Skip to main content Accessibility help
×
Home
Hostname: page-component-7f7b94f6bd-9g8ph Total loading time: 1.072 Render date: 2022-06-28T19:04:07.308Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "useRatesEcommerce": false, "useNewApi": true } hasContentIssue true

GeNuIne (gene–nutrient interactions) Collaboration: towards implementing multi-ethnic population-based nutrigenetic studies of vitamin B12 and D deficiencies and metabolic diseases

Published online by Cambridge University Press:  22 September 2021

Karani S. Vimaleswaran*
Affiliation:
Hugh Sinclair Unit of Human Nutrition, Department of Food and Nutritional Sciences, University of Reading, Reading, UK The Institute for Food, Nutrition, and Health (IFNH), University of Reading, Reading, UK
*
Corresponding author: Karani S. Vimaleswaran, email v.karani@reading.ac.uk
Rights & Permissions[Opens in a new window]

Abstract

Gene–nutrient interactions (GeNuIne) collaboration, a large-scale collaborative project, has been initiated to investigate the impact of gene–nutrient interactions on cardiometabolic diseases using population-based studies from ethnically diverse populations. In this project, the relationship between deficiencies of vitamins B12 and D, and metabolic diseases was explored using a nutrigenetic approach. A genetic risk score (GRS) analysis was used to examine the combined effect of several genetic variations that have been shown to be associated with metabolic diseases and vitamin B12 and D deficiencies, respectively. In Sri Lankan, Indonesian and Brazilian populations, those carrying a high B12-GRS had an increased risk of metabolic diseases under the influence of dietary protein, fibre and carbohydrate intakes, respectively; however, in Asian Indians, genetically instrumented metabolic disease risk showed a significant association with low vitamin B12 status. With regards to nutrigenetic studies on vitamin D status, although high metabolic-GRS showed an interaction with dietary carbohydrate intake on vitamin D status, the study in Indonesian women demonstrated a vitamin D GRS–carbohydrate interaction on body fat percentage. In summary, these nutrigenetic studies from multiple ethnic groups have provided evidence for the influence of the dietary factors on the relationship between vitamin B12/D deficiency and metabolic outcomes. Furthermore, these studies highlight the existence of genetic heterogeneity in gene–diet interactions across ethnically diverse populations, which further implicates the significance of personalised dietary approaches for the prevention of these micronutrient deficiencies and metabolic diseases.

Type
Conference on ‘Micronutrient malnutrition across the life course, sarcopenia and frailty’
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of The Nutrition Society

Metabolic diseases such as obesity and diabetes are vastly growing epidemics prevalent in both developed and developing countries, affecting all ages, genders, ethnicities and socioeconomic groups(Reference Zobel, Andreasen and Grarup1,Reference Di Cesare, Soric and Bovet2) . Metabolic diseases have been shown to reduce the quality of life for the individual by leading to severe and potentially life-threatening consequences, such as CVD, cancers, hypertension and musculoskeletal disorders(Reference Niessen, Mohan and Akuoku3,Reference Miranda, Barrientos-Gutierrez and Corvalan4) . Even though the main influences of metabolic diseases are lifestyle factors such as diet and exercise, a substantial amount of evidence is emerging in the field of genetic epidemiology, suggesting that an individual's genetic profile may also play a key role in the development of these diseases(Reference Vimaleswaran and Loos5Reference Radha, Vimaleswaran and Deepa9).

Several studies have shown that healthy lifestyle may modify the association between genetic risk and metabolic disease-related traits(Reference Vimaleswaran, Li and Zhao10Reference Vimaleswaran, Bodhini and Jiang16). Although some studies have shown that increased physical activity levels and healthy diet can attenuate the effect of genetic variants on metabolic traits, other studies have shown conflicting results(Reference Dietrich, Jacobs and Zheng17), which could be attributed to genetic heterogeneity and differences in the dietary patterns across multiple ethnic groups(Reference Vimaleswaran7,Reference Radha, Vimaleswaran and Babu18,Reference Vimaleswaran19) . Majority of the gene–lifestyle interactions have focused on metabolic disease-related outcomes(Reference Du, Vimaleswaran and Angquist20Reference Matusheski, Caffrey and Christensen23) and only a few have focused on vitamins B12 and D, two critical vitamins which have been shown to be associated with age-related cardiometabolic diseases(Reference Chakraverty and Chakraborty24).

Genetic studies have implicated several gene loci associated with vitamin B12 and D concentrations(Reference Surendran, Adaikalakoteswari and Saravanan25Reference Vimaleswaran, Cavadino and Berry28). Several Mendelian randomisation studies have explored the relationship between genetically instrumented vitamin B12 and D concentrations and metabolic disease-related outcomes; however, the findings have been inconsistent(Reference Moen, Qvigstad and Birkeland29Reference Dimitrakopoulou, Tsilidis and Haycock32). The aim of this paper is to provide an overview of ethnic-specific findings from gene–nutrient interactions (GeNuIne) collaboration that used a nutrigenetic approach to investigate the relationship between metabolic disease-related traits and vitamin B12/D status, where the effects of macronutrient intake such as carbohydrate, fat and protein intake on these relationships were explored.

Role of British Nutrition Foundation Drummond Pump Priming Award in gene–nutrient interactions collaboration

Given that the genetic profile varies across various ethnic groups(Reference Palaniappan, Carnethon and Fortmann33,Reference Vimaleswaran34) , it is crucial to explore gene–diet interactions in multiple ethnicities, which will enable us to personalise diet according to each ethnic group(Reference Matusheski, Caffrey and Christensen23,Reference Vimaleswaran and Reddy35) . To address this issue, the GeNuIne Collaboration was initiated in 2013 to implement nutrigenetic studies on metabolic disease-related traits using population-based studies from multiple ethnic groups in lower-middle income countries(Reference Vimaleswaran6,Reference Vimaleswaran7) . The British Nutrition Foundation Drummond Pump Priming award was the seed funding for the initiation of the GeNuIne Collaboration, where the funds were used to establish a collaborative network with academic institutions in India(Reference Vimaleswaran, Bodhini and Lakshmipriya12), Brazil(Reference Surendran, Morais and Abdalla36), Turkey(Reference Isgin-Atici, Alsulami and Turan-Demirci22), Thailand(Reference Sae-Tan and Vimaleswaran37), Sri Lanka(Reference Surendran, Alsulami and Lankeshwara38), Indonesia(Reference Surendran, Aji and Ariyasra39), Morocco and Pakistan. In addition, the Newton Fund British Council Researcher Links travel grants were obtained to carry out pilot studies in lower-middle income countries. Given that there were no nutrigenetic studies that had explored the relationship between metabolic diseases and vitamin B12/D status, the GeNuIne Collaboration was established to address this missing gap in nutritional science(Reference Vimaleswaran7,Reference Surendran and Vimaleswaran40) .

