The liking and selective ingestion of palatable foods—including sweets—is biologically controlled, and dysfunction of this regulation may promote unhealthy eating, obesity, and disease. The hepatokine fibroblast growth factor 21 (FGF21) reduces sweet consumption in rodents and primates, whereas knockout of Fgf21 increases sugar consumption in mice. To investigate the relevance of these findings in humans, we genotyped variants in the FGF21 locus in participants from the Danish Inter99 cohort (n = 6,514) and examined their relationship with a detailed range of food and ingestive behaviors. This revealed statistically significant associations between FGF21 rs838133 and increased consumption of candy, as well as nominal associations with increased alcohol intake and daily smoking. Moreover, in a separate clinical study, plasma FGF21 levels increased acutely after oral sucrose ingestion and were elevated in fasted sweet-disliking individuals. These data suggest the liver may secrete hormones that influence eating behavior.

Investigation of the diet composition in Fgf21 transgenic mice and cynomolgus monkeys revealed that FGF21 reduces appetite for sugars and artificial sweeteners without directly changing consumption of other nutrients or tastants, or overall calorie intake (). In these experiments, animals were offered a choice between a nutrient solution and water, with free access to regular chow. The ratio of nutrient to water intake between FGF21-treated and control mice was then compared to assess preference. Since FGF21 secretion by the liver is increased by simple sugars and since Fgf21 knockout mice consume more sugar than wild-type littermates, we proposed that FGF21 mediates a hormonal liver-to-brain feedback loop whereby sugar consumption negatively autoregulates sugar appetite (). Given major species differences in FGF21 physiology, however, the significance of these findings in humans remains speculative. To address this gap in knowledge, we investigated whether FGF21 variants are associated with consumption of sucrose-rich, sweet-tasting food (broadly referred to as “sweets”) and quantitative diet composition in general in humans. In parallel, we performed a clinical study to quantify FGF21 secretion after an oral sucrose load and further tested whether the basal level or sucrose-evoked secretion of FGF21 differs between matched individuals who reported liking or disliking sweets.

In rodents, plasma FGF21 increases dramatically after a 24 hr fast, ketogenic diet feeding, and dietary protein restriction (). In humans, however, ketogenic diet feeding and short-term fasting do not induce FGF21 (), and 7–10 days without food are required for circulating FGF21 levels to rise (). In contrast, human plasma FGF21 levels are increased by several days of overfeeding irrespective of dietary protein restriction (), acutely by oral boluses of glucose and fructose (), and by 24 hr of hyperglycemia maintained via intravenous glucose infusion (). Notably, the induction of FGF21 by overfeeding is associated with excess carbohydrate rather than excess fat intake (). Because FGF21 gene variants are associated with an increased percentage of carbohydrate in the diet, together these data suggest that FGF21 could regulate nutrient-specific appetite.

FGF21 is a liver-derived hormone that exerts a range of metabolic effects in rodents and non-human primates in both physiological and pharmacological contexts (). Inter alia, it normalizes blood glucose in diabetic animals (), enhances fatty acid oxidation (), alleviates β cell dysfunction (), and reduces body weight in diet-induced obese mice (). In rodents, it also signals in the brain to regulate food intake, energy expenditure, and fertility (). However, independent clinical trials in humans (n = 38 and 50, respectively) have shown that high doses of FGF21 analogs do not lower blood glucose in obese type 2 diabetic patients, despite improving markers of insulin sensitivity, suggesting that some of its biological effects in model organisms do not extend to humans ().

An important mediator of feeding is the central reward system, which promotes adaptive actions such as consuming palatable nutrients by associating them with pleasure (). Gastrointestinal hormones and signaling metabolites, secreted in response to nutrients in the gut, restrain further food intake in part through the reward system altering motivation to seek and consume food by influencing its reward value. Thus, efficient coupling between nutrient intake and activity of the reward system is needed to prevent overeating. For example, breakdown of this coupling due to decreased intestinal production of the fat-specific satiety factor oleoylethanolamide (OEA) contributes to the genesis of high-fat-diet-induced obesity in mice (). Specific negative feedback signals may also exist for sucrose and other sugars, whose consumption is promoted by both taste and reinforcing post-ingestive factors, and these signals may act directly on the reward system or indirectly by regulating production of factors like OEA that signal through afferent nerves. However, the endocrine factors that mediate sucrose satiety and reward remain unidentified (). Moreover, whether pivotal organs for sugar metabolism, such as the liver, are involved in production of these factors is unclear.

