Non-Caloric Sweetener Effects on Brain Appetite Regulation in Individuals Across Varying Body Weights

Non-Caloric Sweetener Effects on Brain Appetite Regulation in Individuals Across Varying Body Weights | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Non-Caloric Sweetener Effects on Brain Appetite Regulation in Individuals Across Varying Body Weights Kathleen Page, Sandhya Chakravartti, Kay Jann, Ralf Veit, Hanyang Liu, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4684043/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Mar, 2025 Read the published version in Nature Metabolism → Version 1 posted You are reading this latest preprint version Abstract Introduction: Sucralose is a non-caloric sweetener commonly consumed to provide sweet taste without calories. Yet, some studies suggest that non-caloric sweeteners stimulate appetite, possibly due to the delivery of a sweet taste without the post-ingestive metabolic signals that normally communicate with the hypothalamus to suppress hunger. We tested the hypothesis that acute consumption of the non-caloric sweetener, sucralose, would stimulate greater increases in hypothalamic blood flow, an MRI correlate of hunger, compared to caloric sugar (sucrose) and water, and would alter functional connectivity between the hypothalamus and other brain regions. Additionally, we expected sucrose, but not sucralose, to raise blood glucose levels, inversely affecting hypothalamic blood flow. We anticipated variations in hypothalamic responses based on weight status. Methods: Seventy-five young adults with healthy-weight, overweight, or obesity from a random-order crossover design study (NCT02945475) with acute consumption of a drink containing either 75g sucrose, sucralose (individually sweetness matched to sucrose), or plain water were included in this analysis to compare the effects of sucralose relative to sucrose and water on changes in hypothalamic blood flow, circulating glucose levels, and ratings of hunger. Hypothalamic blood flow (measured by pulsed arterial spin labeling perfusion MRI), hunger ratings, and glycemic responses were concurrently measured fasting, +10, and +35min after drink ingestion. As a secondary outcome, functional connectivity was performed using blood oxygen level-dependent (BOLD) fMRI to explore changes between the hypothalamus seed region and other brain areas after ingestion of sucralose relative to sucrose and water.Linear mixed-effects models were used for comparisons of drink contrasts. Linear regressions were used to examine associations between peripheral glucose levels, hypothalamic blood flow, and hunger ratings. Models were adjusted for age, sex, BMI, and race/ethnicity. Post hoc comparisons were adjusted for multiple comparisons using a Bonferroni correction. Results: There was a significant effect of drink on hypothalamic blood flow, adjusting for age, sex, BMI, and race/ethnicity (F = 5.05; p<.007). Compared to sucrose, intake of drinks sweetened with sucralose produced greater hypothalamic blood flow (Mean diff = .079, ± 0.03, p < .018) and greater hunger responses (Mean diff = .575 ± 0.16, p < .001). Sucralose vs water also increased hypothalamic blood flow (Mean diff = .078 ± 0.03, p < .019), but produced no difference in hunger ratings. Sucrose, but not sucralose, produced increases in peripheral glucose levels, which were associated with reductions in medial hypothalamic blood flow (beta =-.005 ± .002, p < .007). Sucralose, compared to sucrose and water, resulted in increased functional connections between the hypothalamus and brain regions involved in motivation and somatosensory processing. Conclusion: These results underscore the notable differences in sucralose, a non-caloric sweetener, on hypothalamic signaling pathways linked to appetite regulation when compared to sugar or water. The findings suggest that non-caloric sweeteners could affect key mechanisms in the hypothalamus responsible for appetite regulation. Health sciences/Medical research/Clinical trial design/Randomized controlled trials Biological sciences/Neuroscience/Feeding behaviour/Hypothalamus Health sciences/Endocrinology/Endocrine system and metabolic diseases/Obesity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Obesity rates have risen dramatically over the last three decades, posing a significant public health challenge 1 . A growing body of evidence links the rise in sugar-sweetened beverage consumption to weight gain and obesity 2–4 . To address this, non-caloric sweeteners are increasingly consumed as a calorie-free alternative to satisfy the craving for sweetness 2 . Despite their widespread use, the health implications of non-caloric sweeteners remain uncertain and subject to debate 3 . While epidemiological studies link non-caloric sweetener consumption to weight gain 4 , obesity 5 , and type 2 diabetes 6 , randomized controlled trials report that non-caloric sweeteners have neutral or beneficial effects on body weight and glucose metabolism 7 . Studies conducted in rodents suggest that non-caloric sweeteners stimulate hunger by interfering with the conventional neural responses to sweet taste and nutrient signaling that occur with caloric sugar 5 . Human studies using functional magnetic resonance imaging (fMRI) also indicate that the brain may respond differently to beverages containing non-caloric sweeteners compared to caloric sugar 6–8 . However, previous fMRI studies have often been constrained by small sample sizes, comprising healthy-weight individuals 6–11 . Furthermore, prior studies have shown a lack of diversity in sex and race/ethnicity, primarily focusing on male and white participants, which limits their external validity 10–12,15,16,18 . In this study, we included a demographically diverse cohort to investigate how sucralose, a prevalent non-caloric sweetener 12 , influences hypothalamic activity and glucose signaling across a range of body weights. Our investigation extends the findings of Yunker et al. (2021), linking obesity with an increased brain response to food cues after sucralose consumption, contrasting with the response to sucrose. Sucralose, chemically altered from sucrose by replacing hydroxyl groups with chlorine, offers sweetness without caloric absorption 13 . Drinks with sucralose were calibrated to match the sweetness of sucrose-containing drinks, allowing us to assess the differential effects of a sweet taste without nutrients (sucralose) compared to a sweet taste with nutrients (sucrose) on hypothalamic activity, glucose concentrations, and hunger responses. We also compared sucralose with water to examine the specific effects of sweetness on hypothalamic activity and the corresponding physiological responses. The hypothalamus plays a crucial role in appetite and homeostatic metabolic regulation 14,15 , and functional connectivity between the hypothalamus and other brain areas coordinates homeostatic energy balance 14,16,17 . Prior work shows that ingestion of the simple sugar, glucose, exerts suppressive effects on hypothalamic activation (evidenced in MRI studies as reduced blood oxygen level dependent (BOLD) signal or reduced cerebral blood flow (CBF)) 15,18–24 . The glucose-linked reductions in hypothalamic activity are associated with reductions in hunger 22,23 , while an increase in hypothalamic activity is associated with heightened hunger 25,26 . Based on prior findings, we hypothesized that sucralose, compared to sucrose and water, would cause greater increases in hypothalamic blood flow and would alter functional connectivity between the hypothalamus and other brain regions. Additionally, we expected sucrose, but not sucralose, to raise blood glucose levels, inversely affecting hypothalamic blood flow. We expected to observe differences between the lateral and medial subfields of the hypothalamus, given their distinct functional roles. Finally, we projected that these hypothalamic responses would differ according to participants' weight status. Methods Study Overview Data are from the Brain Response to Sugar study, an investigation of neuroendocrine responses to high-reward foods (NCT02945475) 27 . Data presented are the primary results of the arterial spin labeling perfusion MRI analyses (pASL) from the sucralose, sucrose, and water conditions from the randomized crossover trial (Fig. 1 ). Glucose was included in the larger trial for the purposes of testing differences in equicaloric sugars on outcomes 27 . Participants provided written informed consent compliant with the University of Southern California Institutional Review Board (IRB #H-09-00395). This study followed Consolidated Standards of Reporting Trials (CONSORT) guidelines (trial protocol can be found in online digital repository 28 ). The study included an initial screening visit and MRI visits performed at the Dornsife Cognitive Neuroimaging Center of the University of Southern California at approximately 8:00 AM after a 12-hour overnight fast. During the screening visit, height was measured to the nearest 0.1 cm using a stadiometer and weight to the nearest 0.1 kg using a calibrated digital scale. Body mass index (BMI) was calculated as weight in kg divided by height in meters squared. Enrollment occurred between July 2016 and March 2020. The three MRI visits were performed in blinded, random order (using function randperm, a computer-generated randomization procedure in Matlab) on separate days between 2 and 2 months apart with ingestion of 300 mL drinks containing either sucrose (75 g), sucralose (individually sweetness matched to sucrose, as previously described 27 ) or a water control to test their effects on changes in hypothalamic blood flow, circulating glucose levels, and ratings of hunger ( Fig. 2 ). Hypothalamic blood flow (measured by pulsed arterial spin labeling perfusion MRI), hunger ratings, and glycemic responses were measured fasting, + 10 and + 35min after drink ingestion. Hunger ratings and glycemic responses were additionally measured 120 min after drink ingestion. Drinks were flavored with 0.25 tsp (1.07g) of non-sweetened cherry flavoring (Kraft Foods Kool-Aid® Unsweetened Cherry Drink Mix) to improve palatability. Females underwent MRI visits during the follicular phase of the menstrual cycle to reduce variability in hunger 29,30 . Participants included in this analysis were 75 adults (43 female) ages 18 to 35 years with healthy weight, overweight, or obesity (Table 1 ). They were right-handed, nonsmokers, weight-stable for at least 3 months before the study visits, nondieters, not taking medication (except oral contraceptives), and with no history of diabetes, eating disorders, illicit drug use, or other medical diagnoses. The prespecified primary outcome was relative changes in hypothalamus blood flow in response to acute sucralose vs. sucrose and water among the whole cohort and stratified by weight status (healthy-weight, overweight, obese). Secondary outcomes included (1) associations between changes in circulating glucose levels, hypothalamic blood flow, and ratings of hunger in response to sucralose and sucrose; and (2) functional connectivity analysis to investigate brain regions with MR signal responses that were correlated with the hypothalamic response after sucralose relative to sucrose and water. MRI Acquisition Parameters Participants were scanned at the USC Dana and David Dornsife Neuroimaging Center. Data were collected using a 3T Siemens MAGNETOM Prismafit MRI System, with a 32-channel head coil. A high-resolution 3D magnetization prepared rapid gradient echo (MPRAGE) sequence (TR = 1950ms; TE = 2.26ms; bandwidth = 200Hz/pixel; flip angle = 9°; slice thickness = 1mm; FOV = 224mm×256mm; matrix = 224×256) was used to acquire structural images for multi-subject