Use of genetic risk scores as instruments for micronutrient deficiencies and metabolic diseases

Genome-wide association studies have discovered thousands of genetic variants associated with metabolic diseases(Reference Bradfield, Taal and Timpson41Reference Vimaleswaran, Tachmazidou and Zhao45) and vitamin B12(Reference Surendran, Adaikalakoteswari and Saravanan25,Reference Nongmaithem, Joglekar and Krishnaveni46) /D(Reference Alathari, Sabta and Kalpana27,Reference Jiang, O'Reilly and Aschard47) status, respectively; however, the individual SNPs explain only a small proportion of variation for obesity and diabetes, with limited ability for predicting disease risk. Given that these complex traits are influenced by several genetic variants, with each having a small effect on these traits, combining the effect of several variants as a polygenic score can provide a better understanding of disease risk than single variant approaches(Reference Udler, McCarthy and Florez48Reference Torkamani and Topol50). The idea of grouping individual SNPs into genetic risk scores (GRSs) has been used to predict and quantify a discrete increment in the overall risk of diseases, as well as capturing the overall variance in a trait(Reference Igo, Kinzy and Cooke Bailey51). There are several approaches for generating a GRS such as weighted and unweighted methods(Reference Huls, Kramer and Carlsten52). Fundamentally, a GRS is constructed by summarising genotype data across multiple genetic variants. The most commonly used method is summing the number of alleles that confer risk across all loci (zero, one or two). Employing the GRS approach for predicting disease risk has advantages over analysing the effect of individuals SNPs as it decreases the drawback of multiple testing, maximises statistical power and widens the scope of generalisability of genetic associations(Reference Udler, McCarthy and Florez48,Reference Igo, Kinzy and Cooke Bailey51) . Previous studies have emphasised the potential of GRS for predicting the risk of complex diseases. Given that there were no previously reported effect sizes in lower-middle income countries, the nutrigenetic studies from the GeNuIne Collaboration used an unweighted GRS method(Reference Xie, Wang and Nolte53) which was calculated for each participant by adding the number of risk alleles for metabolic diseases and micronutrient deficiencies, respectively. A value of zero, one and two was assigned to each SNP, which indicates the number of metabolic disease-related risk alleles and vitamin B12/D lowering risk alleles, respectively. These values were then calculated by adding the number of risk alleles across each SNP. The risk allele score was then divided by the median into two groups: participants carrying a lower number of risk alleles and those with a higher number of risk alleles.

Findings from gene–nutrient interactions collaboration

Impact of genetic and dietary factors on vitamin B12 status and metabolic diseases in ethnically diverse populations

Several epidemiological studies have shown associations between metabolic diseases and micronutrient deficiencies including vitamin B12(Reference Knight, Shields and Brook54Reference Lee, Wang and Lin56); however, the findings have been inconsistent due to high level of confounding. Given that genetic associations are less prone to confounding(Reference Berry, Vimaleswaran and Whittaker26,Reference Vimaleswaran, Berry and Lu30) , studies conducted as part of the GeNuIne Collaboration used a nutrigenetics approach to examine this relationship (Fig. 1).

Fig. 1. Objective, study design and the expected results of the nutrigenetic studies from the GeNuIne Collaboration. Genetic associations are represented by one-sided arrows with unbroken lines and interactions between GRS and dietary intakes on metabolic traits and vitamin D/B12 status are shown as one-sided arrows with broken lines. The association of the metabolic-GRS with vitamin B12/D status and metabolic traits, respectively, and the association of vitamin B12/D-GRS with vitamin B12/D status and metabolic traits, respectively, were tested. In addition, the effect of dietary factors on these genetic associations was examined.

South Asians have been shown to exhibit increased visceral fat and waist circumference, hyperinsulinaemia and insulin resistance; this has been termed the ‘South Asian phenotype’(Reference Mohan and Deepa57,Reference Patel, Shivashankar and Ali58) . Although there is a strong genetic component to developing the ‘South Asian Phenotype’, consuming an unhealthy diet and leading a sedentary lifestyle can further contribute to this phenotype(Reference Vimaleswaran, Bodhini and Lakshmipriya12). A cross-sectional nutrigenetic study in nine hundred Asian Indians demonstrated that metabolic-GRS, that was developed using metabolic disease-related genetic variants (Table 1), was associated with vitamin B12 levels, where carriers of more than one risk allele for the GRS had significantly lower vitamin B12 concentrations, compared to those carrying zero risk alleles(Reference Surendran, Jayashri and Drysdale59) (Fig. 2). This finding suggests that genetically instrumented metabolic disease could be a risk factor for vitamin B12 deficiency with implications on the possible targeting of relevant obesity prevention strategies. However, a cross-sectional nutrigenetic study in one-hundred and nine Sinhalese adults aged 25–50 years showed that vitamin B12-GRS, that was developed using vitamin B12-related genetic variants (Table 1), was associated with central obesity under the influence of protein consumption(Reference Surendran, Alsulami and Lankeshwara38). Given that the daily intake of protein is low in Sri Lankan adults(Reference Jayawardena60,Reference Medagama, Fernando and Widanapathirana61) , these findings may have significant public health implications in terms of revising dietary guidelines for this population, which could prevent central obesity and its related complications.

Fig. 2. Results from the vitamin B12-related nutrigenetic studies in South Asians and Southeast Asians. (a) A nutrigenetic study in Asian Indians(59): metabolic disease risk increasing alleles ranged from 0 to 3. The white bars indicate individuals with 0 risk alleles and the black bars indicate individuals carrying ≥1 alleles. Individuals who carried 1 or more risk alleles had significantly lower B12 concentrations compared to individuals carrying 0 risk alleles (P = 0⋅018). (b) A nutrigenetic study in Southeast Asians (Indonesia)(39): individuals who carried nine or more risk alleles for vitamin B12 deficiency (high B12-GRS) had significantly higher HbAC1 concentrations (ng/ml) in the lowest tertile of fibre intake (g) (mean(sd): 4⋅90(sd 1⋅00) g) compared to those with eight or less risk alleles for vitamin B12 deficiency (low B12-GRS). GRS: genetic risk score; HbAC1, haemoglobin AC1.

Table 1. Details of the SNPs that were examined in each ethnic group

Countries in Southeast Asia, especially Indonesia, have undergone rapid epidemiological and nutritional transitions over the past few decades(Reference Schroders, Wall and Hakimi62). Indonesia has the seventh largest number of diabetic patients (about 10 million)(63) and non-communicable diseases are estimated to account for 73 % of all deaths of which, CVD contributed to 35 % followed by cancers (12 %) and diabetes (6 %). In a cross-sectional study of one-hundred and seventeen Indonesian women(Reference Surendran, Aji and Ariyasra39), those with high B12-GRS (comprising nine B12-related SNPs) and consuming a low fibre diet (4⋅90(sd 1⋅00) g daily) had significantly higher haemoglobin A1C levels compared to those with low B12-GRS (Fig. 2). This study suggests that genetically instrumented low B12 levels might be a risk factor for the development of metabolic diseases such as type 2 diabetes.

Brazil is a developing country that is undergoing rapid economic, demographic and behavioural transition(Reference Conde and Monteiro64Reference Monteiro, Mondini and de Souza66) which has resulted in an increased prevalence of CVD, one of the leading causes of mortality(Reference Ribeiro, Duncan and Brant67). Studies have also reported unhealthy dietary patterns which are characterised by higher intakes of processed foods, refined grains and sugar sweetened beverages(Reference Souza, Yokoo and Sichieri68). The first nutrigenetics study on vitamin B12 status in Brazil(Reference Surendran, Morais and Abdalla36) was a cross-sectional study in one-hundred and thirteen adolescents (10–19 years old), recruited from a public school in the city of Goiânia, Goiás, Brazil. The study demonstrated that those who had high carbohydrate intake and high B12-pathway-related genetic risk had significantly higher oxidised-LDL concentrations compared to those with low genetic risk suggesting the impact of genetically instrumented B12 status on cardiovascular risk factors.