In humans, the existence of innate, nutrient-specific appetites has not been clearly established (). However, food preferences are partly heritable (), and obesity-promoting FTO alleles have been shown to associate with elevated dietary protein intake in adults () and children (), raising the possibility that qualitative shifts in diet could have metabolic consequences. In addition, a fat-preferring, sucrose-disliking phenotype was recently reported in experiments involving patients with obesity-causing mutations in the melanocortin-4 receptor, providing clinical evidence that nutrient-specific appetite is under genetic control in humans (). Finally, independent genome-wide association studies (GWASs) have correlated variants at the FGF21 locus (rs838133 and rs838145) with increased relative carbohydrate but decreased protein and fat intake ().

Health is influenced by diet composition as well as total energy consumption (). Despite this, and despite evidence in favor of independent appetites for different nutrients in model organisms, the circuits that control hunger and food-seeking in general are better understood than the interoceptive mechanisms that lead to consumption of specific nutrients, though the latter may impact both total energy intake and the health quality of food choices ().

Results and Discussion

Toft et al., 2008 Toft U.

Kristoffersen L.

Ladelund S.

Bysted A.

Jakobsen J.

Lau C.

Jørgensen T.

Borch-Johnsen K.

Ovesen L. Relative validity of a food frequency questionnaire used in the Inter99 study. 2 = 0.59, D’ = 0.78) and hence produced comparable results. We analyzed the association of rs838133 with consumption of specific sweet food types. To do this, we created frequency scores summarizing weekly intake of sweet snacks between meals, grouping them into categories “candy” (sweet category, e.g., mixed candy, wine gums) and “cake” (fatty-sweet category, e.g., pastries, cake) and adding the two together to calculate the overall sweet-containing intake. The “cake” summary category we used contained all items from the original cake portion of the snack section of the validated FFQ, while the “candy” summary category also included all original items with the exception of licorice, due to its strong flavor and frequent combination with salt and ammonia in Denmark. We found that the rs838133 A-allele increased the odds ratio (OR) of being in the highest tertile of total intake of all types of sweet-tasting foods, with an OR of 1.18 per A-allele (95% CI 1.06–1.32, p = 0.003, Benjamini-Hochberg [BH] Q < 0.05) ( Table 1 Effect of FGF21 rs838133 on Snack Intake and Reward-Related Phenotypes Categories, n Tertile or Group n OR 95% CI p Sweet Snacking Total sweet snacking (3,950) second 1,322 1.08 0.97–1.20 0.16 third 1,284 1.18 1.06–1.32 0.003∗∗ Candy (4,360) second 1,247 1.06 0.96–1.19 0.21 third 1,435 1.19 1.07–1.32 0.0007∗∗ Cake (4,246) second 1,206 1.00 0.90–1.11 0.97 third 1,373 1.05 0.95–1.16 0.35 Salty Snacking Total salty snacking (4,410) second 1,425 0.95 0.86–1.05 0.33 third 1,204 1.05 0.94–1.17 0.41 Stimulants: Reward/Addiction Alcoholic drinks (5,445) second 2,119 1.08 0.99–1.19 0.09 third 1,910 1.11 1.01–1.22 0.03∗ Coffee consumption (6,080) second 2,202 0.98 0.91–1.07 0.71 third 1,490 1.03 0.94–1.13 0.54 Smoking (4,637) occasionally 224 0.83 0.68–1.01 0.07 every day 2,233 1.11 1.02–1.20 0.02∗ Data represent the odds ratios (ORs) and 95% confidence interval (CI) of being in the second and third tertile of intake or consumption of the different food and reward categories. For all categories, the first tertile was set as the reference. For smoking, the OR of being occasional and every day smoker was calculated with never smoker as reference. p values (p) were calculated using multinomial logistic regression, adjusted for age and sex; asterisks denote Benjamin-Hochberg Q values; ∗∗Q < 0.05, ∗Q < 0.15. Table 2 Association between FGF21 rs838133 and