In this meta-analysis involving more than 34,748 participants free of type 2 diabetes in 11 cohort studies from the CHARGE Consortium, we observed significant associations between SSB intake and fasting glucose and insulin concentrations, independent of demographics, overall adiposity, total energy intake and other dietary factors. We adjusted for BMI to consider whether obesity may be in the causal pathway between SSB and fasting insulin or glucose, since consuming SSB may lead to a higher BMI, and a higher BMI is associated with worsening glycaemic traits. We observed that the results remained largely the same without attenuation after accounting for BMI, suggesting that although SSBs may increase body weight and adiposity, the relationship with glycaemic traits is independent from adiposity. For each additional serving of SSBs, fasting insulin was 3% higher. The SSB association with fasting glucose was less consistent. Significant associations were observed only in the replication cohorts, and in the meta-analysis of all cohorts only in women. There was no evidence of SNP–SSB interactions in the meta-analysis of all cohorts or in the sex-stratified analysis.

This is the first meta-analysis to assess the association of SSB intake with measures of diabetic risk factors and confirms the positive association between SSB consumption and insulin resistance (HOMA-IR or fasting insulin) suggested by cross-sectional studies in adults [10, 11], young children [49] and adolescents [50, 51]. However, in well-controlled, short-term intervention studies in healthy adults, the evidence is less consistent with some studies reporting that consumption of fructose-containing sugars for 3–10 weeks has a detrimental effect on insulin sensitivity [5, 6, 52], whereas others observed no significant detrimental effect on insulin resistance [53, 54]. Nevertheless, given the observed associations between SSB intake and risk of diabetes [2, 3, 55], our results further favour efforts to assess the potential beneficial effects of reducing SSB consumption on cardiometabolic risk factors in human populations.

In this meta-analysis, we confirmed the previously reported SNP associations with fasting glucose and insulin in GCK-rs4607517 [47], GCKR-rs1260326 [48], SLC2A2-rs11920090 [47] and GCKR-rs1260326 in women only [48]. We also observed a positive association between fasting glucose and the FADS1-rs174546 variant, which is in linkage disequilibrium with FADS1-rs174550, recognised as having an association with fasting glucose [47] and also in linkage disequilibrium with the FADS1-rs174547 variant, which has been associated with atherogenic dyslipidaemia. While we have not formally investigated the relationship between SSB intake and our selected SNPs, our lookup in a large macronutrient intake genome-wide association study from the CHARGE Consortium indicates an association between the FGF21-rs8381.33 variant and carbohydrate intake (ESM Table 17).

To date, few studies have considered whether genetic variation impacts the susceptibility to the detrimental effects of SSB intake on key cardiometabolic traits. In a large cohort of men and women in the USA [56], as well as two large Swedish [57] and Finnish cohorts [58], SSB intake significantly interacted with underlying genetic predisposition for weight gain and obesity risk. More recently, daily SSB intake was observed to interact with variants in the 9p21 region to exacerbate the genetic predisposition effects on coronary artery disease in Hispanics living in Costa Rica [59]. In Hispanic children, the effects of PNPLA3 on liver fat were exacerbated under conditions of a high carbohydrate diet, in particular high sugar intake [60]. Although limited to a few studies, the findings indicate that SSB intake may interact with genetic variants to increase cardiometabolic risk in susceptible individuals.

Here, we pursued a candidate approach to examine whether SNPs in a ChREBP-FGF21 pathway might interact with SSB intake to regulate glycaemic traits. In the discovery phase of our analysis, we identified a promising interaction between the KLB SNP (rs1542423) and SSB for fasting insulin. We observed that individuals who carried a T allele in this SNP consistently had a higher level of fasting insulin in response to high SSB intake in five of the six discovery cohorts. Because these data were consistent with our hypothesis that variants in a ChREBP-FGF21 signalling axis might regulate metabolic traits in response to SSB intake, we sought out replication cohorts to further test this suggestive interaction. The interaction between SSB intake and KLB-rs1542423 for fasting insulin was not significant in the replication cohorts, or in the combined meta-analysis of all 11 participating cohorts, suggesting a false-positive finding. Because we observed a sex-specific main association between SSB intake and fasting glucose, we pursued sex-stratified interaction analyses of SSB intake by selected SNPs as a secondary analysis. We observed a suggestive interaction between SSB intake with one SNP in men (FGF21-rs838133) and one SNP (GCK-rs4607517) in women for fasting insulin.