registration. Pulsed arterial spin labeling (pASL) was used to quantify cerebral blood flow (CBF) changes in response to acute consumption of the different drinks. pASL provides a measure of CBF by magnetically tagging arterial blood directly before it enters the brain and measuring the amount of tagged blood to reach specific brain areas 31,32 . To acquire CBF maps, pulsed arterial spin labelling images were obtained with a PICORE-Q2TIPS (proximal inversion with control for off-resonance effects—quantitative imaging of perfusion by using a single subtraction) sequence by using a frequency offset corrected inversion pulse and echo planar imaging readout for acquisition 33 . The pASL acquisition parameters used in this study were: FOV = 192 mm; matrix = 64x64; bandwidth = 2232 Hz/Pixel; slice thickness = 5 mm; in-plane resolution = 3 × 3 mm 2 ; interslice spacing = 0 mm; TR = 4000 ms, and flip angle = 90; bolus duration (TI1) = .7 seconds, inversion time (TI2) = 1.8 seconds; number of label/control pairs = 60. The first control volume of the pASL sequence was used as calibration image for CBF quantification. The temporal stability of pASL compliments the longer curve of glucose responses that we measured with the accompanying blood draws during the study sessions. Blood oxygen level-dependent (BOLD) fMRI was acquired with a multi-band interleaved gradient echo planar imaging sequence. Eighty-eight 1.5-mm thick slices covering the whole brain were acquired using the following parameters: TR = 1,000ms; TE = 43.20ms; bandwidth = 2,055Hz/pixel; flip angle = 52°; Multi Band factor = 8; FOV = 128mm×112mm, matrix = 128×112. number of volumes = 376 volumes. Arterial Spin Labeling Analysis We used the Bayesian Inference for Arterial Spin Labeling (BASIL) toolbox, part of the Oxford FMRIB Software Library (FSL), to determine mean CBF across the entire brain, as well as regional CBF in the hypothalamus and hypothalamic subfields. Following motion correction of the whole image sequence, CBF quantification was performed based on a single compartment model and voxel-wise calibration. The hypothalamic response to drink was measured as change in regional CBF (see section below) after drink (averaged across post drink time points) divided by whole brain CBF to adjust for changes occurring across the whole brain. This post drink value was then subtracted from pre drink value to correct for baseline CBF. To reduce confounding effects due to limited spatial resolution, we adjusted for partial volume effects. Hypothalamus Regions of Interest We examined the hypothalamic response to different drink conditions by focusing on two distinct hypothalamic subfields, the lateral hypothalamic (LH) and medial hypothalamic (MH). This approach was driven by the well-recognized functional differences between these two areas of the hypothalamus 14 . The lateral and medial hypothalamic subfield ROI masks were anatomically defined according to Baroncini et al. 34 and previously used to examine hypothalamic appetite regulation 35 . We included an additional exploratory subfield based on the high-resolution Neudorfer anatomical atlas of hypothalamic nuclei related to energy regulation, in order to enable direct comparison with future studies that may employ similar methodologies 36 . The Neudorfer hypothalamus ROI includes all hypothalamic nuclei related to energy regulation: lateral hypothalamus, ventromedial nucleus, dorsomedial hypothalamic nucleus, arcuate nucleus, and paraventricular nucleus. A single mask was created and normalized into MNI152 space. The Neudorfer ROI was used in bilateral functional connectivity analyses employing the comprehensive mask encompassing hypothalamic nuclei involved in energy regulation. Functional Connectivity Analysis BOLD-fMRI data were collected during a visual food-cue task, where food and non-food images were presented to participants in random order as previously described 27 . To explore the primary effects of different drinks on hypothalamic activity and its functional connections, we performed an analysis across the whole fMRI time series including both food and non-food stimuli. This approach allowed us to identify differences in hypothalamic functional connections after the consumption of sucralose relative to sucrose and water. A similar methodology was previously applied to investigate how increments in peripheral glucose affect brain connectivity during visual food and non-food tasks 37 . Data were processed using Conn Toolbox v21.a ( https://www.nitrc.org/projects/conn ) 38,39 and Statistical Parametric Mapping (SPM) v12.7 ( https://www.fil.ion.ucl.ac.uk/spm/ ) 40 . Preprocessing included motion alignment, regression of physiological noise fluctuations and bandpass filtering between 0.008 Hz and 0.09 Hz. Noise-corrected fMRI images were then co-registered to anatomical T1 weighted images, normalized into MNI standard space. We then performed seed-to-voxel analyses starting from a seed in the hypothalamus as described above. Results were utilized in second-level group analyses comparing functional connectivity between drink conditions using a full factorial model. This model incorporated drink condition as a between-subjects factor and included 4 covariates correcting for age, sex, BMI, and race/ethnicity. Group-level analyses were performed using a weighted general linear model (GLM) 41 , which evaluated voxel-level hypotheses and accounted for random effects across subjects and sample covariance estimation across multiple measurements. Statistical inferences for clusters were based on Gaussian Random Field theory 41,42 , with significance determined using a voxel-level threshold of p < .001 and an FDR-corrected cluster-size threshold of p < 0.05 43 . See ( Supplemental File 1 ) for more details of methods. Glucose Assay Plasma glucose was measured enzymatically using glucose oxidase (YSI 2300 STAT PLUS Enzymatic Electrode-YSI analyzer, Yellow Springs Instruments). Hunger Ratings Visual analogue scales were used to assess feelings of hunger on a scale from 1 to 10 where 1 was “not at all” and 10 was” very much”. Hunger was assessed at baseline, + 10, +35 and + 120 min after drink consumption. Prior studies have demonstrated good reproducibility and validity of these VAS scores for assessing subjective sensations of hunger 44 . Statistical Analysis Descriptive statistics were used to characterize frequency for categorical variables and mean (SD) and median (interquartile range) for continuous variables and to check distributional properties. Linear mixed-effects models were employed for comparisons between sucralose and sucrose, aiming to assess the impact of sweet taste without calories (sucralose) versus sweet taste with calories (sucrose), and sucralose versus water to investigate the effects of a sweetened drink without calories (sucralose) versus a non-sweet drink without calories (water). A linear contrast with a significance threshold of p < .05 was used to compare changes from before to after ingestion between the sucralose vs sucrose and sucralose vs water conditions. Since there was no significant interaction between drink condition and time on hypothalamic response, drink condition was collapsed across both time points. Glycemic measures and hunger ratings were collapsed across three time points. We also examined the main effect of BMI group on the hypothalamic response across all drinks, tested for interactions between BMI group and drink comparisons, and stratified drink comparisons by weight status group. Linear mixed effects models assumed random intercept for each subject. General linear regression models were used to assess the association of changes in circulating glucose (independent variable) with hypothalamic response (dependent variable) after drink ingestion and the association between hypothalamic responses (independent variable) with hunger ratings (dependent variable) after drink ingestion. Models were adjusted for age, sex, BMI and race/ethnicity. Post hoc comparisons of drink contrasts and time points were adjusted for multiple comparisons using when necessary, using a Bonferroni correction, with significance levels set at .05. Model fits were examined using the r2beta function from the r2glmm package. All statistical analyses were performed used Rstudio (version 2023.06.1). Results Hypothalamic Blood Flow Response: Comparing Sucralose to Sucrose and Water There were no baseline differences in hypothalamic blood flow among the three drink sessions (p = 0.40). There was a significant effect of drink condition on the lateral hypothalamic blood flow response (F (2, 363.88) = 5.05, p < .007, R 2 model = .059). Specifically, the response in the lateral ROI was higher after consuming sucralose compared to sucrose (Mean difference = .079 ± 0.028; p < .018) and sucralose compared to water (Mean difference = .078 ± 0.028; p < .019), as shown in Fig. 3 and Supplemental Table 1 . While there was no interaction between drink and time on the hypothalamic response (p = 0.26), the hypothalamic response to drinks over time is shown in Supplemental Fig. 2A and Supplemental Table 8 for completeness. No significant difference was observed in the medial hypothalamic ROI after sucralose compared to sucrose, although the response was greater after sucralose compared to water ( Supplemental Table 1 ). Additionally, differences in the Neudorfer ROI between sucralose and water were observed ( Supplemental Table 1 ). Although the interaction between weight status and drink contrasts did not reach statistical significance, we stratified the results by weight status to explore our hypothesis that weight status influences hypothalamic responses to sucralose compared to sucrose and water. In the lateral hypothalamic ROI, individuals with obesity had greater hypothalamic responses to sucralose vs water (beta = .105 ± 0.052; p = .042) but not sucralose vs sucrose (beta = .046 ± 0.051; p = .37). In contrast, individuals with healthy-weight had greater hypothalamic responses to sucralose vs sucrose (beta = .106 ± 0.048; p = .027) and no differences between sucralose vs water (beta = .051 ± 0.047; p = .28). There were no differences in individuals with overweight to either sucralose vs sucrose (beta = .081 ± 0.049; p = .102) or sucralose vs water (beta = .081 ± 0.049; p = .103) ( Fig. 4 , Supplemental Table 2 ). The Neudorfer and medial hypothalamic ROIs showed differential responses to sucralose relative to sucrose comparisons only among those with healthy-weight ( Supplemental Table 2 ). We also examined the hypothalamic responses to sucrose compared with water. There were no differences in hypothalamic blood flow when comparing sucrose to water conditions across BMI groups ( Supplemental Table 1 ). However, the data revealed a pattern where individuals with healthy-weight showed a non-significant trend towards decreased lateral hypothalamic blood flow in response to sucrose compared to water (mean difference = -0.06, ± .047 p = .25), in contrast to individuals with obesity, who showed an increase, though this was also not statistically significant (mean difference = 0.059, ± .051, p = .25) ( Supplemental Table 2 ). Hypothalamic Seed-to-Voxel Connectivity Statistical analysis of the connectivity from the bilateral hypothalamus to all other voxels in the brain resulted in different connectivity patterns after the ingestion of sucralose, relative to sucrose and water. After ingestion of sucralose relative to sucrose, we observed increased connectivity between the left hypothalamus and anterior cingulate cortex (Fig. 5 a). After ingestion of sucralose relative to water, we observed increased connectivity between the right hypothalamus and left superior parietal lobule (Fig. 5 b). After