Quantitative Dietary Outcomes Quantitative Diet Outcomes n subjects WT (GG) HE (GA) HO (AA) β (SE) p Total daily energy intake (KJ/day) 6,134 10,042 (3,738) 9,953 (3,694) 10,177 (3,894) 37.3 (65.0) 0.56 Total carbohydrate (% daily energy intake) 6,134 48.8 (7.9) 49.0 (8.1) 49.3 (8.2) 0.26 (0.14) 0.06 Simple carbohydrate (% daily energy intake) 6,134 16.1 (6.6) 16.3 (6.8) 16.4 (6.8) 0.18 (0.11) 0.13 Complex carbohydrate (% daily energy intake) 6,134 15.9 (4.4) 15.8 (4.5) 15.9 (4.5) 0.00 (0.08) 0.98 Added sugar (g/day) 6,134 27.9 (15.6;47.1) 27.6 (15.8;47.9) 31.0 (17.5;51.4) 1.35 (0.71) 0.06 Alcohol intake (% daily energy intake) 6,134 4.6 (5.2) 4.7 (5.4) 4.8 (5.2) 0.10 (0.09) 0.30 Total protein (% daily energy intake) 6,134 13.9 (2.6) 13.8 (2.6) 13.6 (2.6) −0.17 (0.05) 0.001 Total fat (% daily energy intake) 6,134 32.6 (6.9) 32.4 (7.3) 32.3 (7.4) −0.20 (0.13) 0.12 MUFA (% daily energy intake) 6,134 10.8 (2.7) 10.7 (2.9) 10.6 (2.8) −0.9 (0.05) 0.08 PUFA (% daily energy intake) 6,134 5.0 (1.5) 5.0 (1.5) 4.8 (1.5) −0.06 (0.03) 0.02 SFA (% daily energy intake) 6,134 12.6 (3.4) 12.4 (3.6) 12.6 (3.7) −0.04 (0.06) 0.56 Omega-3 fatty acids (% daily energy intake) 6,134 0.9 (0.3) 0.9 (0.3) 0.9 (0.3) −0.009 (0.006) 0.08 Food Item Frequencies n WT (GG) HE (GA) HO (AA) – p trend Total sweet snacking (servings/week) 3,950 4.9 (2.6;8.1) 5.0 (2.8;8.5) 5.8 (3.1;9.1) – 0.02 Candy intake (servings/week) 4,360 2.5 (1.3;4.5) 2.6 (1.3;4.8) 3.0 (1.5;5.2) – 0.04 Cake intake (servings/week) 4,246 1.8 (0.9;3.6) 1.8 (0.9;3.6) 1.9 (0.9;3.9) – 0.07 Salty-snacking (servings/week) 4,410 0.9 (0.5;1.8) 0.9 (0.5;1.8) 1.1 (0.3;1.9) – 0.39 Fructose intake (servings/week) 6,134 8.0 (4.5;16.3) 8.3 (4.3;17.0) 8.3 (4.4;16.1) – 0.44 Complex carbohydrates (servings/week) 3,014 39.7 (30.2;49.8) 38.7 (28.8;48.6) 40.4 (30.2;50.5) – 0.94 Quantitative diet outcomes are given as mean ± SD, except for added sugar and food item frequencies, which are given as median ± interquartile range. p values and per allele effect sizes (β) were calculated using general linear models assuming an additive genetic model (p trend ), adjusted for age and gender. MUFA, mono-unsaturated fatty acid; PUFA, poly-unsaturated fatty acid; SFA, saturated fatty acid. To investigate the relationship between FGF21 variants and human sweet appetite, we genotyped FGF21 rs838133 and rs838145 in the population-based Inter99 cohort, where detailed dietary information is available from a validated 198-item food frequency questionnaire (FFQ) (). The two variants were in high linkage disequilibrium (r= 0.59, D’ = 0.78) and hence produced comparable results. We analyzed the association of rs838133 with consumption of specific sweet food types. To do this, we created frequency scores summarizing weekly intake of sweet snacks between meals, grouping them into categories “candy” (sweet category, e.g., mixed candy, wine gums) and “cake” (fatty-sweet category, e.g., pastries, cake) and adding the two together to calculate the overall sweet-containing intake. The “cake” summary category we used contained all items from the original cake portion of the snack section of the validated FFQ, while the “candy” summary category also included all original items with the exception of licorice, due to its strong flavor and frequent combination with salt and ammonia in Denmark. We found that the rs838133 A-allele increased the odds ratio (OR) of being in the highest tertile of total intake of all types of sweet-tasting foods, with an OR of 1.18 per A-allele (95% CI 1.06–1.32, p = 0.003, Benjamini-Hochberg [BH] Q < 0.05) ( Table 1 ). When sweet intake was divided into “candy” and “cake,” we observed that individuals carrying the A-allele had higher candy intake (OR 1.19 [95% CI 1.07–1.32], p = 0.0007, BH Q < 0.05), whereas intake of cake was similar between genotype groups (p = 0.35). We also constructed a salty snack score, but found no association with rs838133 ( Tables 1 and 2 ).