Given that high fibre and protein diets are recommended for preventing metabolic disease outcomes(Reference Morenga, Williams and Brown69Reference Kaczmarczyk, Miller and Freund71), the gene–diet interaction findings observed in these nutrigenetic studies will have significant public health implications, where people carrying risk alleles for vitamin B12 deficiency could be advised to alter their diet according to their ethnic background.

Impact of genetic and dietary factors on vitamin D status and metabolic diseases in ethnically diverse populations

Vitamin D is a fat-soluble vitamin and a secosteroid prohormone that plays a crucial role in calcium absorption, immune function and protecting bone, muscle and heart health(Reference Hossein-nezhad and Holick72Reference Vimaleswaran, Forouhi and Khunti74). Deficiency of vitamin D has been found to contribute to the development of various cardiometabolic conditions such as obesity, diabetes and hypertension(Reference Valer-Martinez, Martinez and Sayon-Orea75,Reference Vimaleswaran, Cavadino and Berry76) . However, the association between vitamin D deficiency and these cardiometabolic conditions has not been firmly established. The application of a nutrigenetic approach to establish the link between vitamin D status and metabolic diseases is favoured over observational studies since genetic associations are less affected by confounding(Reference Berry, Vimaleswaran and Whittaker26).

A recent review from the GeNuIne Collaboration team has identified seventy-three peer reviewed articles demonstrating ninety-two significant ethnic-specific associations between genes related to the synthesis and metabolism of 25-hydroxyvitamin D (25(OH)D) and metabolic disease-related outcomes such as obesity and diabetes traits(Reference Alathari, Sabta and Kalpana27). Similarly, there are also studies which have shown associations between metabolic disease-related genes and 25(OH)D concentrations(Reference Vimaleswaran, Cavadino and Berry28,Reference Vimaleswaran, Power and Hypponen77) . Using these disease-specific genetic variations, the studies from GeNuIne Collaboration used a nutrigenetic approach to examine the link between vitamin D status and metabolic diseases. In addition, the studies investigated the combined effects of multiple genetic variants using GRSs instead of the common single gene variant method in order to increase the statistical power to detect gene–diet interactions(Reference Udler, McCarthy and Florez48,Reference Igo, Kinzy and Cooke Bailey51) . In this nutrigenetic approach, the studies tested whether dietary factors influenced the associations between vitamin D status and metabolic diseases.

The first nutrigenetic study of vitamin D and metabolic diseases was implemented in Asian Indians(Reference Alathari, Bodhini and Jayashri78). In this study, five hundred and forty-five Asian Indians were randomly selected from the Chennai Urban Rural Epidemiology Study, where two hundred and nineteen were normal glucose tolerant individuals, one hundred and fifty-one were with pre-diabetes and one hundred and seventy-five had type 2 diabetes. The study showed a significant interaction between metabolic GRS and carbohydrate intake on 25(OH)D, where individuals consuming a low carbohydrate diet (≤62 % of total energy intake) and those having lesser number of metabolic risk alleles had significantly higher levels of 25(OH)D. However, among individuals who had a higher carbohydrate intake (>67 %), despite having lower number of metabolic risk alleles, did not show a significantly higher 25(OH)D concentrations. These findings demonstrate that individuals carrying a low genetic risk of metabolic diseases are likely to have higher 25(OH)D levels if they consume a low carbohydrate diet (Fig. 3). Given that previous studies have reported that Asian Indians have lower 25(OH)D concentrations(Reference Selvarajan, Gunaseelan and Anandabaskar79Reference Jayashri, Venkatesan and Shanthirani81), these findings suggest that, even if the metabolic genetic risk is lower, following the dietary carbohydrate recommendations (50–60 %) is required to improve the vitamin D status in this Asian Indian population.

Fig. 3. Results from the vitamin D-related nutrigenetic studies in South Asians and Southeast Asians. (a) A nutrigenetic study in Asian Indians(78): interaction between metabolic GRS and carbohydrate intake (%) on log 25(OH)D. White bars indicate individuals with GRS ≤1 risk allele; black bars indicate individuals with GRS >1 risk allele. Among individuals with low carbohydrates intake, those with <1 risk allele had significantly higher 25(OH)D concentrations compared to those with >1 risk allele (P = 0⋅003). (b) A nutrigenetic study in Southeast Asians (Indonesia)(87): interaction between the vitamin D-GRS and dietary carbohydrate intake (g) on body fat percentage (%) (Pinteraction = 0⋅049). Those who were on the highest tertile of carbohydrate intake and carried >2 risk alleles had significantly higher body fat percentage compared to individuals carrying ≤2 risk alleles (P = 0⋅016). GRS: genetic risk score.

To date, only a few studies have examined the influence of SNPs on 25(OH)D levels in populations within Southeast Asia(Reference Aji, Erwinda and Rasyid82). The study from GeNuIne Collaboration focused on Minangkabau women from Padang, the capital of West Sumatra. The Minangkabau ethnic group is of particular interest given that the Minangkabau people have the largest matrilineal family structure in the world(Reference Aji, Erwinda and Yusrawati83Reference Lipoeto, Mmedsci and Agus86). A nutrigenetic study to investigate the relationship between vitamin D status and metabolic traits in a cohort of one hundred and ten Minangkabau women from urban and rural areas of Padang was conducted(Reference Alathari, Aji and Ariyasra87). The study identified a significant interaction between the vitamin D-GRS and carbohydrate intake (g) on body fat percentage, where individuals who consumed a high carbohydrate diet (mean(sd): 319 g daily(sd 46)) and carried >2 vitamin D-lowering risk alleles had significantly higher body fat percentage than those with ≤2 risk alleles (Fig. 3). These findings are biologically plausible as vitamin D has been shown to mediate the impact of reduced consumption of carbohydrate through its direct action on pancreatic β-cell function(Reference Pittas, Lau and Hu88). Given that percent body fat is a better predictor of cardiovascular risk factors(Reference Adab, Pallan and Whincup89), and that the main source of energy for the Minangkabau is carbohydrates, where rice, banana, cassava, maize, sweet potato, sago, noodles, glutinous rice and mung bean are part of their daily meals(Reference Lipoeto, Mmedsci and Agus86), these findings, if replicated, may have a significant public health implication in preventing CVD in Minangkabau women by developing dietary intervention strategies to reduce the intake of carbohydrates.

There are ethnic differences in body fat composition given the complex interaction between the genes, lifestyle and culture. Understanding of ethnic differences may lead to the implementation of effective approaches to recognise and prevent metabolic diseases across different ethnic groups. It is important that the findings from these studies are replicated before consideration is given to personalised dietary advice for individuals carrying a higher genetic risk of vitamin D deficiency.