ingestion of sucrose relative to water, we observed increased connectivity between the right hypothalamus and precuneus cortex and decreased connection between right hypothalamus and occipital pole (Fig. 5 c). Circulating Glucose Responses Mean results for the effects of sucrose, sucralose, and water on changes in peripheral glucose levels were previously reported 27 . There were no baseline differences in peripheral glucose levels among the three drink sessions (p = 0.596). There was a significant effect of drink on the peripheral glucose response (F (2, 552.65) = 139.36, p = < 0.00001, R 2 model = .34), adjusting for age, sex, race/ethnicity, and BMI. Post hoc analysis showed a marked increase in peripheral glucose following sucrose compared to sucralose intake (p < 0.0003), but no differences were observed in peripheral glucose levels when comparing sucralose to water (p = .99) ( Supplemental Table 3 ). There was an interaction between time and drink on peripheral glucose levels (p < 0.002), and drink comparison by time on peripheral glucose levels shown in Supplemental Fig. 2b and Supplemental Table 8 . There were no differences in peripheral glucose levels by weight status (p < .48). Hunger responses There were no differences in baseline hunger ratings among the drink sessions (p = 0.678), however, there was a significant effect of drink condition on changes in hunger (F (2,580.21) = 10.79, p < .00005, R 2 model = .153). Post hoc analysis revealed a significant increase in hunger after sucralose compared to sucrose (mean diff = .575 ± 0.16; p < .001) but no differences after sucralose vs water (mean diff = − .090 ± 0.16; p = .99) ( Supplemental Table 4, Supplemental Fig. 2 ). While there was no interaction between time and drink on hunger ratings (p = 0.38), drink comparisons by time on hunger are shown in Supplemental Fig. 2c and Supplemental Table 8 for informational purposes. A trending difference in hunger was observed by weight status (p = .053), with greater hunger among individuals with obesity compared to healthy-weight (mean diff = .975 ± 0.40; p = .056). Associations between changes in peripheral glucose levels and hypothalamic blood flow There was a significant relationship between changes in circulating glucose levels and blood flow in the medial hypothalamus 30 minutes after consuming sucrose (beta = − 0.005 ± 0.002, p < 0.007 ) (see Fig. 6 a and Supplemental Table 5 ). However, no significant association was found between changes in circulating glucose and hypothalamic blood flow after consuming sucralose (p = 0.19; Fig. 6 A). Associations were suggested between circulating glucose and responses in other hypothalamic ROIs after sucrose ingestion, as noted in Supplemental Table 5. In an exploratory analysis stratified by weight status, negative associations were observed between increments in peripheral glucose and the medial hypothalamic response to sucrose in individuals with healthy weight and those overweight, but not in individuals with obesity (see Supplemental Table 6 ) Associations between changes in hypothalamic blood flow and hunger Decreases in medial hypothalamic blood flow observed within 10 minutes after consuming sucrose were associated with reduced hunger post-ingestion ( Supplemental Table 7 , Fig. 6 b). Similar trends were observed in both the lateral hypothalamus and Neudorfer ROI, although these did not reach statistical significance ( Supplemental Table 7 ). No such associations were found following sucralose ingestion ( Supplemental Table 7 , Fig. 6 b). Discussion In this randomized cross-over trial involving healthy young adults of varying weights, we show that drinks sweetened with sucralose, a non-caloric sweetener, led to an increase in hypothalamic blood flow—a purported MRI marker of hunger—when compared to caloric sugar (sucrose) and water. Sucrose, compared to sucralose, had a hunger-dampening effect while also raising peripheral glucose levels, which corresponded to reduced medial hypothalamic blood flow. These results support the notion, initially observed in rodents 5,45 , that non-caloric sweeteners may alter appetite by interfering with the conventional neural responses to sweet taste and nutrient signaling observed with caloric sugar. Stratified analyses based on weight categories revealed variations in the hypothalamic responses to sucralose relative to sucrose and water. Specifically, healthy-weight individuals displayed a marked increase in hypothalamic blood flow in response to sucralose compared to sucrose. This suggests that for individuals of healthy weight, the hypothalamic response to drinks containing the non-caloric sweetener, sucralose, differs significantly from that of the caloric sweetener, sucrose. In contrast, individuals with obesity exhibited a stronger increase in the hypothalamic response to sucralose than to water, indicating a potential relationship between obesity and heightened hypothalamic sensitivity to sweetness. Understanding this relationship may have important implications for the use of non-caloric sweeteners in weight management strategies. Our findings revealed that oral intake of sucrose led to increased peripheral glucose levels and reduced hypothalamic activity, while sucralose had no such effect. This supports existing evidence suggesting that metabolic signals like glucose are tightly connected to changes in hypothalamic activity 18,19,22,25,46 . Importantly, the relationship between peripheral glucose fluctuations and hypothalamic activity was less evident in individuals with obesity, further reinforcing the idea that obesity may disrupt glucose signaling within the hypothalamus. By including two distinct subfields of the hypothalamus—the lateral hypothalamus and the medial hypothalamus, which comprises the ventromedial hypothalamic nucleus (VMH) and the arcuate nucleus—we aimed to explore subfield-specific roles. Previous research in rats showed that bilateral lesions in the VMH led to hyperphagia, while lesions in the lateral hypothalamic area (LHA) resulted in hypophagia 47 . These foundational studies identified the VMH as the "satiety center," which suppresses food intake, and the LHA as the "hunger center," which stimulates eating. Our findings revealed that sucralose, compared to sucrose or water, induced the most pronounced differences in the lateral hypothalamic subfield. However, the response patterns in the medial hypothalamic subfield and the hypothalamus ROI defined by the Neudorfer high-resolution atlas were similar. Importantly, associations between changes in peripheral glucose levels post-sucrose ingestion were notable in the medial hypothalamic subfield but not in the lateral subfield. Furthermore, reductions in blood flow in the medial hypothalamus correlated with decreased hunger. These findings align with prior studies demonstrating that glucose ingestion reduced the fMRI signal in the hypothalamic area corresponding to the VMH, correlating with lower fasting plasma glucose and insulin levels, highlighting the significant role of the medial hypothalamus in nutrient sensing 18 . Finally, our functional connectivity analysis revealed that acute consumption of sucralose, as opposed to sucrose, significantly increased coupling between the hypothalamus and the anterior cingulate cortex (ACC)—an area of the brain that plays a crucial role in attention, motivation, and reward processing 48 . Additionally, compared to water, sucralose intake led to greater connectivity between the right hypothalamus and the left superior parietal lobule, a region integral to somatosensory integration 49 . These results suggest that sucralose may enhance the functional connection between brain regions coordinating appetite with reward and motivation, potentially influencing food-seeking behavior. Limitations There are several limitations to be considered. Our study investigated both the medial and lateral hypothalamic subfields to determine if the varying effects of sucralose, compared to sucrose and water, could be attributed to specific areas within the hypothalamus. However, due to the limitations in spatial resolution, we were unable to precisely attribute these effects to individual neurons within these hypothalamic subfields. While the primary focus of this study is to investigate the isolated effects of the non-caloric sweetener, sucralose, considering that beverages are frequently consumed as part of a meal containing carbohydrates 50 and proteins, further investigation into whether hypothalamic responses are differentially modulated by the ingestion of sucralose within a mixed-meal context would be valuable. Given that the unique chemical structure of each type of non-caloric sweetener may elicit varying physiological responses 51 , future studies should examine whether altered hypothalamic and metabolic responses are also observed in other types of non-caloric sweeteners. Finally, since the study focused on examining the acute effects of sucralose consumption, we did not investigate the impact of chronic consumption of non-caloric sweeteners on hypothalamic signaling and appetitive behaviors. Given that rodent studies have shown chronic consumption of non-nutritive sweeteners to alter central appetite signaling mechanisms 52 , more research addressing the long-term effects of non-caloric sweeteners is warranted. Conclusion Our findings indicate that non-caloric sweeteners could affect key mechanisms in the hypothalamus responsible for appetite regulation, and that individuals with obesity might be particularly susceptible to effects of non-caloric sweeteners on appetite regulation. Considering the prevalent consumption of non-caloric sweeteners, it is vital to conduct comprehensive studies to clarify their long-term health ramifications. References Hales, C. M., Carroll, M. D., Fryar, C. D. & Ogden, C. L. Prevalence of obesity among adults and youth: United States, 2015–2016. (2017). Hu, F. B. & Malik, V. S. Sugar-sweetened beverages and risk of obesity and type 2 diabetes: epidemiologic evidence. Physiology & behavior 100 , 47-54 (2010). Malik, V. S., Pan, A., Willett, W. 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Nature 405 , 1058-1062 (2000). Smeets, P. A., de Graaf, C., Stafleu, A., van Osch, M. J. & van der Grond, J. Functional MRI of human hypothalamic responses following glucose ingestion. Neuroimage 24 , 363-368 (2005). Smeets, P. A. et al. Oral glucose intake inhibits hypothalamic neuronal activity more effectively than glucose infusion. American Journal of Physiology-Endocrinology and Metabolism 293 , E754-E758 (2007). Page, K. A. et al. Effects of fructose vs glucose on regional cerebral blood flow in brain regions involved with appetite and reward pathways. Jama 309 , 63-70 (2013). Luo, S. et al. Resting state hypothalamic response to glucose predicts glucose-induced attenuation in the ventral striatal response to food cues. Appetite 116 , 464-470 (2017). Page, K. A. et al. Children exposed to maternal obesity or gestational diabetes mellitus during early fetal development have hypothalamic alterations that predict future weight gain. Diabetes care 42 , 1473-1480 (2019). Page, K. A. et al. Circulating glucose levels modulate neural control of desire for high-calorie foods in humans. The Journal of clinical investigation 121 , 4161-4169 (2011). Page, K. A. et al. Small decrements in systemic glucose provoke increases in hypothalamic blood flow prior to the release of counterregulatory hormones. Diabetes 58 , 448-452 (2009). Yunker, A. G. et al. Obesity and sex-related associations with differential effects of sucralose vs sucrose on appetite and reward processing: A randomized crossover trial. JAMA Network Open 4 , e2126313-e2126313 (2021). Page, K., Luo, S. & Dorton, H. Neural mechanisms for appetitive response for high reward foods. Open Science Framework. doi 10 (2020). Dye, L. & Blundell, J. Menstrual cycle and appetite control: implications for weight regulation. Human reproduction (Oxford, England) 12 , 1142-1151 (1997). Krishnan, S., Tryon, R. R., Horn, W. F., Welch, L. & Keim, N. L. Estradiol, SHBG and leptin interplay with food craving and intake across the menstrual cycle. Physiology & behavior 165 , 304-312 (2016). Aguirre, G. K., Detre, J. A. & Wang, J. Perfusion fMRI for functional neuroimaging. International review of neurobiology 66 , 213-236 (2005). Detre, J. A., Wang, J., Wang, Z. & Rao, H. Arterial spin-labeled perfusion MRI in basic and clinical neuroscience. Current opinion in neurology 22 , 348-355 (2009). Wang, Y. et al. Regional reproducibility of pulsed arterial spin labeling perfusion imaging at 3T. Neuroimage 54 , 1188-1195 (2011). Baroncini, M. et al. MRI atlas of the human hypothalamus. Neuroimage 59 , 168-180 (2012). Kullmann, S. et al. Resting‐state functional connectivity of the human hypothalamus. Human brain mapping 35 , 6088-6096 (2014). Neudorfer, C. et al. A high-resolution in vivo magnetic resonance imaging atlas of the human hypothalamic region. Scientific Data 7 , 305 (2020). Hoang, H. et al. Low‐calorie diet‐induced weight loss is associated with altered brain connectivity and food desire in obesity. Obesity Whitfield-Gabrieli, S. & Nieto-Castanon, A. Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain connectivity 2 , 125-141 (2012). Nieto-Castanon, A. & Whitfield-Gabrieli, S. CONN functional connectivity toolbox (RRID: SCR_009550), Version 21. Series CONN functional connectivity toolbox (RRID: SCR_009550), Version 21 (2021). Penny, W. D., Friston, K. J., Ashburner, J. T., Kiebel, S. J. & Nichols, T. E. Statistical parametric mapping: the analysis of functional brain images . (Elsevier, 2011). Nieto-Castanon, A. Handbook of functional connectivity magnetic resonance imaging methods in CONN . (Hilbert Press, 2020). Worsley, K. J. et al. A unified statistical approach for determining significant signals in images of cerebral activation. Human brain mapping 4 , 58-73 (1996). Chumbley, J., Worsley, K., Flandin, G. & Friston, K. Topological FDR for neuroimaging. Neuroimage 49 , 3057-3064 (2010). Flint, A., Raben, A., Blundell, J. & Astrup, A. Reproducibility, power and validity of visual analogue scales in assessment of appetite sensations in single test meal studies. International journal of obesity 24 , 38-48 (2000). Swithers, S. E., Baker, C. R. & Davidson, T. General and persistent effects of high-intensity sweeteners on body weight gain and caloric compensation in rats. Behavioral neuroscience 123 , 772 (2009). Kullmann, S. et al. Selective insulin resistance in homeostatic and cognitive control brain areas in overweight and obese adults. Diabetes Care 38 , 1044-1050 (2015). Hetherington, A. & Ranson, S. Hypothalamic lesions and adiposity in the rat. The Anatomical Record 78 , 149-172 (1940). Apps, M. A., Rushworth, M. F. & Chang, S. W. The anterior cingulate gyrus and social cognition: tracking the motivation of others. Neuron 90 , 692-707 (2016). Wang, J. et al. Convergent functional architecture of the superior parietal lobule unraveled with multimodal neuroimaging approaches. Human brain mapping 36 , 238-257 (2015). Dalenberg, J. R. et al. Short-term consumption of sucralose with, but not without, carbohydrate impairs neural and metabolic sensitivity to sugar in humans. Cell Metabolism 31 , 493-502. e497 (2020). Higgins, K. A. & Mattes, R. D. A randomized controlled trial contrasting the effects of 4 low-calorie sweeteners and sucrose on body weight in adults with overweight or obesity. The American journal of clinical nutrition 109 , 1288-1301 (2019). Contreras-Chavez, G. G., Estrada, J. A. & Contreras, I. Changes in appetite regulation-related signaling pathways in the brain of mice supplemented with non-nutritive sweeteners. Journal of Molecular Neuroscience 71 , 1144-1155 (2021). Table Table 1. Participant characteristics N=75 Mean or Freq (SD or Freq %) Range Age (years) 23.33 (3.97) 18.15-34.51 BMI (kg/m 2 ) 27.16 (5.17) 19.18-40.28 BMI Group Obese 23 (30.7%) Overweight 24 (32%) Healthy Weight 28 (37.3%) Sex F 43 (57.33%) M 32 (42.66%) Race & Ethnicity Asian 23 (30.66%) Hispanic 19 (25.33%) Non-Hispanic Black 12 (16%) Non-Hispanic White 21 (28%) Additional Declarations There is NO Competing Interest. Supplementary Files NMstudyprotocolstatisticalplanfinal.pdf SupplementalDatav34062624.pdf Cite Share Download PDF Status: Published Journal Publication published 26 Mar, 2025 Read the published version in Nature Metabolism → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4684043","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":327753644,"identity":"3e50fd2b-34c5-45c1-b207-366ae3350a36","order_by":0,"name":"Kathleen Page","email":"data:image/png;base64,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","orcid":"","institution":"USC","correspondingAuthor":true,"prefix":"","firstName":"Kathleen","middleName":"","lastName":"Page","suffix":""},{"id":327753645,"identity":"3b23d695-96e0-4fe9-923b-8129dfa7dce6","order_by":1,"name":"Sandhya Chakravartti","email":"","orcid":"https://orcid.org/0000-0002-7267-6742","institution":"USC","correspondingAuthor":false,"prefix":"","firstName":"Sandhya","middleName":"","lastName":"Chakravartti","suffix":""},{"id":327753646,"identity":"fda11389-da86-42b2-a09c-e625057ff464","order_by":2,"name":"Kay Jann","email":"","orcid":"","institution":"USC","correspondingAuthor":false,"prefix":"","firstName":"Kay","middleName":"","lastName":"Jann","suffix":""},{"id":327753647,"identity":"02c36744-3aaa-4e14-a1c9-7733664005c8","order_by":3,"name":"Ralf Veit","email":"","orcid":"","institution":"University of Tübingen","correspondingAuthor":false,"prefix":"","firstName":"Ralf","middleName":"","lastName":"Veit","suffix":""},{"id":327753648,"identity":"22391227-a37e-4acd-8812-86f176bc6196","order_by":4,"name":"Hanyang Liu","email":"","orcid":"","institution":"USC","correspondingAuthor":false,"prefix":"","firstName":"Hanyang","middleName":"","lastName":"Liu","suffix":""},{"id":327753649,"identity":"83f04cf7-dff6-47ce-8176-34b006f0eee2","order_by":5,"name":"Alexandra Yunker","email":"","orcid":"","institution":"USC","correspondingAuthor":false,"prefix":"","firstName":"Alexandra","middleName":"","lastName":"Yunker","suffix":""},{"id":327753650,"identity":"c2e0da88-76c3-4c17-aef8-aead823b064a","order_by":6,"name":"Brendan Angelo","email":"","orcid":"","institution":"USC","correspondingAuthor":false,"prefix":"","firstName":"Brendan","middleName":"","lastName":"Angelo","suffix":""},{"id":327753651,"identity":"f6455525-e0d7-4eed-a289-0b091729d0c1","order_by":7,"name":"John Monterosso","email":"","orcid":"","institution":"USC","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"","lastName":"Monterosso","suffix":""},{"id":327753652,"identity":"79fb590d-16a9-42ac-a015-367cb2b6d655","order_by":8,"name":"Anny Xiang","email":"","orcid":"","institution":"Kaiser Permanente Southern California","correspondingAuthor":false,"prefix":"","firstName":"Anny","middleName":"","lastName":"Xiang","suffix":""},{"id":327753653,"identity":"06ad3c45-98e8-42c0-869f-54af7485f781","order_by":9,"name":"Stephanie Kullmann","email":"","orcid":"https://orcid.org/0000-0001-9951-923X","institution":"University of Tuebingen","correspondingAuthor":false,"prefix":"","firstName":"Stephanie","middleName":"","lastName":"Kullmann","suffix":""}],"badges":[],"createdAt":"2024-07-04 06:00:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4684043/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4684043/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s42255-025-01227-8","type":"published","date":"2025-03-26T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60911276,"identity":"b288fdf9-7dae-45bb-a4ae-de45e2fe84c5","added_by":"auto","created_at":"2024-07-23 12:55:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":83049,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eParticipant Enrollment Flowchart for the Randomized Crossover Brain Response to Sugar II Trial and Final Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea \u003c/sup\u003eOf the 76 participants enrolled, who received at least 1 drink allocation, 1 participant did not receive any of the drinks (i.e., sucralose, sucrose, or water) included in this analysis because of dropout and was excluded from this analysis (N=75).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4684043/v1/f6a524fe7f2421e501ff9850.png"},{"id":60912503,"identity":"34c5521d-4754-4d33-98e7-53e8943f79de","added_by":"auto","created_at":"2024-07-23 13:03:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":121211,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic of Study Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBOLD indicates blood oxygen level-dependent.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4684043/v1/f7697fc75ff31599e3fef26b.png"},{"id":60912505,"identity":"0e5d0d7e-f935-4e16-a416-e88d6970481b","added_by":"auto","created_at":"2024-07-23 13:03:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":54598,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential Hypothalamic Response to Drink Comparisons\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSignificant increases in hypothalamic blood flow were observed after sucralose vs sucrose (p = 0.018) and sucralose vs water, (p = 0.019) comparisons, adjusted for multiple comparisons using a Bonferroni correction. Data showing lateral hypothalamic ROI.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4684043/v1/c1e5a3e306a2b812b5a2a337.png"},{"id":60913479,"identity":"f7ba1872-57d6-426a-b3f5-b13cc9d322f7","added_by":"auto","created_at":"2024-07-23 13:11:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":36051,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifference in hypothalamic response to drinks by weight status\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMean Change in lateral hypothalamic blood flow after water, sucrose or sucralose ingestion stratified by weight status. Individuals with healthy weight (green circles) had greater hypothalamic blood flow after sucralose vs sucrose (p=0.027) but not sucralose vs water (p=0.279). Individuals with obesity (red circles) had greater hypothalamic activation after sucralose vs water (p=0.042) but not sucralose vs sucrose (p=0.370). Individuals with overweight (yellow circles) had no differences in hypothalamic response to either drink comparison.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4684043/v1/a29183e569159de6744be62a.png"},{"id":60913481,"identity":"e6061383-876b-427f-b351-ab891f7e71af","added_by":"auto","created_at":"2024-07-23 13:11:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":303333,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential functional connectivity from hypothalamus seed region after sucralose ingestion relative to sucrose and water\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeed-to-voxel analysis comparing functional connectivity between the hypothalamus (seed region) and other brain regions after sucralose ingestion relative to sucrose or water, adjusting for age, sex, BMI, and race/ethnicity. (\u003cstrong\u003ea\u003c/strong\u003e) Sucralose compared to sucrose resulted in increased connectivity between the left hypothalamus and anterior cingulate cortex, (\u003cstrong\u003eb\u003c/strong\u003e) Sucralose compared to water resulted in increased connectivity between the right hypothalamus and left superior parietal lobule. Significance was set at p \u0026lt; 0.05 with correction for multiple comparisons using the false discovery rate (FDR q \u0026lt; 0.05). Hot colors in red, orange and yellow indicate a more positive z score, suggesting greater connectivity after sucralose relative to comparison drink. Neudorfer hypothalamic ROI was used as the seed region. (\u003cstrong\u003ec\u003c/strong\u003e) Sucrose, compared to water, resulted in increased connectivity between the right hypothalamus and precuneus cortex and decreased connectivity between the right hypothalamus and the occipital pole. Five participants had excessive motion during the BOLD acquisition and were excluded from the functional connectivity analysis (N=70).