There are several limitations to our study. One limitation is the focus on a small number of SNPs in a hypothesised candidate gene pathway. While this excludes many other genes and regulatory regions, the focus provides a testable hypothesis and reduces the penalty for genome-wide testing. Sufficiently large populations with the requisite genotyping, phenotyping and dietary information do not yet exist to achieve statistical power sufficient for a genome-wide approach. A second limitation is the heterogeneity within the discovery cohorts as well as heterogeneity between the discovery and replication cohorts. Although cohort inclusion is based upon European ancestry, each cohort has unique characteristics in terms of location, age, sex and covariate structure. For example, participants were, on average, younger in the replication cohorts compared with the discovery cohorts (mean age 54.2 vs 57.6 years). We have also observed additional significant differences in fasting insulin and SSB intake, among other general characteristics including BMI, smoking, education and energy intake (ESM Table 18). Although we attempted to adjust for age in our regression models, this difference in age could have a non-linear impact on the effects of the variant on SSB-induced insulin resistance, thereby contributing to residual confounding. The difference in age could contribute to the difference in SSB intake as the mean SSB intake was higher in the replication cohorts (0.19 to 0.98 servings/day) compared with the discovery cohorts (0.10 to 0.32 servings/day) (p < 0.0001). Furthermore, meta-regression findings suggest differences in the magnitude, but not significance, of the associations between SSB intake and fasting insulin. This may be a result of differences in the moderator in the analysis, such as age and BMI, or as a result of other trait differences in the cohorts. Despite those differences, the associations still remain in subgroup analyses, with the exception of subgroup analyses by sample size, possibly as a result of low power in the analyses with smaller cohorts.

It is important to note that these analyses use a cross-sectional design, incorporating the phenotypic measures (fasting glucose and fasting insulin) and SSB intake at one point in time. Although many of the cohorts contributing to the analyses are longitudinal in nature, not all have measures of outcome and exposure longitudinally. Thus, we did not capture long-term SSB intake patterns, which probably change with age, and thus misclassification of dietary exposure may vary across cohorts. Furthermore, SSB intake was significantly associated with fasting glucose among women, but not men. It has been noted that the effects of excessive sugars on glycaemic traits in animal models are sexually dimorphic although the pattern is not the same as observed here [61]. Though we have no mechanistic explanation for the difference at this time, our results support the need for future studies concerning the metabolic effects of SSBs to carefully consider sex-based stratification.

Finally, the use of self-reported data in our assessment of dietary intake may be susceptible to reporting bias, such as under-reporting, and the validity of questionnaires may vary across cohorts, thereby potentially attenuating associations. Strengths of the study include the large sample size attained by our meta-analytic approach necessary to detect gene–environment interactions. Our collaborative approach also enabled us to standardise our analyses across cohorts. The observed interaction regression coefficients were small compared with the magnitude of interaction observed in other studies looking at gene–environment interactions between SSBs and cardiometabolic outcomes [56, 59]. Thus, even with the large sample size in this study, it is possible that we were insufficiently powered to detect and replicate a small gene–SSB interaction. If such interactions did exist, but are too small to be detected in this analysis, the clinical relevance of such small interactions might be questioned. Nevertheless, our candidate gene approach was suggestive of interaction at one locus, and the ChREBP-FGF21 pathway remains mechanistically interesting.

Variants within the CHREBP locus associate with hypertriacylglycerolaemia [18, 19]. For this analysis, SNPs within candidate genes in a putative ChREBP-FGF21 signalling axis (ESM Table 4) were selected based on genome-wide or sub-genome-wide association with fasting triacylglycerol levels, and not on the basis of glycaemic traits. This approach was pursued because excess sugar consumption is thought to cause hypertriacylglycerolaemia, and hypertriacylglycerolaemia and insulin resistance are linked epidemiologically and may share common pathogenic mechanisms [26, 62, 63]. Thus, implicit to this strategy is the hypothesis that genetic determinants of fasting hypertriacylglycerolaemia may be linked to insulin sensitivity. One limitation of this approach is that mechanisms mediating sugar-induced hypertriacylglycerolaemia and insulin resistance may be distinct. A second limitation is that our analyses were limited to 18 lead SNPs, and it is possible that SNPs that interact with the environment to associate with a trait are distinct from the variants that associate with a trait unconditioned on the environment, particularly those associating with a trait at genome-wide significance threshold. Thus, it may be necessary to examine all SNPs within a locus of interest as opposed to a lead SNP, although this would further increase the burden of multiple testing. Future studies should undertake a more comprehensive testing for interactions between SSB intake and key genes like KLB on glycaemic outcomes.

In summary, the present observational study from 11 cohorts is the largest investigation of the relationship between SSB intake, genetics and glycaemic traits. We observed that SSB intake was positively associated with higher fasting insulin and glucose. Although a suggestive interaction with a genetic variant in the ChREBP-FGF21 signalling axis was observed in the discovery cohorts, this observation was not confirmed in the replication analysis. In conclusion, our results suggest that SSB consumption may unfavourably impact glucose homeostasis in different populations, regardless of genotypes at loci within the ChREBP-FGF21 signalling axis.