Strengths and limitations

Firstly, the studies from the GeNuIne Collaboration are the first nutrigenetic studies to evaluate the relationship of vitamin B12/D status with metabolic disease risk in ethnically diverse populations. Secondly, the construction of the GRSs instead of a single-SNP approach had increased the statistical power to identify gene–diet interactions(Reference Igo, Kinzy and Cooke Bailey51). Thirdly, the use of a comprehensive, validated food frequency questionnaires collected by trained nutritionists increased the accuracy of dietary data collection. The study does have several limitations that should be acknowledged. The studies had a cross-sectional study design, and hence, causality cannot be inferred. Given that the studies were a pilot, the sample size was small; however, the studies were sufficiently powered to identify significant gene–diet interactions. Even though the study used a validated food frequency questionnaire, bias due to self-reported dietary intake information cannot be excluded. Age was adjusted in all the regression analyses; however, it is possible that the unmatched age in cases and controls, especially the study in Asian Indians, might have introduced a bias in the study. Furthermore, other confounders such as sex, BMI, disease status, socioeconomic status and locality, wherever appropriate, were adjusted in all our analyses; but residual confounding due to unknown factors cannot be excluded(Reference Liang, Zhao and Lee90). In addition, these studies investigated only a limited number of the increasingly identified metabolic-associated SNPs, thus there is a need to utilise a comprehensive panel of genetic variants to construct the GRS. Finally, the studies were conducted in specific ethnic groups and hence, the findings cannot be generalised to the countries.

From nutrigenetics to genotype-based dietary recommendations

Although remarkable improvements have been achieved in epidemiological studies in the field of nutrigenetics, future research should focus on understanding the metabolic pathways underlying gene–diet interactions. Therefore, science that identifies the connection between compounds in food and diet, and genetic susceptibility is needed. Food scientists and nutritionists have described a new discipline called ‘Foodomics’, which is defined as the application of new methodologies, or ‘omics’, to improve individual health(Reference Vimaleswaran, Le Roy and Claus91,Reference Ibanez, Valdes and Garcia-Canas92) . This field has helped to identify the interactions of bioactive compounds from the diet at the molecular and cellular levels to provide evidence on their health benefits, and to understand variations and differential response to nutrition interventions.

Another approach is nutrigenomics, which investigates the effect of diet and its bioactive components on gene expression(Reference Fenech, El-Sohemy and Cahill93). This field of research will help in understanding how diet interacts with the metabolic pathways, which may have a role in diet-related diseases. Although nutrigenetics investigates gene–diet interaction or in other words, explores how the genes (at the levels of SNPs) cause the disease in response to a particular diet(Reference Vimaleswaran34). The knowledge from these two fields will help in designing optimal diets that allow health maintenance and disease prevention in an individual(Reference Vimaleswaran, Le Roy and Claus91).

Besides personalised nutrition, precision nutrition is another approach, which is aimed to develop more comprehensive nutritional recommendations based on the interaction between internal and external parameters of an individual's environment throughout life(Reference Heianza and Qi15,Reference Marcum94,Reference de Toro-Martin, Arsenault and Despres95) . Precision nutrition takes into account the genetic factors, dietary habits, food behaviour, physical activity, the microbiota and the metabolome(Reference Bordoni and Gabbianelli96Reference Zeisel99). For implementing precision nutrition, the underlying science should be translated so that clinicians and other health care providers understand the scientific basis of heterogeneity in metabolic diseases and can deliver precision nutrition interventions to people with such chronic diseases. In addition, policy makers will need to understand the underlying science, so that they can enforce the use of precision nutrition in the implementation of policy recommendations and public health interventions. Although nutrigenetic and nutrigenomic studies hold immense promise for preventing metabolic diseases and micronutrient deficiencies, there are several challenges that need to be overcome(Reference Reddy, Palika and Ismail100,Reference Zeisel101) .

In summary, clear guidance from nutrigenetics studies is required for the implementation of personalised nutrition(Reference Kohlmeier, De Caterina and Ferguson102) and foodomics(Reference Ibanez, Valdes and Garcia-Canas92), which can only be achieved by using large and well powered studies, examining various ethnic groups, considering the variety in dietary patterns globally and conducting additional testing for other modifiable factors such as physical activity.

Conclusions

The studies from GeNuIne Collaboration have provided evidence for the influence of the dietary factors on the relationship between vitamin B12/D deficiency and metabolic outcomes(Reference Surendran, Morais and Abdalla36,Reference Surendran, Aji and Ariyasra39,Reference Surendran and Vimaleswaran40,Reference Surendran, Jayashri and Drysdale59,Reference Alathari, Bodhini and Jayashri78,Reference Alathari, Aji and Ariyasra87) and highlighted the existence of genetic heterogeneity in gene–diet interactions across ethnically diverse populations. These differences in gene–diet interactions implicate the significance of personalised approaches for the prevention of vitamin B12 and D deficiencies and metabolic diseases. In terms of implementing ethnic-specific personalised dietary strategies, for Sri Lankans, Indonesians and Brazilians who are carrying a high B12-GRS, it would be possible to prevent the development of metabolic diseases by modifying their dietary daily intakes of protein, fibre and carbohydrate, respectively. For Asian Indians with low metabolic-GRS, a low carbohydrate diet can improve the vitamin D status, and for Southeast Asian women, reducing the intake of carbohydrate-rich foods can overcome the genetic risk of obesity.

It is important that these gene–diet interactions are replicated, before public health recommendations can be enforced. Furthermore, prospective genotyping should be considered in future studies to avoid an imbalance in the frequency of genotype between groups, which might confound the findings, and to increase statistical and discriminatory power(Reference Lovegrove and Gitau103). Also, it is important to further investigate whether people with increased weight require more vitamin B12/D containing foods, for the possibility of implementing micronutrient deficiency screening programmes in the population. If low vitamin B12/D concentrations stimulate metabolic diseases through a dietary influence, it is important that mechanistic studies are carried out to determine how vitamin B12/D interacts with adipose tissue metabolism or how epigenetic mechanisms contribute to the epidemic of metabolic diseases(Reference Sales, Ferguson-Smith and Patti104). These functional studies are highly warranted before applying personalised dietary strategies to prevent or treat these micronutrient deficiencies and metabolic diseases.

GeNuIne Collaborators

Hugh Sinclair Unit of Human Nutrition and Institute for Cardiovascular and Metabolic Research (ICMR), Department of Food and Nutritional Sciences, University of Reading UK: Prof. Julie Lovegrove; Dr Mohan's Diabetes Specialties Centre, Chennai, India: Prof. Mohan Viswanathan, Dr Anjana Mohan, and Dr Guha Pradeepa; Department of Molecular Genetics, Madras Diabetes Research Foundation, Chennai, India: Dr Radha Venkatesan; Department of Foods, Nutrition and Dietetics Research, Madras Diabetes Research Foundation, Chennai, India: Mrs Sudha Vasudevan; School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, UK: Dr Shelini Surendran; Faculty of Nutrition, Federal University of Goiás, Goiânia: Dr Maria Aderuza Horst; Faculty of Medicine, Andalas University, Padang, Indonesia: Prof. Nur Indrawathy Liputo, Dr Finny Fitri Yani; Faculty of Medicine, Universitas Hasanuddin, Makassar, Indonesia: Prof Nurpudji A. Taslim; Department of Pediatrics, Medical Faculty of Sam Ratulangi University, Republic of Indonesia: Dr Nelly Mayulu; Department of Nutrition, Faculty of Health Science, Universitas Alma Ata, Yogyakarta, Indonesia: Dr Arif Sabta Aji; Grupo de Análisis para el Desarrollo, Lima, Peru: Dr Alan Sánchez; Instituto de Investigación Nutricional, Lima, Peru: Prof. Mary E Penny; Department of International Development, University of Oxford, UK: Dr Marta Favara; Centre for Biomedical and Nutrition Research (CBNR), Universiti Tunku Abdul Rahman (Kampar Campus), Malaysia: Dr Anto Cordelia Tanislaus Antony Dhanapal, Dr Eddy Seong Guan Cheah, and Dr Annaletchumy Loganathan; Faculty of Health and Social Care, University of Chester, Chester, UK: Prof. Basma Ellahi; Biochemistry and Biotechnology Department, College of Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana: Dr Reggie Annan; Department of Nutrition and Dietetics, Hacettepe University, Ankara, Turkey: Dr Zehra Buyuktuncer-Demirel; Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK: Dr Sanjay Kinra; Université Hassan II de Casablanca, Laboratoire de Biologie et Santé, Faculté des Sciences Ben M'Sik, Unité de Recherche en Nutrition Humaine, Casablanca, Morocco: Prof. Abdelfettah Derouiche, Dr Ali Jafri; Department of Food Science and Technology, Faculty of Agro-Industry, Kasetsart University, Bangkok, Thailand: Dr Parichat Hongsprabhas, Dr Sudathip Sae-tan; National Institute of Food Science and Technology, Faculty of Food, Nutrition and Home Sciences, University of Agriculture, Faisalabad, Pakistan: Dr Mian Kamran Sharif.