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4684043/v1/8cc494156c831c3fcbdb2a55.png"},{"id":60911280,"identity":"9f200897-4869-47a6-a7cd-80d064751805","added_by":"auto","created_at":"2024-07-23 12:55:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":119584,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociations between peripheral glucose, hunger, and changes in medial hypothalamic blood flow\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eshow scatterplots with Pearson correlations for visual purposes. Linear regression models were used in the statistical analysis and data are reported in Supplemental Tables 5 and 7.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4684043/v1/e90ac4f3cc20a849322f295f.png"},{"id":79332935,"identity":"8bbd0f29-255c-4fd3-bb8d-fc399fb729a6","added_by":"auto","created_at":"2025-03-27 07:08:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1945317,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4684043/v1/1f13b0da-304d-4499-88cb-8addf56c0d2c.pdf"},{"id":60911282,"identity":"06cad4ae-76e8-435f-9223-0823a930e2ae","added_by":"auto","created_at":"2024-07-23 12:55:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":319334,"visible":true,"origin":"","legend":"","description":"","filename":"NMstudyprotocolstatisticalplanfinal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4684043/v1/c607473e016f793f5385d780.pdf"},{"id":60913480,"identity":"eef1e7ce-127e-482e-b6e0-ea664321a4a8","added_by":"auto","created_at":"2024-07-23 13:11:51","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2143326,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SupplementalDatav34062624.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4684043/v1/b7690dc8981c911e3b74cb57.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Non-Caloric Sweetener Effects on Brain Appetite Regulation in Individuals Across Varying Body Weights","fulltext":[{"header":"Introduction","content":"\u003cp\u003eObesity rates have risen dramatically over the last three decades, posing a significant public health challenge\u003csup\u003e1\u003c/sup\u003e. A growing body of evidence links the rise in sugar-sweetened beverage consumption to weight gain and obesity\u003csup\u003e2\u0026ndash;4\u003c/sup\u003e. To address this, non-caloric sweeteners are increasingly consumed as a calorie-free alternative to satisfy the craving for sweetness \u003csup\u003e2\u003c/sup\u003e. Despite their widespread use, the health implications of non-caloric sweeteners remain uncertain and subject to debate \u003csup\u003e3\u003c/sup\u003e. While epidemiological studies link non-caloric sweetener consumption to weight gain\u003csup\u003e4\u003c/sup\u003e, obesity\u003csup\u003e5\u003c/sup\u003e, and type 2 diabetes\u003csup\u003e6\u003c/sup\u003e, randomized controlled trials report that non-caloric sweeteners have neutral or beneficial effects on body weight and glucose metabolism\u003csup\u003e7\u003c/sup\u003e. Studies conducted in rodents suggest that non-caloric sweeteners stimulate hunger by interfering with the conventional neural responses to sweet taste and nutrient signaling that occur with caloric sugar\u003csup\u003e5\u003c/sup\u003e. Human studies using functional magnetic resonance imaging (fMRI) also indicate that the brain may respond differently to beverages containing non-caloric sweeteners compared to caloric sugar \u003csup\u003e6\u0026ndash;8\u003c/sup\u003e. However, previous fMRI studies have often been constrained by small sample sizes, comprising healthy-weight individuals \u003csup\u003e6\u0026ndash;11\u003c/sup\u003e. Furthermore, prior studies have shown a lack of diversity in sex and race/ethnicity, primarily focusing on male and white participants, which limits their external validity\u003csup\u003e10\u0026ndash;12,15,16,18\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, we included a demographically diverse cohort to investigate how sucralose, a prevalent non-caloric sweetener\u003csup\u003e12\u003c/sup\u003e, influences hypothalamic activity and glucose signaling across a range of body weights. Our investigation extends the findings of Yunker et al. (2021), linking obesity with an increased brain response to food cues after sucralose consumption, contrasting with the response to sucrose.\u003c/p\u003e \u003cp\u003eSucralose, chemically altered from sucrose by replacing hydroxyl groups with chlorine, offers sweetness without caloric absorption\u003csup\u003e13\u003c/sup\u003e. Drinks with sucralose were calibrated to match the sweetness of sucrose-containing drinks, allowing us to assess the differential effects of a sweet taste without nutrients (sucralose) compared to a sweet taste with nutrients (sucrose) on hypothalamic activity, glucose concentrations, and hunger responses. We also compared sucralose with water to examine the specific effects of sweetness on hypothalamic activity and the corresponding physiological responses.\u003c/p\u003e \u003cp\u003eThe hypothalamus plays a crucial role in appetite and homeostatic metabolic regulation\u003csup\u003e14,15\u003c/sup\u003e, and functional connectivity between the hypothalamus and other brain areas coordinates homeostatic energy balance\u003csup\u003e14,16,17\u003c/sup\u003e. Prior work shows that ingestion of the simple sugar, glucose, exerts suppressive effects on hypothalamic activation (evidenced in MRI studies as reduced blood oxygen level dependent (BOLD) signal or reduced cerebral blood flow (CBF))\u003csup\u003e15,18\u0026ndash;24\u003c/sup\u003e. The glucose-linked reductions in hypothalamic activity are associated with reductions in hunger\u003csup\u003e22,23\u003c/sup\u003e, while an increase in hypothalamic activity is associated with heightened hunger\u003csup\u003e25,26\u003c/sup\u003e. Based on prior findings, we hypothesized that sucralose, compared to sucrose and water, would cause greater increases in hypothalamic blood flow and would alter functional connectivity between the hypothalamus and other brain regions. Additionally, we expected sucrose, but not sucralose, to raise blood glucose levels, inversely affecting hypothalamic blood flow. We expected to observe differences between the lateral and medial subfields of the hypothalamus, given their distinct functional roles. Finally, we projected that these hypothalamic responses would differ according to participants' weight status.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy Overview\u003c/h2\u003e \u003cp\u003eData are from the Brain Response to Sugar study, an investigation of neuroendocrine responses to high-reward foods (NCT02945475)\u003csup\u003e27\u003c/sup\u003e. Data presented are the primary results of the arterial spin labeling perfusion MRI analyses (pASL) from the sucralose, sucrose, and water conditions from the randomized crossover trial (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Glucose was included in the larger trial for the purposes of testing differences in equicaloric sugars on outcomes\u003csup\u003e27\u003c/sup\u003e. Participants provided written informed consent compliant with the University of Southern California Institutional Review Board (IRB #H-09-00395). This study followed Consolidated Standards of Reporting Trials (CONSORT) guidelines (trial protocol can be found in online digital repository\u003csup\u003e28\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eThe study included an initial screening visit and MRI visits performed at the Dornsife Cognitive Neuroimaging Center of the University of Southern California at approximately 8:00 AM after a 12-hour overnight fast. During the screening visit, height was measured to the nearest 0.1 cm using a stadiometer and weight to the nearest 0.1 kg using a calibrated digital scale. Body mass index (BMI) was calculated as weight in kg divided by height in meters squared. Enrollment occurred between July 2016 and March 2020.\u003c/p\u003e \u003cp\u003eThe three MRI visits were performed in blinded, random order (using function randperm, a computer-generated randomization procedure in Matlab) on separate days between 2 and 2 months apart with ingestion of 300 mL drinks containing either sucrose (75 g), sucralose (individually sweetness matched to sucrose, as previously described\u003csup\u003e27\u003c/sup\u003e) or a water control to test their effects on changes in hypothalamic blood flow, circulating glucose levels, and ratings of hunger \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Hypothalamic blood flow (measured by pulsed arterial spin labeling perfusion MRI), hunger ratings, and glycemic responses were measured fasting, +\u0026thinsp;10 and +\u0026thinsp;35min after drink ingestion. Hunger ratings and glycemic responses were additionally measured 120 min after drink ingestion. Drinks were flavored with 0.25 tsp (1.07g) of non-sweetened cherry flavoring (Kraft Foods Kool-Aid\u0026reg; Unsweetened Cherry Drink Mix) to improve palatability. Females underwent MRI visits during the follicular phase of the menstrual cycle to reduce variability in hunger\u003csup\u003e29,30\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eParticipants included in this analysis were 75 adults (43 female) ages 18 to 35 years with healthy weight, overweight, or obesity (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). They were right-handed, nonsmokers, weight-stable for at least 3 months before the study visits, nondieters, not taking medication (except oral contraceptives), and with no history of diabetes, eating disorders, illicit drug use, or other medical diagnoses.\u003c/p\u003e \u003cp\u003eThe prespecified primary outcome was relative changes in hypothalamus blood flow in response to acute sucralose vs. sucrose and water among the whole cohort and stratified by weight status (healthy-weight, overweight, obese). Secondary outcomes included (1) associations between changes in circulating glucose levels, hypothalamic blood flow, and ratings of hunger in response to sucralose and sucrose; and (2) functional connectivity analysis to investigate brain regions with MR signal responses that were correlated with the hypothalamic response after sucralose relative to sucrose and water.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMRI Acquisition Parameters\u003c/h2\u003e \u003cp\u003eParticipants were scanned at the USC Dana and David Dornsife Neuroimaging Center. Data were collected using a 3T Siemens MAGNETOM Prismafit MRI System, with a 32-channel head coil. A high-resolution 3D magnetization prepared rapid gradient echo (MPRAGE) sequence (TR\u0026thinsp;=\u0026thinsp;1950ms; TE\u0026thinsp;=\u0026thinsp;2.26ms; bandwidth\u0026thinsp;=\u0026thinsp;200Hz/pixel; flip angle\u0026thinsp;=\u0026thinsp;9\u0026deg;; slice thickness\u0026thinsp;=\u0026thinsp;1mm; FOV\u0026thinsp;=\u0026thinsp;224mm\u0026times;256mm; matrix\u0026thinsp;=\u0026thinsp;224\u0026times;256) was used to acquire structural images for multi-subject registration.