:
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low risk of bias (any truly random process, e.g. random number table; computer random number generator);
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high risk of bias (any non‐random process, e.g. odd or even date of birth; hospital or clinic record number);
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unclear risk of bias.
Allocation concealment (checking for possible selection bias)
We will describe for each included study the method used to conceal allocation to interventions prior to assignment and will assess whether intervention allocation could have been foreseen in advance of, or during recruitment, or changed after assignment. We will assess the methods as:
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low risk of bias (e.g. telephone or central randomisation; consecutively numbered sealed opaque envelopes);
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high risk of bias (open random allocation; unsealed or non‐opaque envelopes, alternation; date of birth);
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unclear risk of bias.
Blinding of participants and personnel (checking for possible performance bias)
We will describe for each included study the methods used, if any, to blind study participants and personnel from knowledge of which intervention a participant received. We will consider that studies are at low risk of bias if they were blinded, or if we judge that the lack of blinding would be unlikely to affect results. We will assess blinding separately for different outcomes or classes of outcomes.We will assess the methods as:
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low, high or unclear risk of bias for participants;
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low, high or unclear risk of bias for personnel;
Blinding of outcome assessment (checking for possible detection bias)
We will describe for each included study the methods used, if any, to blind outcome assessors from knowledge of which intervention a participant received. We will assess blinding separately for different outcomes or classes of outcomes. We will assess methods used to blind outcome assessment as:
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low, high or unclear risk of bias.
Incomplete outcome data (checking for possible attrition bias due to the amount, nature and handling of incomplete outcome data)
We will describe for each included study, and for each outcome or class of outcomes, the completeness of data including attrition and exclusions from the analysis. We will state whether attrition and exclusions were reported and the numbers included in the analysis at each stage (compared with the total randomised participants), reasons for attrition or exclusion where reported, and whether missing data were balanced across groups or were related to outcomes. Where sufficient information is reported, or can be supplied by the trial authors, we will re‐include missing data in the analyses which we undertake. We will assess methods as:
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low risk of bias (e.g. no missing outcome data; missing outcome data balanced across groups);
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high risk of bias (e.g. numbers or reasons for missing data imbalanced across groups; 'as treated' analysis done with substantial departure of intervention received from that assigned at randomisation);
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unclear risk of bias.
Selective reporting (checking for reporting bias)
We will describe for each included study how we investigated the possibility of selective outcome reporting bias and what we found. We will assess the methods as:
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low risk of bias (where it is clear that all of the study's pre‐specified outcomes and all expected outcomes of interest to the review have been reported);
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high risk of bias (where not all the study's pre‐specified outcomes have been reported; one or more reported primary outcomes were not pre‐specified; outcomes of interest are reported incompletely and so cannot be used; study fails to include results of a key outcome that would have been expected to have been reported);
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unclear risk of bias.
Other bias
We will describe for each included study any important concerns we have about other possible sources of bias. We will assess whether each study was free of other problems that could put it at risk of bias:
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low risk of other bias;
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high risk of other bias;
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unclear whether there is risk of other bias.
Overall risk of bias
We will make explicit judgements about whether studies are at high risk of bias, according to the criteria given in Higgins 2011. We will assess the likely magnitude and direction of the bias and whether we consider it is likely to impact on the findings. We will explore the impact of the level of bias through undertaking sensitivity analyses.
Measures of treatment effect
Dichotomous data
For dichotomous data, we will present results as summary risk ratio with 95% confidence intervals.
Continuous data
For continuous data, we will use the mean difference if outcomes are measured in the same way between trials. We will use the standardised mean difference to combine trials that measure the same outcome, but use different methods.
Unit of analysis issues
Cluster‐randomised trials
We will include cluster‐randomised trials in the analyses along with individually randomised trials. We will adjust their standard errors using the methods described in Higgins 2011 using an estimate of the intracluster correlation coefficient (ICC) derived from the trial (if possible), from a similar trial or from a study of a similar population. If we use ICCs from other sources, we will report this and conduct sensitivity analyses to investigate the effect of variation in the ICC. If we identify both cluster‐randomised trials and individually‐randomised trials, we plan to synthesise the relevant information. We will consider it reasonable to combine the results from both if there is little heterogeneity between the study designs and the interaction between the effect of intervention and the choice of randomisation unit is considered to be unlikely. We will also acknowledge heterogeneity in the randomisation unit and perform a subgroup analysis to investigate the effects of the randomisation unit.
Studies with more than two treatment groups
If we identify studies with more than two intervention groups (multi‐arm studies) where possible, we will combine groups to create a single pair‐wise comparison or use the methods set out in the Cochrane Handbook for Systematic Reviews of Interventions to avoid double‐counting study participants (Higgins 2011). For the subgroup analyses, when the control group is shared by two or more study arms, we will divide the control group (events and total population) over the number of relevant subgroups to avoid double counting the participants.
