Table 1.
Population-based studies of the interaction between genetics and sugar-sweetened beverage (SSB) consumption on various health outcomes.
| Author (reference) | Year | n | Region | Age, year mean (SD) | Female, % | BMI, kg/m2mean (SD) | Total energy intake, kcal/d mean (SD) | SSB, serving/day mean (SD)a | Outcomes | Key Observations |
|---|---|---|---|---|---|---|---|---|---|---|
| Qi (26) | 2012 | 33,097 | United States | 53.2 ± 7.17 | 86.6 | 25.6 ± 4.64 | 1,742 ± 518 | 0.28 ± 0.60 | 4-year change in BMI, incident obesity | Significant positive interaction between SSB consumption and BMI genetic risk score on 4-year change in BMI and incident obesity (p < 0.001) |
| The increases in BMI per increment of 10 risk alleles were 1.00 for a consumption of less than one serving per month, 1.12 for one to four servings per month, 1.38 for two to six servings per week, and 1.78 for one or more servings per day (p < 0.001 for interaction) | ||||||||||
| The relative risks of incident obesity per increment of 10 risk alleles were 1.19 (95% confidence interval [CI], 0.90–1.59), 1.67 (95% CI, 1.28–2.16), 1.58 (95% CI, 1.01–2.47), and 5.06 (95% CI, 1.66–15.5) (p = 0.02 for interaction) | ||||||||||
| Batt (67) | 2014 | 1,634 (n = 925 cases) | New Zealand (Polynesian and Caucasian) | 50.2 (17–94) | 34.5 | 32.0 (18.1–77.0)b | NA | NA | Gout, and serum urate levels | Significant positive interaction between SSB consumption and SLC2A9 genotype on gout risk (p = 0.010), whereas among carriers of the gout-protective allele of SLC2A9, each extra daily SSB serving associated with a 15% increase in risk (p = 0.078), compared with a 12% increase in non-carriers (p = 0.002). The interaction term was significant in pooled (pInteraction = 0.01), but not meta-analyzed (pInteraction = 0.99) data. In the US cohort, with each extra daily serving, a greater increase in serum uric acid was observed among protective allele carriers (0.005 (p = 8.7 × 10−5) compared with 0.002 (p = 0.016) mmol/L) |
| 7,075 (n = 148 cases) | United States (Caucasian) | 53.8 (44–65) | 52.6 | 26.4 (14.4–54.6)b | ||||||
| Nobili (57) | 2014 | 200 | Italy | 11 (10,13)c | 56.0 | 25.1 (22,27.4)c | NA | NA | Steatosis severity (%) | Significant positive interaction between consumption of SSB and PNPLA3 I148M genotype on severity of steatosis (p = 0.033) |
| Sonestedt (50) | 2015 | 26,455 | Sweden | 57.9 ± NA | 62.5 | 25.7 ± NA | 2,280 ± NA | 0.23 ± NA | Incident CVD, and TG, HDL-C, and LDL-C | No significant interactions observed between SSB consumption and outcomes (p > 0.05) |
| Brunkwall (27) | 2016 | 26,726 | Sweden | 56.3 ± 7.87 | 62.1 | 25.7 ± 3.8 | 2,173 ± 606 | 0.31 ± 0.57 | BMI | Significant positive interaction between SSB consumption and BMI genetic risk score on BMI (p < 0.05) |
| Olsen (28) | 2016 | 4,765 | Denmark | 47.6 ± NA | 50.3 | NA | 2,143 ± NA | 0.05 ± NA | Change in body weight, waist circumference, waist-to-hip ratio regressed on BMI | Significant negative interaction between soft drink consumption and waist circumference genetic risk score on change in body weight (p < 0.01). Significant positive interaction between soft drink consumption and both BMI and adiposity genetic risk scores on waist circumference change (p = 0.001). |
| Zheng (51) | 2016 | 3,311 (n = 1,560 cases) | Costa Rica | 57.7 ± 11.7 | 24.5 | 26.2 ± 4.11 | 2,598 ± 862 | 1.79 ± 1.44 | Myocardial infarction (based on WHO criteria) | Significant positive interaction between SSB consumption and per-risk allele of rs4977574 increased risk of myocardial infarction (p < 0.05) |
| A genetic risk score derived from three SNPs in the same locus also showed a significant interaction with SSB consumption on MI risk | ||||||||||
| Hosseini-Esfahani (41) | 2017 | 828 (n = 414 cases) | Iran | 42.3 ± 12.5 | 44.0 | 24.5 ± 4.0 | 2,338 ± 1,025 | NA | Metabolic syndrome (based on modified National Cholesterol Education Program/Adult Treatment panel III (ATP III) definition) | Significant positive interaction between SSB consumption and specific haplotypes at APOA1/APOC3 loci (GA+AA rs670/CT+TT rs5069/CC rs5128 genotypes) on risk of MetS (p = 0.03) |
| Note: when accounting for multiple comparisons, interaction no longer significant | ||||||||||
| McKeown and Dashti (37) | 2017 | 37,748 | United States, Netherlands, Finland, Denmark, Sweden Australia | 55.7 ± 7.1 | 56.4 | 26.9 ± 4.44 | 1,994 ± 644 | 0.31 ± 0.67 | Fasting glucose and fasting insulin | Suggestive interaction was observed between genetic variant in KLB (rs1542423) and SSB consumption on FI (p = 0.006). No other significant interactions found between selected genetic variants in CHREBP–FGF21 pathway and FG/FI. |
aSSB consumption was ascertained by a semi-quantitative food-frequency questionnaire alone (Qi et al., Hosseini-Esfahani et al., Zheng et al., Nobili et al., and Batt et al.,) or a combination of semi-quantitative food-frequency questionnaire or 7-day food diary (McKeown and Dashti et al., Olsen et al., and Brunkwall et al.). SSB consumption includes fruit juices in the following studies: Qi et al., McKeown and Dashti et al., Zheng et al., and Batt et al.
bRange.
cMedian (interquartile range).
BMI, body mass index; CVD, cardiovascular disease; FG, fasting glucose; FI, fasting insulin; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; MetS, metabolic syndrome; MI, myocardial infarction; SSB, sugar-sweetened beverages; TG, triglyceride; WHO, World Health Organization.