\u003c/p\u003e \u003cp\u003ePulsed arterial spin labeling (pASL) was used to quantify cerebral blood flow (CBF) changes in response to acute consumption of the different drinks. pASL provides a measure of CBF by magnetically tagging arterial blood directly before it enters the brain and measuring the amount of tagged blood to reach specific brain areas\u003csup\u003e31,32\u003c/sup\u003e. To acquire CBF maps, pulsed arterial spin labelling images were obtained with a PICORE-Q2TIPS (proximal inversion with control for off-resonance effects\u0026mdash;quantitative imaging of perfusion by using a single subtraction) sequence by using a frequency offset corrected inversion pulse and echo planar imaging readout for acquisition\u003csup\u003e33\u003c/sup\u003e. The pASL acquisition parameters used in this study were: FOV\u0026thinsp;=\u0026thinsp;192 mm; matrix\u0026thinsp;=\u0026thinsp;64x64; bandwidth\u0026thinsp;=\u0026thinsp;2232 Hz/Pixel; slice thickness\u0026thinsp;=\u0026thinsp;5 mm; in-plane resolution\u0026thinsp;=\u0026thinsp;3 \u0026times; 3 mm\u003csup\u003e2\u003c/sup\u003e; interslice spacing\u0026thinsp;=\u0026thinsp;0 mm; TR\u0026thinsp;=\u0026thinsp;4000 ms, and flip angle\u0026thinsp;=\u0026thinsp;90; bolus duration (TI1)\u0026thinsp;=\u0026thinsp;.7 seconds, inversion time (TI2)\u0026thinsp;=\u0026thinsp;1.8 seconds; number of label/control pairs\u0026thinsp;=\u0026thinsp;60. The first control volume of the pASL sequence was used as calibration image for CBF quantification. The temporal stability of pASL compliments the longer curve of glucose responses that we measured with the accompanying blood draws during the study sessions. Blood oxygen level-dependent (BOLD) fMRI was acquired with a multi-band interleaved gradient echo planar imaging sequence. Eighty-eight 1.5-mm thick slices covering the whole brain were acquired using the following parameters: TR\u0026thinsp;=\u0026thinsp;1,000ms; TE\u0026thinsp;=\u0026thinsp;43.20ms; bandwidth\u0026thinsp;=\u0026thinsp;2,055Hz/pixel; flip angle\u0026thinsp;=\u0026thinsp;52\u0026deg;; Multi Band factor\u0026thinsp;=\u0026thinsp;8; FOV\u0026thinsp;=\u0026thinsp;128mm\u0026times;112mm, matrix\u0026thinsp;=\u0026thinsp;128\u0026times;112. number of volumes\u0026thinsp;=\u0026thinsp;376 volumes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eArterial Spin Labeling Analysis\u003c/h2\u003e \u003cp\u003eWe used the Bayesian Inference for Arterial Spin Labeling (BASIL) toolbox, part of the Oxford FMRIB Software Library (FSL), to determine mean CBF across the entire brain, as well as regional CBF in the hypothalamus and hypothalamic subfields. Following motion correction of the whole image sequence, CBF quantification was performed based on a single compartment model and voxel-wise calibration. The hypothalamic response to drink was measured as change in regional CBF (see section below) after drink (averaged across post drink time points) divided by whole brain CBF to adjust for changes occurring across the whole brain. This post drink value was then subtracted from pre drink value to correct for baseline CBF. To reduce confounding effects due to limited spatial resolution, we adjusted for partial volume effects.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eHypothalamus Regions of Interest\u003c/h2\u003e \u003cp\u003eWe examined the hypothalamic response to different drink conditions by focusing on two distinct hypothalamic subfields, the lateral hypothalamic (LH) and medial hypothalamic (MH). This approach was driven by the well-recognized functional differences between these two areas of the hypothalamus \u003csup\u003e14\u003c/sup\u003e. The lateral and medial hypothalamic subfield ROI masks were anatomically defined according to Baroncini et al.\u003csup\u003e34\u003c/sup\u003e and previously used to examine hypothalamic appetite regulation\u003csup\u003e35\u003c/sup\u003e. We included an additional exploratory subfield based on the high-resolution Neudorfer anatomical atlas of hypothalamic nuclei related to energy regulation, in order to enable direct comparison with future studies that may employ similar methodologies \u003csup\u003e36\u003c/sup\u003e. The Neudorfer hypothalamus ROI includes all hypothalamic nuclei related to energy regulation: lateral hypothalamus, ventromedial nucleus, dorsomedial hypothalamic nucleus, arcuate nucleus, and paraventricular nucleus. A single mask was created and normalized into MNI152 space. The Neudorfer ROI was used in bilateral functional connectivity analyses employing the comprehensive mask encompassing hypothalamic nuclei involved in energy regulation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFunctional Connectivity Analysis\u003c/h2\u003e \u003cp\u003eBOLD-fMRI data were collected during a visual food-cue task, where food and non-food images were presented to participants in random order as previously described\u003csup\u003e27\u003c/sup\u003e. To explore the primary effects of different drinks on hypothalamic activity and its functional connections, we performed an analysis across the whole fMRI time series including both food and non-food stimuli. This approach allowed us to identify differences in hypothalamic functional connections after the consumption of sucralose relative to sucrose and water. A similar methodology was previously applied to investigate how increments in peripheral glucose affect brain connectivity during visual food and non-food tasks\u003csup\u003e37\u003c/sup\u003e. Data were processed using Conn Toolbox v21.a (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nitrc.org/projects/conn\u003c/span\u003e\u003cspan address=\"https://www.nitrc.org/projects/conn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e38,39\u003c/sup\u003e and Statistical Parametric Mapping (SPM) v12.7 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fil.ion.ucl.ac.uk/spm/\u003c/span\u003e\u003cspan address=\"https://www.fil.ion.ucl.ac.uk/spm/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e40\u003c/sup\u003e. Preprocessing included motion alignment, regression of physiological noise fluctuations and bandpass filtering between 0.008 Hz and 0.09 Hz. Noise-corrected fMRI images were then co-registered to anatomical T1 weighted images, normalized into MNI standard space. We then performed seed-to-voxel analyses starting from a seed in the hypothalamus as described above. Results were utilized in second-level group analyses comparing functional connectivity between drink conditions using a full factorial model. This model incorporated drink condition as a between-subjects factor and included 4 covariates correcting for age, sex, BMI, and race/ethnicity. Group-level analyses were performed using a weighted general linear model (GLM)\u003csup\u003e41\u003c/sup\u003e, which evaluated voxel-level hypotheses and accounted for random effects across subjects and sample covariance estimation across multiple measurements. Statistical inferences for clusters were based on Gaussian Random Field theory\u003csup\u003e41,42\u003c/sup\u003e, with significance determined using a voxel-level threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;.001 and an FDR-corrected cluster-size threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003csup\u003e43\u003c/sup\u003e. See (\u003cb\u003eSupplemental File 1\u003c/b\u003e) for more details of methods.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eGlucose Assay\u003c/h2\u003e \u003cp\u003ePlasma glucose was measured enzymatically using glucose oxidase (YSI 2300 STAT PLUS Enzymatic Electrode-YSI analyzer, Yellow Springs Instruments).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eHunger Ratings\u003c/h2\u003e \u003cp\u003eVisual analogue scales were used to assess feelings of hunger on a scale from 1 to 10 where 1 was \u0026ldquo;not at all\u0026rdquo; and 10 was\u0026rdquo; very much\u0026rdquo;. Hunger was assessed at baseline, +\u0026thinsp;10, +35 and +\u0026thinsp;120 min after drink consumption. Prior studies have demonstrated good reproducibility and validity of these VAS scores for assessing subjective sensations of hunger\u003csup\u003e44\u003c/sup\u003e .\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were used to characterize frequency for categorical variables and mean (SD) and median (interquartile range) for continuous variables and to check distributional properties. Linear mixed-effects models were employed for comparisons between sucralose and sucrose, aiming to assess the impact of sweet taste without calories (sucralose) versus sweet taste with calories (sucrose), and sucralose versus water to investigate the effects of a sweetened drink without calories (sucralose) versus a non-sweet drink without calories (water). A linear contrast with a significance threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;.05 was used to compare changes from before to after ingestion between the sucralose vs sucrose and sucralose vs water conditions. Since there was no significant interaction between drink condition and time on hypothalamic response, drink condition was collapsed across both time points. Glycemic measures and hunger ratings were collapsed across three time points. We also examined the main effect of BMI group on the hypothalamic response across all drinks, tested for interactions between BMI group and drink comparisons, and stratified drink comparisons by weight status group. Linear mixed effects models assumed random intercept for each subject. General linear regression models were used to assess the association of changes in circulating glucose (independent variable) with hypothalamic response (dependent variable) after drink ingestion and the association between hypothalamic responses (independent variable) with hunger ratings (dependent variable) after drink ingestion. Models were adjusted for age, sex, BMI and race/ethnicity. Post hoc comparisons of drink contrasts and time points were adjusted for multiple comparisons using when necessary, using a Bonferroni correction, with significance levels set at .05. Model fits were examined using the r2beta function from the r2glmm package. All statistical analyses were performed used Rstudio (version 2023.06.1).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eHypothalamic Blood Flow Response: Comparing Sucralose to Sucrose and Water\u003c/h2\u003e \u003cp\u003eThere were no baseline differences in hypothalamic blood flow among the three drink sessions (p\u0026thinsp;=\u0026thinsp;0.40). There was a significant effect of drink condition on the lateral hypothalamic blood flow response (F (2, 363.88)\u0026thinsp;=\u0026thinsp;5.05, p\u0026thinsp;\u0026lt;\u0026thinsp;.007, R\u003csup\u003e2\u003c/sup\u003e model\u0026thinsp;=\u0026thinsp;.059). Specifically, the response in the lateral ROI was higher after consuming sucralose compared to sucrose (Mean difference\u0026thinsp;=\u0026thinsp;.079\u0026thinsp;\u0026plusmn;\u0026thinsp;0.028; p\u0026thinsp;\u0026lt;\u0026thinsp;.018) and sucralose compared to water (Mean difference\u0026thinsp;=\u0026thinsp;.078\u0026thinsp;\u0026plusmn;\u0026thinsp;0.028; p\u0026thinsp;\u0026lt;\u0026thinsp;.019), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003eand Supplemental Table\u0026nbsp;1\u003c/b\u003e. While there was no interaction between drink and time on the hypothalamic response (p\u0026thinsp;=\u0026thinsp;0.26), the hypothalamic response to drinks over time is shown in Supplemental Fig.