Acknowledgements

K. S. V. acknowledges support from the British Nutrition Foundation and the GeNuIne Collaborators and thanks PhD students, Shelini Surendran and Buthaina Alathari, for their contribution to the GeNuIne Collaboration studies.

Financial Support

None.

Conflict of Interest

None.

Authorship

The author had sole responsibility for all aspects of preparation of this paper.

References

Zobel, DP, Andreasen, CH, Grarup, N et al. (2009) Variants near MC4R are associated with obesity and influence obesity-related quantitative traits in a population of middle-aged people: studies of 14,940 Danes. Diabetes 58, 757764.CrossRefGoogle Scholar
Di Cesare, M, Soric, M, Bovet, P et al. (2019) The epidemiological burden of obesity in childhood: a worldwide epidemic requiring urgent action. BMC Med 17, 212.CrossRefGoogle ScholarPubMed
Niessen, LW, Mohan, D, Akuoku, JK et al. (2018) Tackling socioeconomic inequalities and non-communicable diseases in low-income and middle-income countries under the sustainable development agenda. Lancet 391, 20362046.CrossRefGoogle ScholarPubMed
Miranda, JJ, Barrientos-Gutierrez, T, Corvalan, C et al. (2019) Understanding the rise of cardiometabolic diseases in low- and middle-income countries. Nat Med 25, 16671679.CrossRefGoogle ScholarPubMed
Vimaleswaran, KS & Loos, RJ (2010) Progress in the genetics of common obesity and type 2 diabetes. Expert Rev Mol Med 12, e7.CrossRefGoogle ScholarPubMed
Vimaleswaran, KS (2020) A nutrigenetics approach to study the impact of genetic and lifestyle factors on cardiometabolic traits in various ethnic groups: findings from the GeNuIne Collaboration. Proc Nutr Soc 79, 194204.CrossRefGoogle Scholar
Vimaleswaran, KS (2017) Gene–nutrient interactions on metabolic diseases: findings from the GeNuIne Collaboration. Nutr Bull 42, 8086.CrossRefGoogle Scholar
Vimaleswaran, KS, Cavadino, A & Hypponen, E (2012) Evidence for a genetic interaction in allergy-related responsiveness to vitamin D deficiency. Allergy 67, 10331040.CrossRefGoogle ScholarPubMed
Radha, V, Vimaleswaran, KS, Deepa, R et al. (2003) The genetics of diabetes mellitus. Indian J Med Res 117, 225238.Google ScholarPubMed
Vimaleswaran, KS, Li, S, Zhao, JH et al. (2009) Physical activity attenuates the body mass index-increasing influence of genetic variation in the FTO gene. Am J Clin Nutr 90, 425428.CrossRefGoogle ScholarPubMed
Vimaleswaran, KS, Franks, PW, Barroso, I et al. (2008) Habitual energy expenditure modifies the association between NOS3 gene polymorphisms and blood pressure. Am J Hypertens 21, 297302.CrossRefGoogle ScholarPubMed
Vimaleswaran, KS, Bodhini, D, Lakshmipriya, N et al. (2016) Interaction between FTO gene variants and lifestyle factors on metabolic traits in an Asian Indian population. Nutr Metab (Lond) 13, 39.CrossRefGoogle Scholar
Vimaleswaran, KS, Cavadino, A & Hypponen, E (2013) APOA5 genotype influences the association between 25-hydroxyvitamin D and high density lipoprotein cholesterol. Atherosclerosis 228, 188192.CrossRefGoogle ScholarPubMed
Bodhini, D, Gaal, S, Shatwan, I et al. (2017) Interaction between TCF7L2 polymorphism and dietary fat intake on high density lipoprotein cholesterol. PLoS ONE 12, e0188382.CrossRefGoogle ScholarPubMed
Heianza, Y & Qi, L (2017) Gene–diet interaction and precision nutrition in obesity. Int J Mol Sci 18, 787.CrossRefGoogle ScholarPubMed
Vimaleswaran, KS, Bodhini, D, Jiang, J et al. (2021) Circulating adiponectin mediates the association between omentin gene polymorphism and cardiometabolic health in Asian Indians. PLoS ONE 16, e0238555.CrossRefGoogle ScholarPubMed
Dietrich, S, Jacobs, S, Zheng, JS et al. (2019) Gene–lifestyle interaction on risk of type 2 diabetes: a systematic review. Obes Rev 20, 15571571.CrossRefGoogle ScholarPubMed
Radha, V, Vimaleswaran, KS, Babu, HN et al. (2006) Role of genetic polymorphism peroxisome proliferator-activated receptor-gamma2 Pro12Ala on ethnic susceptibility to diabetes in South-Asian and Caucasian subjects: evidence for heterogeneity. Diabetes Care 29, 10461051.CrossRefGoogle ScholarPubMed
Vimaleswaran, KS (2020) Comment: ‘Evaluation of the association of Omentin 1 rs2274907 A>T and rs2274908 G>A gene polymorphisms with coronary artery disease in Indian population: a case control study’. J Pers Med 10, 190.CrossRefGoogle Scholar
Du, H, Vimaleswaran, KS, Angquist, L et al. (2011) Genetic polymorphisms in the hypothalamic pathway in relation to subsequent weight change – the DiOGenes study. PLoS ONE 6, e17436.CrossRefGoogle ScholarPubMed
Fisher, E, Meidtner, K, Angquist, L et al. (2012) Influence of dietary protein intake and glycemic index on the association between TCF7L2 HapA and weight gain. Am J Clin Nutr 95, 14681476.CrossRefGoogle ScholarPubMed
Isgin-Atici, K, Alsulami, S, Turan-Demirci, B et al. (2021) FTO gene–lifestyle interactions on serum adiponectin concentrations and central obesity in a Turkish population. Int J Food Sci Nutr 72, 375385.CrossRefGoogle Scholar
Matusheski, NV, Caffrey, A, Christensen, L et al. (2021) Diets, nutrients, genes and the microbiome: recent advances in personalised nutrition. Br J Nutr 126, 19.Google ScholarPubMed
Chakraverty, R & Chakraborty, P (2018) Recent insights into the role of vitamin B12 and vitamin D upon cardiovascular mortality: a systematic review. Acta Sci Pharm Sci 2, 6165.Google Scholar
Surendran, S, Adaikalakoteswari, A, Saravanan, P et al. (2018) An update on vitamin B12-related gene polymorphisms and B12 status. Genes Nutr 13, 2.CrossRefGoogle ScholarPubMed
Berry, DJ, Vimaleswaran, KS, Whittaker, JC et al. (2012) Evaluation of genetic markers as instruments for Mendelian randomization studies on vitamin D. PLoS ONE 7, e37465.CrossRefGoogle ScholarPubMed
Alathari, BE, Sabta, AA, Kalpana, CA et al. (2020) Vitamin D pathway-related gene polymorphisms and their association with metabolic diseases: a literature review. J Diabetes Metab Disord 19, 17011729.CrossRefGoogle ScholarPubMed
Vimaleswaran, KS, Cavadino, A, Berry, DJ et al. (2013) Genetic association analysis of vitamin D pathway with obesity traits. Int J Obes (Lond) 37, 13991406.CrossRefGoogle ScholarPubMed
Moen, GH, Qvigstad, E, Birkeland, KI et al. (2018) Are serum concentrations of vitamin B-12 causally related to cardiometabolic risk factors and disease? A Mendelian randomization study. Am J Clin Nutr 108, 398404.CrossRefGoogle ScholarPubMed
Vimaleswaran, KS, Berry, DJ, Lu, C et al. (2013) Causal relationship between obesity and vitamin D status: bi-directional Mendelian randomization analysis of multiple cohorts. PLoS Med 10, e1001383.CrossRefGoogle ScholarPubMed
Jiang, X, Ge, T & Chen, CY (2021) The causal role of circulating vitamin D concentrations in human complex traits and diseases: a large-scale Mendelian randomization study. Sci Rep 11, 184.CrossRefGoogle ScholarPubMed
Dimitrakopoulou, VI, Tsilidis, KK, Haycock, PC et al. (2017) Circulating vitamin D concentration and risk of seven cancers: Mendelian randomisation study. Br Med J 359, j4761.CrossRefGoogle ScholarPubMed