Dealing with missing data
We shall describe missing data, including dropouts. Differential dropout rates can lead to biased estimates of the effect size, and bias may arise if the reasons for dropping out differ across groups. We shall report the reasons for dropout. If data are missing for some cases, or if the reasons for dropping out are not reported, we shall contact the authors. For included studies, we will note levels of attrition. We will explore the impact of including studies with high levels of missing data in the overall assessment of treatment effect by using sensitivity analysis. For all outcomes, we will carry out analyses, as far as possible, on an intention‐to‐treat basis, i.e. we will attempt to include all participants randomised to each group in the analyses, and all participants will be analysed in the group to which they were allocated, regardless of whether or not they received the allocated intervention. The denominator for each outcome in each trial will be the number randomised minus any participants whose outcomes are known to be missing.
Assessment of heterogeneity
We shall assess included studies for clinical, methodological, and statistical heterogeneity. We shall assess clinical heterogeneity by comparing the distribution of important factors, such as the study participants, study setting, dose and duration of the intervention and co‐interventions. We shall evaluate methodological heterogeneity on the basis of factors such as the method of sequence generation, allocation concealment, blinding of outcome assessment, and losses to follow up. We will assess statistical heterogeneity in each meta‐analysis using the T2, I2 and Chi2 statistics. We will regard heterogeneity as substantial if I2 is greater than 30% and either T2 is greater than zero, or there is a low P value (< 0.10) in the Chi2 test for heterogeneity. In case of absence of heterogeneity, pre‐specified subgroup analysis will be undertaken.
Assessment of reporting biases
If there are 10 or more studies in the meta‐analysis we will investigate reporting biases (such as publication bias) using funnel plots. We will assess funnel plot asymmetry visually, and use formal tests for funnel plot asymmetry. For continuous outcomes we will use the test proposed by Egger 1997, and for dichotomous outcomes we will use the test proposed by Harbord 2006. If asymmetry is detected in any of these tests or is suggested by a visual assessment, we will perform exploratory analyses to investigate it.
Data synthesis
We will carry out statistical analysis using the Review Manager software (Review Manager 2011). We will use fixed‐effect meta‐analysis for combining data where it is reasonable to assume that studies are estimating the same underlying treatment effect: i.e. where trials are examining the same intervention, and the trials' populations and methods are judged sufficiently similar. If there is clinical heterogeneity sufficient to expect that the underlying treatment effects differ between trials, or if substantial statistical heterogeneity is detected, we will use random‐effects meta‐analysis to produce an overall summary. The results will be presented as the average treatment effect with 95% confidence intervals, and the estimates of T2 and I2.
We will set out the main findings of the review in summary of findings tables prepared using the GRADE approach (Guyatt 2008) using GRADE profiler software. We will list the primary outcomes for each comparison with estimates of relative effects along with the number of participants and studies contributing data for those outcomes. For each individual outcome, we will assess the quality of the evidence using the GRADE approach, which involves consideration of within‐study risk of bias (methodological quality), directness of evidence, heterogeneity, precision of effect estimates and risk of publication bias. We will rate the quality of the body of evidence for each key outcomes as "high", "moderate", "low" or "very low".
Subgroup analysis and investigation of heterogeneity
If we identify substantial heterogeneity, we will investigate it using subgroup analyses and sensitivity analyses. We will consider whether an overall summary is meaningful, and if it is, we will use random‐effects analysis to produce it.
We plan to carry out the following subgroup analyses:
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gender (male versus female);
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age group particularly among females i.e. prepubertal versus women of reproductive age versus postmenopausal women;
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type of fortification (vitamin D alone versus calcium alone versus vitamin D and calcium in combination);
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geographical setting to rule our differences in outcomes among population living in area with greater sun exposure versus cloudy areas.
For fixed‐effect inverse variance meta‐analyses we will assess differences between subgroups by interaction tests. For random‐effects and fixed‐effect meta‐analyses using methods other than inverse variance, we will assess differences between subgroups by inspection of the subgroups' confidence intervals; non‐overlapping confidence intervals indicate a statistically significant difference in treatment effect between the subgroups.
Sensitivity analysis
We shall perform sensitivity analyses to examine the affect of removing studies at high risk of bias (those with high or unclear risk of bias according to method and adequacy of allocation concealment; blinding status of the participants; percentage lost to follow up or with an attrition of greater than or equal to 20%; and random‐effects model of the primary analysis). Moreover, sensitivity analyses will be performed based on different ICC values.