\u0026nbsp;2A and Supplemental Table\u0026nbsp;8 for completeness. No significant difference was observed in the medial hypothalamic ROI after sucralose compared to sucrose, although the response was greater after sucralose compared to water (\u003cb\u003eSupplemental Table\u0026nbsp;1\u003c/b\u003e). Additionally, differences in the Neudorfer ROI between sucralose and water were observed (\u003cb\u003eSupplemental Table\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eAlthough the interaction between weight status and drink contrasts did not reach statistical significance, we stratified the results by weight status to explore our hypothesis that weight status influences hypothalamic responses to sucralose compared to sucrose and water. In the lateral hypothalamic ROI, individuals with obesity had greater hypothalamic responses to sucralose vs water (beta\u0026thinsp;=\u0026thinsp;.105\u0026thinsp;\u0026plusmn;\u0026thinsp;0.052; p\u0026thinsp;=\u0026thinsp;.042) but not sucralose vs sucrose (beta\u0026thinsp;=\u0026thinsp;.046\u0026thinsp;\u0026plusmn;\u0026thinsp;0.051; p\u0026thinsp;=\u0026thinsp;.37). In contrast, individuals with healthy-weight had greater hypothalamic responses to sucralose vs sucrose (beta\u0026thinsp;=\u0026thinsp;.106\u0026thinsp;\u0026plusmn;\u0026thinsp;0.048; p\u0026thinsp;=\u0026thinsp;.027) and no differences between sucralose vs water (beta\u0026thinsp;=\u0026thinsp;.051\u0026thinsp;\u0026plusmn;\u0026thinsp;0.047; p\u0026thinsp;=\u0026thinsp;.28). There were no differences in individuals with overweight to either sucralose vs sucrose (beta\u0026thinsp;=\u0026thinsp;.081\u0026thinsp;\u0026plusmn;\u0026thinsp;0.049; p\u0026thinsp;=\u0026thinsp;.102) or sucralose vs water (beta\u0026thinsp;=\u0026thinsp;.081\u0026thinsp;\u0026plusmn;\u0026thinsp;0.049; p\u0026thinsp;=\u0026thinsp;.103) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cb\u003eSupplemental Table\u0026nbsp;2\u003c/b\u003e). The Neudorfer and medial hypothalamic ROIs showed differential responses to sucralose relative to sucrose comparisons only among those with healthy-weight (\u003cb\u003eSupplemental Table\u0026nbsp;2\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eWe also examined the hypothalamic responses to sucrose compared with water. There were no differences in hypothalamic blood flow when comparing sucrose to water conditions across BMI groups (\u003cb\u003eSupplemental Table\u0026nbsp;1\u003c/b\u003e). However, the data revealed a pattern where individuals with healthy-weight showed a non-significant trend towards decreased lateral hypothalamic blood flow in response to sucrose compared to water (mean difference = -0.06, \u0026plusmn; .047 p\u0026thinsp;=\u0026thinsp;.25), in contrast to individuals with obesity, who showed an increase, though this was also not statistically significant (mean difference\u0026thinsp;=\u0026thinsp;0.059, \u0026plusmn; .051, p\u0026thinsp;=\u0026thinsp;.25) (\u003cb\u003eSupplemental Table\u0026nbsp;2\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eHypothalamic Seed-to-Voxel Connectivity\u003c/h2\u003e \u003cp\u003eStatistical analysis of the connectivity from the bilateral hypothalamus to all other voxels in the brain resulted in different connectivity patterns after the ingestion of sucralose, relative to sucrose and water. After ingestion of sucralose relative to sucrose, we observed increased connectivity between the left hypothalamus and anterior cingulate cortex (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). After ingestion of sucralose relative to water, we observed increased connectivity between the right hypothalamus and left superior parietal lobule (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). After ingestion of sucrose relative to water, we observed increased connectivity between the right hypothalamus and precuneus cortex and decreased connection between right hypothalamus and occipital pole (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003ec).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCirculating Glucose Responses\u003c/h2\u003e \u003cp\u003eMean results for the effects of sucrose, sucralose, and water on changes in peripheral glucose levels were previously reported\u003csup\u003e27\u003c/sup\u003e. There were no baseline differences in peripheral glucose levels among the three drink sessions (p\u0026thinsp;=\u0026thinsp;0.596). There was a significant effect of drink on the peripheral glucose response (F (2, 552.65)\u0026thinsp;=\u0026thinsp;139.36, p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.00001, R\u003csup\u003e2\u003c/sup\u003e model\u0026thinsp;=\u0026thinsp;.34), adjusting for age, sex, race/ethnicity, and BMI. Post hoc analysis showed a marked increase in peripheral glucose following sucrose compared to sucralose intake (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0003), but no differences were observed in peripheral glucose levels when comparing sucralose to water (p\u0026thinsp;=\u0026thinsp;.99) (\u003cb\u003eSupplemental Table\u0026nbsp;3\u003c/b\u003e). There was an interaction between time and drink on peripheral glucose levels (p\u0026thinsp;\u0026lt;\u0026thinsp;0.002), and drink comparison by time on peripheral glucose levels shown in \u003cb\u003eSupplemental Fig.\u0026nbsp;2b\u003c/b\u003e and \u003cb\u003eSupplemental Table\u0026nbsp;8\u003c/b\u003e. There were no differences in peripheral glucose levels by weight status (p\u0026thinsp;\u0026lt;\u0026thinsp;.48).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eHunger responses\u003c/h2\u003e \u003cp\u003eThere were no differences in baseline hunger ratings among the drink sessions (p\u0026thinsp;=\u0026thinsp;0.678), however, there was a significant effect of drink condition on changes in hunger (F (2,580.21)\u0026thinsp;=\u0026thinsp;10.79, p\u0026thinsp;\u0026lt;\u0026thinsp;.00005, R\u003csup\u003e2\u003c/sup\u003e model\u0026thinsp;=\u0026thinsp;.153). Post hoc analysis revealed a significant increase in hunger after sucralose compared to sucrose (mean diff\u0026thinsp;=\u0026thinsp;.575\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16; p\u0026thinsp;\u0026lt;\u0026thinsp;.001) but no differences after sucralose vs water (mean diff\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.090\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16; p\u0026thinsp;=\u0026thinsp;.99) (\u003cb\u003eSupplemental Table\u0026nbsp;4, Supplemental Fig.\u0026nbsp;2\u003c/b\u003e). While there was no interaction between time and drink on hunger ratings (p\u0026thinsp;=\u0026thinsp;0.38), drink comparisons by time on hunger are shown in \u003cb\u003eSupplemental Fig.\u0026nbsp;2c\u003c/b\u003e and \u003cb\u003eSupplemental Table\u0026nbsp;8\u003c/b\u003e for informational purposes. A trending difference in hunger was observed by weight status (p\u0026thinsp;=\u0026thinsp;.053), with greater hunger among individuals with obesity compared to healthy-weight (mean diff\u0026thinsp;=\u0026thinsp;.975\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40; p\u0026thinsp;=\u0026thinsp;.056).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAssociations between changes in peripheral glucose levels and hypothalamic blood flow\u003c/h2\u003e \u003cp\u003eThere was a significant relationship between changes in circulating glucose levels and blood flow in the medial hypothalamus 30 minutes after consuming sucrose (beta\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.005\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002, p\u0026thinsp;\u0026lt;\u0026thinsp;0.007\u003cem\u003e)\u003c/em\u003e (see Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003ea \u003cb\u003eand Supplemental Table\u0026nbsp;5\u003c/b\u003e). However, no significant association was found between changes in circulating glucose and hypothalamic blood flow after consuming sucralose (p\u0026thinsp;=\u0026thinsp;0.19; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Associations were suggested between circulating glucose and responses in other hypothalamic ROIs after sucrose ingestion, as noted in Supplemental Table\u0026nbsp;5. In an exploratory analysis stratified by weight status, negative associations were observed between increments in peripheral glucose and the medial hypothalamic response to sucrose in individuals with healthy weight and those overweight, but not in individuals with obesity (see \u003cb\u003eSupplemental Table\u0026nbsp;6\u003c/b\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eAssociations between changes in hypothalamic blood flow and hunger\u003c/h2\u003e \u003cp\u003eDecreases in medial hypothalamic blood flow observed within 10 minutes after consuming sucrose were associated with reduced hunger post-ingestion (\u003cb\u003eSupplemental Table\u0026nbsp;7\u003c/b\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). Similar trends were observed in both the lateral hypothalamus and Neudorfer ROI, although these did not reach statistical significance (\u003cb\u003eSupplemental Table\u0026nbsp;7\u003c/b\u003e). No such associations were found following sucralose ingestion (\u003cb\u003eSupplemental Table\u0026nbsp;7\u003c/b\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eb).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this randomized cross-over trial involving healthy young adults of varying weights, we show that drinks sweetened with sucralose, a non-caloric sweetener, led to an increase in hypothalamic blood flow\u0026mdash;a purported MRI marker of hunger\u0026mdash;when compared to caloric sugar (sucrose) and water. Sucrose, compared to sucralose, had a hunger-dampening effect while also raising peripheral glucose levels, which corresponded to reduced medial hypothalamic blood flow. These results support the notion, initially observed in rodents\u003csup\u003e5,45\u003c/sup\u003e, that non-caloric sweeteners may alter appetite by interfering with the conventional neural responses to sweet taste and nutrient signaling observed with caloric sugar.\u003c/p\u003e \u003cp\u003eStratified analyses based on weight categories revealed variations in the hypothalamic responses to sucralose relative to sucrose and water. Specifically, healthy-weight individuals displayed a marked increase in hypothalamic blood flow in response to sucralose compared to sucrose. This suggests that for individuals of healthy weight, the hypothalamic response to drinks containing the non-caloric sweetener, sucralose, differs significantly from that of the caloric sweetener, sucrose. In contrast, individuals with obesity exhibited a stronger increase in the hypothalamic response to sucralose than to water, indicating a potential relationship between obesity and heightened hypothalamic sensitivity to sweetness. Understanding this relationship may have important implications for the use of non-caloric sweeteners in weight management strategies.\u003c/p\u003e \u003cp\u003eOur findings revealed that oral intake of sucrose led to increased peripheral glucose levels and reduced hypothalamic activity, while sucralose had no such effect. This supports existing evidence suggesting that metabolic signals like glucose are tightly connected to changes in hypothalamic activity\u003csup\u003e18,19,22,25,46\u003c/sup\u003e. Importantly, the relationship between peripheral glucose fluctuations and hypothalamic activity was less evident in individuals with obesity, further reinforcing the idea that obesity may disrupt glucose signaling within the hypothalamus.