Palaniappan, LP, Carnethon, MR & Fortmann, SP (2002) Heterogeneity in the relationship between ethnicity, BMI, and fasting insulin. Diabetes Care 25, 13511357.CrossRefGoogle ScholarPubMed
Vimaleswaran, KS (2020) A nutrigenetics approach to study the impact of genetic and lifestyle factors on cardiometabolic traits in various ethnic groups: findings from the GeNuIne Collaboration. Proc Nutr Soc 79, 194204.CrossRefGoogle Scholar
Vimaleswaran, KS & Reddy, GB (2021) Comment on ‘Guiding global best practice in personalized nutrition based on genetics: the development of a nutrigenomics care map’. J Acad Nutr Diet 121, 12151216.CrossRefGoogle Scholar
Surendran, S, Morais, CC, Abdalla, DSP et al. (2019) The influence of one-carbon metabolism gene polymorphisms and gene–environment interactions on homocysteine, vitamin B12, folate and lipids in a Brazilian adolescent population. Diabetol 10, 110122.Google Scholar
Sae-Tan, S & Vimaleswaran, KS (2017) From nutrigenetics to personalised nutrition. KMUTNB Int J App Sci Technol 10, 161162.Google Scholar
Surendran, S, Alsulami, S, Lankeshwara, R et al. (2020) A genetic approach to examine the relationship between vitamin B12 status and metabolic traits in a South Asian population. Int J Diabetes Dev Ctries 40, 2131.CrossRefGoogle Scholar
Surendran, S, Aji, AS, Ariyasra, U et al. (2019) A nutrigenetic approach for investigating the relationship between vitamin B12 status and metabolic traits in Indonesian women. J Diabetes Metab Disord 18, 111.CrossRefGoogle ScholarPubMed
Surendran, S & Vimaleswaran, KS (2021) A nutrigenetic approach to examine the relationship between vitamin B12 status and cardio-metabolic traits in multiple ethnic groups – findings from the GeNuIne Collaboration. Nutr Bull 46, 185194.CrossRefGoogle Scholar
Bradfield, JP, Taal, HR, Timpson, NJ et al. (2012) A genome-wide association meta-analysis identifies new childhood obesity loci. Nat Genet 44, 526531.Google ScholarPubMed
Willer, CJ, Speliotes, EK, Loos, RJ et al. (2009) Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nat Genet 41, 2534.Google ScholarPubMed
Ng, MC, Hester, JM, Wing, MR et al. (2012) Genome-wide association of BMI in African Americans. Obesity (Silver Spring) 20, 622627.CrossRefGoogle ScholarPubMed
Schlauch, KA, Read, RW, Lombardi, VC et al. (2020) A comprehensive genome-wide and phenome-wide examination of BMI and obesity in a northern Nevadan cohort. G3 (Bethesda) 10, 645664.CrossRefGoogle Scholar
Vimaleswaran, KS, Tachmazidou, I, Zhao, JH et al. (2012) Candidate genes for obesity-susceptibility show enriched association within a large genome-wide association study for BMI. Hum Mol Genet 21, 45374542.CrossRefGoogle ScholarPubMed
Nongmaithem, SS, Joglekar, CV, Krishnaveni, GV et al. (2017) GWAS identifies population-specific new regulatory variants in FUT6 associated with plasma B12 concentrations in Indians. Hum Mol Genet 26, 2589.CrossRefGoogle ScholarPubMed
Jiang, X, O'Reilly, PF, Aschard, H et al. (2018) Genome-wide association study in 79,366 European-ancestry individuals informs the genetic architecture of 25-hydroxyvitamin D levels. Nat Commun 9, 260.CrossRefGoogle ScholarPubMed
Udler, MS, McCarthy, MI, Florez, JC et al. (2019) Genetic risk scores for diabetes diagnosis and precision medicine. Endocr Rev 40, 15001520.CrossRefGoogle ScholarPubMed
Lewis, CM & Vassos, E (2020) Polygenic risk scores: from research tools to clinical instruments. Genome Med 12, 44.CrossRefGoogle ScholarPubMed
Torkamani, A & Topol, E (2019) Polygenic risk scores expand to obesity. Cell 177, 518520.CrossRefGoogle ScholarPubMed
Igo, RP Jr, Kinzy, TG & Cooke Bailey, JN (2019) Genetic risk scores. Curr Protoc Hum Genet 104, e95.Google ScholarPubMed
Huls, A, Kramer, U, Carlsten, C et al. (2017) Comparison of weighting approaches for genetic risk scores in gene–environment interaction studies. BMC Genet 18, 115.CrossRefGoogle ScholarPubMed
Xie, T, Wang, B, Nolte, IM et al. (2020) Genetic risk scores for complex disease traits in youth. Circ Genom Precis Med 13, e002775.CrossRefGoogle ScholarPubMed
Knight, BA, Shields, BM, Brook, A et al. (2015) Lower circulating B12 is associated with higher obesity and insulin resistance during pregnancy in a non-diabetic white British population. PLoS ONE 10, e0135268.CrossRefGoogle Scholar
Mahabir, S, Ettinger, S, Johnson, L et al. (2008) Measures of adiposity and body fat distribution in relation to serum folate levels in postmenopausal women in a feeding study. Eur J Clin Nutr 62, 644650.CrossRefGoogle Scholar
Lee, YJ, Wang, MY, Lin, MC et al. (2016) Associations between vitamin B-12 status and oxidative stress and inflammation in diabetic vegetarians and omnivores. Nutrients 8, 118.CrossRefGoogle ScholarPubMed
Mohan, V & Deepa, R (2006) Adipocytokines and the expanding ‘Asian Indian phenotype’. J Assoc Physicians India 54, 685686.Google ScholarPubMed
Patel, SA, Shivashankar, R, Ali, MK et al. (2016) Is the ‘South Asian phenotype’ unique to South Asians?: comparing cardiometabolic risk factors in the CARRS and NHANES studies. Glob Heart 11, 8996, e83.CrossRefGoogle Scholar
Surendran, S, Jayashri, R, Drysdale, L et al. (2019) Evidence for the association between FTO gene variants and vitamin B12 concentrations in an Asian Indian population. Genes Nutr 14, 26.CrossRefGoogle Scholar
Jayawardena, R (2014) Energy and nutrient intakes among Sri Lankan adults. Int Arch Med 7, 3434.CrossRefGoogle ScholarPubMed
Medagama, A, Fernando, D & Widanapathirana, H (2015) Energy and nutrient intakes of Sri Lankan patients with type 2 diabetes mellitus: a cross-sectional survey. BMC Res Notes 8, 753.CrossRefGoogle ScholarPubMed
Schroders, J, Wall, S, Hakimi, M et al. (2017) How is Indonesia coping with its epidemic of chronic noncommunicable diseases? A systematic review with meta-analysis. PLoS ONE 12, e0179186.CrossRefGoogle Scholar
IDF (2017) International Diabetes Federation. IDF Diabetes Atlas. Brussels, Belgium: International Diabetes Federation.Google Scholar
Conde, WL & Monteiro, CA (2014) Nutrition transition and double burden of undernutrition and excess of weight in Brazil. Am J Clin Nutr 100, 1617S1622S.CrossRefGoogle ScholarPubMed
Monteiro, CA, Conde, WL & Popkin, BM (2004) The burden of disease from undernutrition and overnutrition in countries undergoing rapid nutrition transition: a view from Brazil. Am J Public Health 94, 433434.CrossRefGoogle ScholarPubMed
Monteiro, CA, Mondini, L, de Souza, AL et al. (1995) The nutrition transition in Brazil. Eur J Clin Nutr 49, 105113.Google ScholarPubMed
Ribeiro, AL, Duncan, BB, Brant, LC et al. (2016) Cardiovascular health in Brazil: trends and perspectives. Circulation 133, 422433.CrossRefGoogle Scholar
Souza, RA, Yokoo, EM, Sichieri, R et al. (2015) Energy and macronutrient intakes in Brazil: results of the first nationwide individual dietary survey. Public Health Nutr 18, 30863095.CrossRefGoogle ScholarPubMed