de Araujo, 2016 de Araujo I.E. Circuit organization of sugar reinforcement. Talukdar et al., 2016a Talukdar S.

Owen B.M.

Song P.

Hernandez G.

Zhang Y.

Zhou Y.

Scott W.T.

Paratala B.

Turner T.

Smith A.

et al. FGF21 regulates sweet and alcohol preference. Because sweet tastant intake is regulated by the central reward system () and because Fgf21 transgenic mice consume less ethanol than wild-type (), a behavior that also involves the reward system, we evaluated the effect of FGF21 variants on reward-related phenotypes for which data were available from the Inter99 cohort. These analyses revealed that each rs838133 A-allele was nominally associated with higher prevalence of smoking with an OR of 1.11 for daily smoking compared to non-smoking (95% CI 1.02–1.20, p = 0.02, BH Q < 0.15), as well as increased alcohol intake (OR 1.11 [95% CI 1.02–1.22], p = 0.03, BH Q < 0.15) ( Table 1 ). Adjustment of smoking for alcohol intake, as well as the reverse, did not change the results ( Table S1 ). No association between FGF21 variants and coffee consumption was observed in Inter99 (p = 0.53), and the significant smoking association could not be replicated in GWAS data from the Tobacco and Genetics (TAG) Consortium ( Table S2 ).

Locke et al., 2015 Locke A.E.

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Genetic studies of body mass index yield new insights for obesity biology. Manning et al., 2012 Manning A.K.

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et al. Wellcome Trust Case Control Consortium Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) Investigators Genetic Investigation of ANthropometric Traits (GIANT) Consortium Asian Genetic Epidemiology Network–Type 2 Diabetes (AGEN-T2D) Consortium South Asian Type 2 Diabetes (SAT2D) Consortium DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium

Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Because sugars are especially palatable and palatable nutrients have been implicated in the pathogenesis of obesity and metabolic disease, we also examined the association of rs838133 with anthropomorphic and metabolic variables. Surprisingly, despite the association of the rs838133 A-allele with increased intake of sweets, this allele did not correlate with increased total energy intake ( Table 2 ) and was associated with lower BMI and waist circumference in the Inter99 cohort ( Table S3 ), which was supported by GWAS results from the Genetic Investigation of Anthropometric Traits (GIANT) Consortium ( Table S2 ) (). The rs838133 A-allele carriers also tended to have better glycemic control compared with non-carriers, with lower plasma glucose or serum insulin at fasting or 2 hr after an oral glucose tolerance test ( Table S3 ). However, the effect on glycemia was not observed in GWAS data from Meta-analyses of Glucose and Insulin-Related Traits Consortium (MAGIC) (). Similarly, no association with type 2 diabetes in Diabetes Genetics Replication and Meta-analysis (DIAGRAM) Consortium GWAS data () was found ( Table S2 ). The results for FGF21 rs838145 were highly consistent with those for rs838133 ( Table S4 ).

Deglaire et al., 2012 Deglaire A.

Mejean C.

Castetbon K.

Kesse-Guyot E.

Urbano C.

Hercberg S.

