Impact of Genetic Risk Score and Dietary Protein Intake on Vitamin D Status in Young Adults from Brazil

Abstract

Given the relationship between vitamin D deficiency (VDD) and adverse outcomes of metabolic diseases, we investigated the interplay of dietary and genetic components on vitamin D levels and metabolic traits in young adults from Brazil. Genetic analysis, dietary intake, and anthropometric and biochemical measurements were performed in 187 healthy young adults (19–24 years). Genetic risk scores (GRS) from six genetic variants associated with vitamin D (vitamin D-GRS) and 10 genetic variants associated with metabolic disease (metabolic-GRS) were constructed. High vitamin D-GRS showed a significant association with low 25(OH)D concentrations (p = 0.001) and high metabolic-GRS showed a significant association with high fasting insulin concentrations (p = 0.045). A significant interaction was found between vitamin D-GRS and total protein intake (g/day) (adjusted for non-animal protein) on 25(OH)D (pinteraction = 0.006), where individuals consuming a high protein diet (≥73 g/d) and carrying >4 risk alleles for VDD had significantly lower 25(OH)D (p = 0.002) compared to individuals carrying ≤4 risk alleles. Even though our study did not support a link between metabolic-GRS and vitamin D status, our study has demonstrated a novel interaction, where participants with high vitamin D-GRS and consuming ≥73 g of protein/day had significantly lower 25(OH)D levels. Further research is necessary to evaluate the role of animal protein consumption on VDD in Brazilians.

Interactions between genetic and lifestyle factors on cardiometabolic disease-related outcomes in Latin American and Caribbean populations: A systematic review

“…Interaction between dietary fat intake and genetic variants on glycemic traits was investigated by two cross-sectional studies ( 72 , 73 ) using data from the BOLD study. In the first study which consisted of 200 participants ( 72 ), a high total fat intake [37.98% of total energy intake (TEI)/day] was shown to interact with a 10-SNP metabolic-GRS ( Table 1 ), where individuals with 5 or more risk alleles had increased homeostasis model assessment estimate of insulin secretion (HOMA-B) ( P interaction = 0.016), fasting insulin ( P interaction = 0.017), body fat mass ( P interaction = 0.009), and decreased insulin:glucose ratio ( P interaction = 0.01), but the interaction did not influence homeostasis model assessment estimate of insulin resistance (HOMA-IR), glycated hemoglobin (HbA1c), or waist circumference (WC).…”

“…Similarly, in the BOLD study discussed above (66), those with a protein intake of 16.99% TEI who were carriers of the "AA" genotype of FUT2 SNP rs602662 (P interaction = 0.007) had increased homocysteine levels (66). However, in the other BOLD study (73), there were no interactions between protein intake and a GRS based on 10 SNPs (Table 1) on obesity or diabetes traits. Frontiers in Nutrition 13 frontiersin.org…”

“…In the first study which consisted of 200 participants (72), a high total fat intake [37.98% of total energy intake (TEI)/day] was shown to interact with a 10-SNP metabolic-GRS (Table 1), where individuals with 5 or more risk alleles had increased homeostasis model assessment estimate of insulin secretion (HOMA-B) (P interaction = 0.016), fasting insulin (P interaction = 0.017), body fat mass (P interaction = 0.009), and decreased insulin:glucose ratio (P interaction = 0.01), but the interaction did not influence homeostasis model assessment estimate of insulin resistance (HOMA-IR), glycated hemoglobin (HbA1c), or waist circumference (WC). Similarly, the second BOLD study (73) which also examined the interaction between dietary fat intake and a 10-SNP metabolic-GRS did not find significant interactions between the GRS and dietary fat intake on fasting glucose, fasting insulin or HbA1c (Table 1). The mechanisms through which dietary fat intake influence glycemic traits are unclear, although a sustained increase in blood glucose levels following a high fat meal has been reported (74).…”

“… A heat map showing the findings for gene-lifestyle interactions on overall cardiometabolic disease risk. Alsulami et al ( 72 ), Metabolic-GRS = TCF7L2 (rs12255372, rs7903146); MC4R (rs17782313, rs2229616); PPAR γ (rs1801282); FTO (rs8050136); CDKN2A/2B (rs10811661); KCNQ1 (rs2237892); CAPN10 (rs5030952); Alathari et al ( 73 ), Vitamin D-GRS = VDR (rs2228570, rs7975232), DHCR7 (rs12785878), CYP2R1(rs12794714), CYP24A1(rs6013897), GC (rs2282679), FTO (rs8050136, rs10163409), TCF7L2 (rs12255372, rs7903146), MC4R (rs17782313), KCNQ1 (rs2237895, rs2237892), CDKN2A (rs10811661), PPAR γ (rs1801282), CAPN10 (rs5030952); Costa-Urrutia et al ( 118 ), Obesity-GRS = ABCA1 (rs2230806, rs9282541); ADIPOQ (rs2241766); ADRB2 (rs1042713); AGT (rs699); APOA4 (rs675); APOB (rs512535); APOE (rs405509); CAPN10 (rs2975760, rs2975762, rs3792267); FTO (rs1121980, rs9939609); HNF4 (rs745975); LIPC (rs1800588); LPL (rs320); PPAR- α (rs1800206); PPAR-γ (rs1801282); SCARB1 (rs1084674); TCF7L2 (rs7903146); TNF (rs361525); TRHR (rs1689249, rs783255 2); Norde et al ( 79 ), 5-SNPs = IL10 rs1554286, rs1800871, rs1800872, rs1800890, rs3024490 ; Oki et al ( 78 ), 4-SNPs = TNF- α rs1799724, rs1800629, rs361525, rs1799964; Norde et al ( 76 ), 4-SNPs = TLR4 rs11536889, rs4986790, rs4986791, rs5030728; Oki et al ( 77 ), 3-SNPs = CRP rs1205, rs1417938, rs2808630; Norde et al ( 79 ), 4-SNPs = IL1B rs16944, rs1143623, rs1143627, rs1143643; 3-SNPs = rs1800795, rs1800796, rs1800797; BR, Brazilian; ME, Mexican; PR, Puerto Rican; AR, Argentinian. …”

Implementation of Nutrigenetics and Nutrigenomics Research and Training Activities for Developing Precision Nutrition Strategies in Malaysia

“…In addition, future considerations include the use of genetic risk scores rather than individual SNPs, large sample sizes, and strategies to improve follow-up. The inclusion of data on total energy and macronutrient intake, data on specific types of food or micronutrients, as well as accounting for various confounding factors in the statistical analysis, need to be taken into consideration [ 26 , 39 , 51 , 54 , 55 ]. To this end, training workshops will include ethics protocols, software for data collection and analysis, and research methods to enhance reproducibility (see Supplementary Table S1 ).…”