Morenga, LT, Williams, S, Brown, R et al. (2010) Effect of a relatively high-protein, high-fiber diet on body composition and metabolic risk factors in overweight women. Eur J Clin Nutr 64, 13231331.CrossRefGoogle ScholarPubMed
Anderson, JW, Baird, P, Davis, RH Jr et al. (2009) Health benefits of dietary fiber. Nutr Rev 67, 188205.CrossRefGoogle ScholarPubMed
Kaczmarczyk, MM, Miller, MJ & Freund, GG (2012) The health benefits of dietary fiber: beyond the usual suspects of type 2 diabetes mellitus, cardiovascular disease and colon cancer. Metabolism 61, 10581066.CrossRefGoogle ScholarPubMed
Hossein-nezhad, A & Holick, MF (2013) Vitamin D for health: a global perspective. Mayo Clin Proc 88, 720755.CrossRefGoogle ScholarPubMed
Wacker, M & Holick, MF (2013) Sunlight and vitamin D: a global perspective for health. Dermatoendocrinology 5, 51108.CrossRefGoogle ScholarPubMed
Vimaleswaran, KS, Forouhi, NG & Khunti, K (2021) Vitamin D and COVID-19. Br Med J 372, n544.CrossRefGoogle ScholarPubMed
Valer-Martinez, A, Martinez, JA, Sayon-Orea, C et al. (2019) Vitamin D and cardio-metabolic risk factors in overweight adults: an overview of the evidence. Curr Pharm Des 25, 24072420.CrossRefGoogle Scholar
Vimaleswaran, KS, Cavadino, A, Berry, DJ et al. (2014) Association of vitamin D status with arterial blood pressure and hypertension risk: a Mendelian randomisation study. Lancet Diabetes Endocrinol 2, 719729.CrossRefGoogle ScholarPubMed
Vimaleswaran, KS, Power, C & Hypponen, E (2014) Interaction between vitamin D receptor gene polymorphisms and 25-hydroxyvitamin D concentrations on metabolic and cardiovascular disease outcomes. Diabetes Metab 40, 386389.CrossRefGoogle ScholarPubMed
Alathari, BE, Bodhini, D, Jayashri, R et al. (2020) A nutrigenetic approach to investigate the relationship between metabolic traits and vitamin D status in an Asian Indian population. Nutrients 12, 1357.CrossRefGoogle Scholar
Selvarajan, S, Gunaseelan, V, Anandabaskar, N et al. (2017) Systematic review on vitamin D level in apparently healthy Indian population and analysis of its associated factors. Indian J Endocrinol Metab 21, 765775.CrossRefGoogle ScholarPubMed
Harinarayan, CV, Ramalakshmi, T, Prasad, UV et al. (2007) High prevalence of low dietary calcium, high phytate consumption, and vitamin D deficiency in healthy south Indians. Am J Clin Nutr 85, 10621067.CrossRefGoogle ScholarPubMed
Jayashri, R, Venkatesan, U, Shanthirani, CS et al. (2020) Prevalence of vitamin D deficiency in urban south Indians with different grades of glucose tolerance. Br J Nutr 124, 18.Google ScholarPubMed
Aji, AS, Erwinda, E, Rasyid, R et al. (2020) A genetic approach to study the relationship between maternal vitamin D status and newborn anthropometry measurements: the vitamin D pregnant mother (VDPM) cohort study. J Diabetes Metab Disord 19, 91103.CrossRefGoogle Scholar
Aji, AS, Erwinda, E, Yusrawati, Y et al. (2019) Vitamin D deficiency status and its related risk factors during early pregnancy: a cross-sectional study of pregnant Minangkabau women, Indonesia. BMC Pregnancy Childbirth 19, 183.CrossRefGoogle Scholar
Ilmiawati, C, Oviana, A, Friadi, A et al. (2020) Sunlight exposed body surface area is associated with serum 25-hydroxyvitamin D (25(OH)D) level in pregnant Minangkabau women, Indonesia. BMC Nutr 6, 18.CrossRefGoogle Scholar
Lipoeto, NI, Agus, Z, Oenzil, F et al. (2004) Dietary intake and the risk of coronary heart disease among the coconut-consuming Minangkabau in West Sumatra, Indonesia. Asia Pac J Clin Nutr 13, 377384.Google ScholarPubMed
Lipoeto, NI, Mmedsci, , Agus, Z et al. (2001) Contemporary Minangkabau food culture in West Sumatra, Indonesia. Asia Pac J Clin Nutr 10, 1016.CrossRefGoogle ScholarPubMed
Alathari, BE, Aji, AS, Ariyasra, U et al. (2021) Interaction between vitamin D-related genetic risk score and carbohydrate intake on body Fat composition: a study in Southeast Asian Minangkabau women. Nutrients 13, 326.CrossRefGoogle ScholarPubMed
Pittas, AG, Lau, J, Hu, FB et al. (2007) The role of vitamin D and calcium in type 2 diabetes. A systematic review and meta-analysis. J Clin Endocrinol Metab 92, 20172029.CrossRefGoogle ScholarPubMed
Adab, P, Pallan, M & Whincup, PH (2018) Is BMI the best measure of obesity? Br Med J 360, k1274.CrossRefGoogle ScholarPubMed
Liang, W, Zhao, Y & Lee, AH (2014) An investigation of the significance of residual confounding effect. Biomed Res Int 2014, 658056.CrossRefGoogle ScholarPubMed
Vimaleswaran, KS, Le Roy, CI & Claus, SP (2015) Foodomics for personalized nutrition: how far are we? Curr Opin Food Sci 4, 129135.CrossRefGoogle Scholar
Ibanez, C, Valdes, A, Garcia-Canas, V et al. (2012) Global foodomics strategy to investigate the health benefits of dietary constituents. J Chromatogr A 1248, 139153.CrossRefGoogle ScholarPubMed
Fenech, M, El-Sohemy, A, Cahill, L et al. (2011) Nutrigenetics and nutrigenomics: viewpoints on the current status and applications in nutrition research and practice. J Nutrigenet Nutrigenomics 4, 6989.CrossRefGoogle Scholar
Marcum, JA (2020) Nutrigenetics/nutrigenomics, personalized nutrition, and precision healthcare. Curr Nutr Rep 9, 338345.CrossRefGoogle ScholarPubMed
de Toro-Martin, J, Arsenault, BJ, Despres, JP et al. (2017) Precision nutrition: a review of personalized nutritional approaches for the prevention and management of metabolic syndrome. Nutrients 9, 913.CrossRefGoogle ScholarPubMed
Bordoni, L & Gabbianelli, R (2019) Primers on nutrigenetics and nutri(epi)genomics: origins and development of precision nutrition. Biochimie 160, 156171.CrossRefGoogle ScholarPubMed
Ozdemir, V & Kolker, E (2016) Precision nutrition 4⋅0: a big data and ethics foresight analysis – convergence of agrigenomics, nutrigenomics, nutriproteomics, and nutrimetabolomics. OMICS 20, 6975.CrossRefGoogle Scholar
Palmnas, M, Brunius, C, Shi, L et al. (2020) Perspective: metabotyping – a potential personalized nutrition strategy for precision prevention of cardiometabolic disease. Adv Nutr 11, 524532.CrossRefGoogle ScholarPubMed
Zeisel, SH (2020) Precision (personalized) nutrition: understanding metabolic heterogeneity. Annu Rev Food Sci Technol 11, 7192.CrossRefGoogle ScholarPubMed
Reddy, VS, Palika, R, Ismail, A et al. (2018) Nutrigenomics: opportunities & challenges for public health nutrition. Indian J Med Res 148, 632641.CrossRefGoogle ScholarPubMed
Zeisel, SH (2010) A grand challenge for nutrigenomics. Front Genet 1, 2.CrossRefGoogle ScholarPubMed
Kohlmeier, M, De Caterina, R, Ferguson, LR et al. (2016) Guide and position of the international society of nutrigenetics/nutrigenomics on personalized nutrition: part 2 – ethics, challenges and endeavors of precision nutrition. J Nutrigenet Nutrigenomics 9, 2846.CrossRefGoogle ScholarPubMed
Lovegrove, JA & Gitau, R (2008) Personalized nutrition for the prevention of cardiovascular disease: a future perspective. J Hum Nutr Diet 21, 306316.CrossRefGoogle ScholarPubMed
Sales, VM, Ferguson-Smith, AC & Patti, ME (2017) Epigenetic mechanisms of transmission of metabolic disease across generations. Cell Metab 25, 559571.CrossRefGoogle Scholar
Figure 0