Schlich P. Development of a questionnaire to assay recalled liking for salt, sweet and fat. Table 3 Clinical Measures among Sweet-Likers and Sweet-Dislikers Sweet-Likers Sweet-Dislikers p n (women/men) 25 (19/6) 26 (19/7) Age (years) a a Data are given as mean ± SD 23.7 (2.6) 24.4 (3.5) 0.39 Body mass index (BMI) (kg/m2) 21.9 (1.7) 21.8 (1.6) 0.70 Waist circumference (cm) a a Data are given as mean ± SD 76.3 (6.6) 75.6 (3.9) 0.65 Fasting plasma glucose (mmol/L) a a Data are given as mean ± SD 4.7 (0.4) 4.6 (0.4) 0.16 Fasting plasma insulin (pmol/L) a a Data are given as mean ± SD 53.8 (26.8) 46.8 (18.8) 0.28 HbA1c (CDDT%) a a Data are given as mean ± SD 5.1 (0.28) 5.1 (0.7) 0.66 Fasting plasma FGF21 (pg/ml) 63.2 (51.5;108.8) 95.7 (65.5;422.2) 0.04 Men b b Differences between groups are evaluated using a Student’s t test 61.5 (40.0;86.2) 74.5 (63.8;213.1) 0.10 Women b b Differences between groups are evaluated using a Student’s t test 64.4 (51.5;286.5) 108.2 (65.5;623.8) 0.13 Data are given as median ± interquartile range, with differences between groups assessed using a Wilcoxon rank test (p). In a separate clinical study, we investigated the association between fasting plasma FGF21 levels and self-reported sweet liking in young, healthy, and lean subjects. A total of 86 subjects (23 men and 63 women) completed a questionnaire to determine sweet, fatty-sweet, and salt preferences (). Based on sweet-liking scores, we selected the 51 subjects from the highest (n = 25, 19 women and 6 men) and lowest (n = 26, 19 women and 7 men) tertiles of sweet preference and classified them as “sweet-likers” and “sweet-dislikers,” respectively. Although more women than men participated in this study overall, there was no difference in the sex distribution between the sweet-liker and sweet-disliker groups. Prior to blood sampling for FGF21 analysis, we also asked each subject to select images of liked and disliked snacks. This test supported the validity of the groups, as sweet-dislikers disliked sweet snacks more than sweet-likers (p = 0.02), and sweet-likers liked sweet snacks more than sweet-dislikers (p = 0.04) ( Table S5 ). Further, to determine whether differences in taste perception might contribute to variation in sweet preference in these subjects, they were asked to rate the pleasantness and intensity of five unmarked solutions of varying sucrose concentration. We observed no difference between sweet-likers and sweet-dislikers in ratings of pleasantness or intensity of any of the five solutions ( Figure S1 A). In addition, no differences in clinical characteristics or glycemic measures were observed between the two groups ( Tables 3 and S5 ).

We measured FGF21 levels after a 12 hr fast and found that concentrations were 51% higher in sweet-dislikers compared to sweet-likers (median 95.7 pg/mL [interquartile range, 65.5–422.2] versus median 63.2 pg/mL [interquartile range, 51.5–108.8], p = 0.04) ( Table 3 ).

Gälman et al., 2008 Gälman C.

Lundåsen T.

Kharitonenkov A.

Bina H.A.

Eriksson M.

Hafström I.

Dahlin M.

Amark P.

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Rudling M. The circulating metabolic regulator FGF21 is induced by prolonged fasting and PPARalpha activation in man. Figure 1 Changes and Concentrations of Plasma FGF21 during an Oral Sucrose Challenge Test Show full caption (A) The percent changes in plasma FGF21 levels in each subject from baseline at different time points during an oral sucrose challenge. Data presented as mean ± SEM. (B) The absolute concentrations of plasma FGF21 at different time points during an oral sucrose challenge. Data presented as median ± interquartile range. The circles and blue line represent sweet-likers (n = 20), and the squares and green line represent sweet-dislikers (n = 21). On a subsequent clinical visit in 41 of the 51 subjects (20 sweet-likers and 21 sweet-dislikers recalled randomly from each group on the basis of a power calculation), we performed a 5 hr, 75 g oral sucrose challenge following a 12 hr fast to assess the dynamic FGF21 response to sucrose consumption and to investigate potential differences in FGF21 secretion between sweet-likers and sweet-dislikers. In the group that completed the challenge study, there was no difference in absolute plasma FGF21 levels between sweet-likers and sweet-dislikers at any time point, and incremental areas under the curve were indistinguishable between groups (p = 0.26). Most importantly, however, plasma FGF21 was markedly increased by sucrose (mean 193% above baseline, p < 0.0001) in both groups, reaching maximum levels after 120 min ( Figures 1 A and 1B , respectively). Interestingly, this increase was delayed relative to the increase in plasma insulin, plasma C-peptide, and plasma glucose, which peaked 30–60 min after the oral sucrose challenge ( Figures S2 A–S2F; Table S6 ). The large variation in inter-subject FGF21 levels is also notable and consistent with prior reports ().

Mozaffarian, 2016 Mozaffarian D. Dietary and policy priorities for cardiovascular disease, diabetes, and obesity: a comprehensive review. Yarmolinsky et al., 2009 Yarmolinsky D.A.

Zuker C.S.

Ryba N.J. Common sense about taste: from mammals to insects. Zuker, 2015 Zuker C.S. Food for the brain. The mechanisms that influence what foods humans want to consume may contribute to the global burden of morbidity and premature death because suboptimal diet is a primary risk factor for many common diseases (). The ubiquity of palatable nutrients, especially sugars and fat, contributes to poor dietary choices by exploiting the tendency, which likely evolved in response to frequent periods of scarcity, to voraciously consume such energy-dense foods when available (). Thus, a better understanding of the biological basis of palatable nutrient appetite is needed to develop strategies to improve diet quality and human health in modern food environments ().

Chu et al., 2013 Chu A.Y.

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et al. CHARGE Nutrition Working Group DietGen Consortium

Novel locus including FGF21 is associated with dietary macronutrient intake. Tanaka et al., 2013 Tanaka T.

Ngwa J.S.

van Rooij F.J.

Zillikens M.C.

Wojczynski M.K.

Frazier-Wood A.C.

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Luan J.

et al. Genome-wide meta-analysis of observational studies shows common genetic variants associated with macronutrient intake. Heianza et al., 2016 Heianza Y.

Ma W.

Huang T.

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Qi L. Macronutrient intake-associated FGF21 genotype modifies effects of weight-loss diets on 2-year changes of central adiposity and body composition: the POUNDS lost trial. von Holstein-Rathlou et al., 2016 von Holstein-Rathlou S.

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et al. FGF21 mediates endocrine control of simple sugar intake and sweet taste preference by the liver. Our data suggest that the liver hormone FGF21 may regulate sweet consumption in humans, offering insight into the fundamental biology of nutrient appetite as well as a potential avenue for developing therapeutics to decrease intake. Consistent with this hypothesis, two GWASs have associated SNPs in FGF21 with relatively increased intake of carbohydrate but decreased intake of protein and fat in humans (), and a recent paper reported that FGF21 variants interact with diet to modify changes in fat mass and waist circumference evoked by weight loss diets containing either high or low amounts of carbohydrate (). However, the former reports do not address how FGF21 variation modifies diet pattern beyond basic relative macronutrient consumption. This study, by contrast, explores how these FGF21 variants correlate with intake of many food items from diverse categories, to produce a detailed portrait of how FGF21 variation affects human ingestive behavior. We show that the rs838133 A-allele and highly correlated rs838145 G-allele associate specifically with increased total intake of sugars rather than complex carbohydrates, as well as the propensity to consume sweet snacks rather than fatty-sweet or salty snacks. Importantly, this change in diet structure does not affect total energy intake, as both protein and fat intake decrease. This is consistent with previous observations in rodents of a specific effect of FGF21 on sweet consumption, which leads to compensatory changes in protein and fat intake to maintain stable energy intake ().

Talukdar et al., 2016a Talukdar S.

Owen B.M.

Song P.

Hernandez G.

Zhang Y.

Zhou Y.

Scott W.T.

Paratala B.

Turner T.

Smith A.

et al. FGF21 regulates sweet and alcohol preference. Schumann et al., 2016 Schumann G.

Liu C.