Fig. 1. Objective, study design and the expected results of the nutrigenetic studies from the GeNuIne Collaboration. Genetic associations are represented by one-sided arrows with unbroken lines and interactions between GRS and dietary intakes on metabolic traits and vitamin D/B12 status are shown as one-sided arrows with broken lines. The association of the metabolic-GRS with vitamin B12/D status and metabolic traits, respectively, and the association of vitamin B12/D-GRS with vitamin B12/D status and metabolic traits, respectively, were tested. In addition, the effect of dietary factors on these genetic associations was examined.

Figure 1

Fig. 2. Results from the vitamin B12-related nutrigenetic studies in South Asians and Southeast Asians. (a) A nutrigenetic study in Asian Indians(59): metabolic disease risk increasing alleles ranged from 0 to 3. The white bars indicate individuals with 0 risk alleles and the black bars indicate individuals carrying ≥1 alleles. Individuals who carried 1 or more risk alleles had significantly lower B12 concentrations compared to individuals carrying 0 risk alleles (P = 0⋅018). (b) A nutrigenetic study in Southeast Asians (Indonesia)(39): individuals who carried nine or more risk alleles for vitamin B12 deficiency (high B12-GRS) had significantly higher HbAC1 concentrations (ng/ml) in the lowest tertile of fibre intake (g) (mean(sd): 4⋅90(sd 1⋅00) g) compared to those with eight or less risk alleles for vitamin B12 deficiency (low B12-GRS). GRS: genetic risk score; HbAC1, haemoglobin AC1.

Figure 2

Table 1. Details of the SNPs that were examined in each ethnic group

Figure 3

Fig. 3. Results from the vitamin D-related nutrigenetic studies in South Asians and Southeast Asians. (a) A nutrigenetic study in Asian Indians(78): interaction between metabolic GRS and carbohydrate intake (%) on log 25(OH)D. White bars indicate individuals with GRS ≤1 risk allele; black bars indicate individuals with GRS >1 risk allele. Among individuals with low carbohydrates intake, those with <1 risk allele had significantly higher 25(OH)D concentrations compared to those with >1 risk allele (P = 0⋅003). (b) A nutrigenetic study in Southeast Asians (Indonesia)(87): interaction between the vitamin D-GRS and dietary carbohydrate intake (g) on body fat percentage (%) (Pinteraction = 0⋅049). Those who were on the highest tertile of carbohydrate intake and carried >2 risk alleles had significantly higher body fat percentage compared to individuals carrying ≤2 risk alleles (P = 0⋅016). GRS: genetic risk score.

You have Access Open access
1
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.

GeNuIne (gene–nutrient interactions) Collaboration: towards implementing multi-ethnic population-based nutrigenetic studies of vitamin B12 and D deficiencies and metabolic diseases
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.

GeNuIne (gene–nutrient interactions) Collaboration: towards implementing multi-ethnic population-based nutrigenetic studies of vitamin B12 and D deficiencies and metabolic diseases
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.

GeNuIne (gene–nutrient interactions) Collaboration: towards implementing multi-ethnic population-based nutrigenetic studies of vitamin B12 and D deficiencies and metabolic diseases
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? *