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et al. KLB is associated with alcohol drinking, and its gene product β-Klotho is necessary for FGF21 regulation of alcohol preference. It is notable that the strongest association observed in our study was with sweet snacking, candy in particular. Snacking often occurs in the absence of energy deficit, motivated by a desire for pleasure. Thus, FGF21 may regulate hedonic sugar craving, as opposed to homeostatic sugar appetite. This possibility is congruent with the potential association between the rs838133 A-allele and other forms of reward-seeking behavior. The connection to alcohol is of particular interest because Fgf21 transgenic mice are alcohol averse (), and a recent GWAS involving more than 105,000 individuals identified variation in the obligate FGF21 co-receptor, β-klotho, as associated with alcohol drinking in humans (). Thus, alcohol, like glucose and fructose, may increase FGF21 production to limit further alcohol intake, although more studies are needed to verify this hypothesis.

Tanaka et al., 2013 Tanaka T.

Ngwa J.S.

van Rooij F.J.

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Wojczynski M.K.

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Lemaitre R.N.

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Malik R.

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et al. METASTROKE and the International Stroke Genetics Consortium

Effect of genetic variants associated with plasma homocysteine levels on stroke risk. Our finding that the rs838133 A-allele, which correlates with increased intake of sweets, is associated with decreased BMI and waist circumference in the Inter99 cohort and GIANT consortium, suggests that small increases in sugar consumption do not necessarily promote adiposity or glucose intolerance in humans. Instead, if a shift in diet composition underlies these effects, it suggests that reducing intake of protein or certain fats may have beneficial effects on whole-body energy balance. Consistent with this, the FTO rs1421085 C-allele is associated both with increased BMI and increased protein intake (). Interestingly, the FGF21 rs838133 A-allele is also associated with increased plasma homocysteine levels, but not coronary artery disease, in a sample of 31,400 cases and 92,927 controls (). However, the rs838133 A-allele is nominally associated with an increased risk of ischemic stroke (OR = 1.04), small vessel disease (OR = 1.08), and large vessel disease (OR = 1.09), but not cardioembolic stroke, in 12,389 cases and 62,004 controls (). Thus, our data suggest that while increased sugar consumption is not associated with obesity, it may correlate with adverse vascular outcomes. Based on these results, we are hopeful that improved methods of capturing human dietary behavior can be combined with GWASs to infer how long-term eating patterns influence body weight and other cardiovascular outcomes, a task that has been difficult and controversial to date.

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Lemaitre R.N.

Luan J.

et al. Genome-wide meta-analysis of observational studies shows common genetic variants associated with macronutrient intake. Our clinical trial also suggests that human FGF21 may be a negative regulator of sweet consumption because it increased markedly after an oral sucrose load and because sweet-disliking individuals have elevated fasting FGF21 levels. Interestingly, taste ratings of sucrose intensity and pleasantness did not differ between groups. This observation is in agreement with a previous report demonstrating that FGF21 increases with a hormone-like profile in humans after oral fructose and glucose ingestion, as well as data from rodents and non-human primates showing that FGF21 treatment reduces sweet consumption independent of taste (). However, the rs838133 A-allele, which is associated with increased consumption of sweets, is highly correlated with the rs838145 G-allele, which was associated with higher fasting plasma levels of FGF21 in 377 samples from the Baltimore Longitudinal Study of Aging (). The explanation for this is likely that the kit used by Tanaka et al. (R&D Systems) and the kit used in our clinical study to measure total FGF21 (Biovendor) exhibit poor intra-subject correlations in the fasted state for reasons that are unknown, but may involve use of a monoclonal capture antibody in the R&D Systems kit that cannot detect cleaved forms of FGF21, or some interference in the Biovendor kit that leads some subjects to exhibit extremely high FGF21 levels (see STAR Methods for technical discussion of this issue). However, both kits give similar conclusions about the magnitude of FGF21 increase after sucrose ingestion, which are also congruent with results from ELISA reagents that only measure full-length FGF21, suggesting both detect relative changes in FGF21 similarly (data not shown). Studies that are more detailed will be required to understand this measurement discrepancy in fasting samples.

The major strength of this study is that it combines human genetics and clinical investigation to test GWAS-inspired results from rodents and primates again in humans. This species back and forth allowed us to evaluate a narrow hypothesis of high prior probability about a potential function of FGF21 in humans and conclude that a clinical trial examining whether FGF21 influences sweet intake, alcohol drinking, and smoking is warranted. The major limitations of this study are its reliance on self-reporting and that it does not experimentally demonstrate that FGF21 regulates sweet appetite and reward seeking, an outcome that awaits future interventional trials.