\u003c/p\u003e \u003cp\u003eBy including two distinct subfields of the hypothalamus\u0026mdash;the lateral hypothalamus and the medial hypothalamus, which comprises the ventromedial hypothalamic nucleus (VMH) and the arcuate nucleus\u0026mdash;we aimed to explore subfield-specific roles. Previous research in rats showed that bilateral lesions in the VMH led to hyperphagia, while lesions in the lateral hypothalamic area (LHA) resulted in hypophagia\u003csup\u003e47\u003c/sup\u003e. These foundational studies identified the VMH as the \"satiety center,\" which suppresses food intake, and the LHA as the \"hunger center,\" which stimulates eating. Our findings revealed that sucralose, compared to sucrose or water, induced the most pronounced differences in the lateral hypothalamic subfield. However, the response patterns in the medial hypothalamic subfield and the hypothalamus ROI defined by the Neudorfer high-resolution atlas were similar. Importantly, associations between changes in peripheral glucose levels post-sucrose ingestion were notable in the medial hypothalamic subfield but not in the lateral subfield. Furthermore, reductions in blood flow in the medial hypothalamus correlated with decreased hunger. These findings align with prior studies demonstrating that glucose ingestion reduced the fMRI signal in the hypothalamic area corresponding to the VMH, correlating with lower fasting plasma glucose and insulin levels, highlighting the significant role of the medial hypothalamus in nutrient sensing\u003csup\u003e18\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFinally, our functional connectivity analysis revealed that acute consumption of sucralose, as opposed to sucrose, significantly increased coupling between the hypothalamus and the anterior cingulate cortex (ACC)\u0026mdash;an area of the brain that plays a crucial role in attention, motivation, and reward processing \u003csup\u003e48\u003c/sup\u003e. Additionally, compared to water, sucralose intake led to greater connectivity between the right hypothalamus and the left superior parietal lobule, a region integral to somatosensory integration\u003csup\u003e49\u003c/sup\u003e. These results suggest that sucralose may enhance the functional connection between brain regions coordinating appetite with reward and motivation, potentially influencing food-seeking behavior.\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThere are several limitations to be considered. Our study investigated both the medial and lateral hypothalamic subfields to determine if the varying effects of sucralose, compared to sucrose and water, could be attributed to specific areas within the hypothalamus. However, due to the limitations in spatial resolution, we were unable to precisely attribute these effects to individual neurons within these hypothalamic subfields. While the primary focus of this study is to investigate the isolated effects of the non-caloric sweetener, sucralose, considering that beverages are frequently consumed as part of a meal containing carbohydrates\u003csup\u003e50\u003c/sup\u003e and proteins, further investigation into whether hypothalamic responses are differentially modulated by the ingestion of sucralose within a mixed-meal context would be valuable. Given that the unique chemical structure of each type of non-caloric sweetener may elicit varying physiological responses\u003csup\u003e51\u003c/sup\u003e, future studies should examine whether altered hypothalamic and metabolic responses are also observed in other types of non-caloric sweeteners. Finally, since the study focused on examining the acute effects of sucralose consumption, we did not investigate the impact of chronic consumption of non-caloric sweeteners on hypothalamic signaling and appetitive behaviors. Given that rodent studies have shown chronic consumption of non-nutritive sweeteners to alter central appetite signaling mechanisms\u003csup\u003e52\u003c/sup\u003e, more research addressing the long-term effects of non-caloric sweeteners is warranted.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur findings indicate that non-caloric sweeteners could affect key mechanisms in the hypothalamus responsible for appetite regulation, and that individuals with obesity might be particularly susceptible to effects of non-caloric sweeteners on appetite regulation. \u0026nbsp;Considering the prevalent consumption of non-caloric sweeteners, it is vital to conduct comprehensive studies to clarify their long-term health ramifications.\u003c/p\u003e"},{"header":" References","content":"\u003col\u003e\n \u003cli\u003eHales, C. M., Carroll, M. D., Fryar, C. D. \u0026amp; Ogden, C. L. Prevalence of obesity among adults and youth: United States, 2015\u0026ndash;2016. 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The anterior cingulate gyrus and social cognition: tracking the motivation of others. \u003cem\u003eNeuron\u003c/em\u003e \u003cstrong\u003e90\u003c/strong\u003e, 692-707 (2016).\u003c/li\u003e\n \u003cli\u003eWang, J.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Convergent functional architecture of the superior parietal lobule unraveled with multimodal neuroimaging approaches. \u003cem\u003eHuman brain mapping\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 238-257 (2015).\u003c/li\u003e\n \u003cli\u003eDalenberg, J. R.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Short-term consumption of sucralose with, but not without, carbohydrate impairs neural and metabolic sensitivity to sugar in humans. \u003cem\u003eCell Metabolism\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, 493-502. e497 (2020).\u003c/li\u003e\n \u003cli\u003eHiggins, K. A. \u0026amp; Mattes, R. D. A randomized controlled trial contrasting the effects of 4 low-calorie sweeteners and sucrose on body weight in adults with overweight or obesity. \u003cem\u003eThe American journal of clinical nutrition\u003c/em\u003e \u003cstrong\u003e109\u003c/strong\u003e, 1288-1301 (2019).\u003c/li\u003e\n \u003cli\u003eContreras-Chavez, G. G., Estrada, J. A. \u0026amp; Contreras, I. Changes in appetite regulation-related signaling pathways in the brain of mice supplemented with non-nutritive sweeteners. \u003cem\u003eJournal of Molecular Neuroscience\u003c/em\u003e \u003cstrong\u003e71\u003c/strong\u003e, 1144-1155 (2021).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1. Participant characteristics\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"277\" style=\"margin-right: calc(69%); width: 31%;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.96028880866426%\" valign=\"top\"\u003e\n \u003cp\u003eN=75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.768953068592058%\" valign=\"top\"\u003e\n \u003cp\u003eMean or Freq (SD or Freq %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.270758122743683%\" valign=\"top\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.96028880866426%\" valign=\"top\"\u003e\n \u003cp\u003eAge (years)\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.768953068592058%\" valign=\"top\"\u003e\n \u003cp\u003e23.33 (3.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.270758122743683%\" valign=\"top\"\u003e\n \u003cp\u003e18.15-34.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.96028880866426%\" valign=\"top\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.768953068592058%\" valign=\"top\"\u003e\n \u003cp\u003e27.16 (5.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.270758122743683%\" valign=\"top\"\u003e\n \u003cp\u003e19.18-40.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.96028880866426%\" valign=\"top\"\u003e\n \u003cp\u003eBMI Group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.768953068592058%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.270758122743683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.96028880866426%\" valign=\"top\"\u003e\n \u003cp\u003eObese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.768953068592058%\" valign=\"top\"\u003e\n \u003cp\u003e23 (30.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.270758122743683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.96028880866426%\" valign=\"top\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.768953068592058%\" valign=\"top\"\u003e\n \u003cp\u003e24 (32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.270758122743683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.96028880866426%\" valign=\"top\"\u003e\n \u003cp\u003eHealthy Weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.768953068592058%\" valign=\"top\"\u003e\n \u003cp\u003e28 (37.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.270758122743683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.96028880866426%\" valign=\"top\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.768953068592058%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.270758122743683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.96028880866426%\" valign=\"top\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.768953068592058%\" valign=\"top\"\u003e\n \u003cp\u003e43 (57.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.270758122743683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.96028880866426%\" valign=\"top\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.768953068592058%\" valign=\"top\"\u003e\n \u003cp\u003e32 (42.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.270758122743683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.96028880866426%\" valign=\"top\"\u003e\n \u003cp\u003eRace \u0026amp; Ethnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.768953068592058%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.270758122743683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.96028880866426%\" valign=\"top\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.768953068592058%\" valign=\"top\"\u003e\n \u003cp\u003e23 (30.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.270758122743683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.96028880866426%\" valign=\"top\"\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.768953068592058%\" valign=\"top\"\u003e\n \u003cp\u003e19 (25.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.270758122743683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.96028880866426%\" valign=\"top\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.768953068592058%\" valign=\"top\"\u003e\n \u003cp\u003e12 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.270758122743683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.96028880866426%\" valign=\"top\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.768953068592058%\" valign=\"top\"\u003e\n \u003cp\u003e21 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.270758122743683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4684043/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4684043/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction:\u003c/strong\u003e Sucralose is a non-caloric sweetener commonly consumed to provide sweet taste without calories. Yet, some studies suggest that non-caloric sweeteners stimulate appetite, possibly due to the delivery of a sweet taste without the post-ingestive metabolic signals that normally communicate with the hypothalamus to suppress hunger. We tested the hypothesis that acute consumption of the non-caloric sweetener, sucralose, would stimulate greater increases in hypothalamic blood flow, an MRI correlate of hunger, compared to caloric sugar (sucrose) and water, and would alter functional connectivity between the hypothalamus and other brain regions. Additionally, we expected sucrose, but not sucralose, to raise blood glucose levels, inversely affecting hypothalamic blood flow. We anticipated variations in hypothalamic responses based on weight status.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Seventy-five young adults with healthy-weight, overweight, or obesity from a random-order crossover design study (NCT02945475) with acute consumption of a drink containing either 75g sucrose, sucralose (individually sweetness matched to sucrose), or plain water were included in this analysis to compare the effects of sucralose relative to sucrose and water on changes in hypothalamic blood flow, circulating glucose levels, and ratings of hunger. Hypothalamic blood flow (measured by pulsed arterial spin labeling perfusion MRI), hunger ratings, and glycemic responses were concurrently measured fasting, +10, and +35min after drink ingestion. As a secondary outcome, functional connectivity was performed using blood oxygen level-dependent (BOLD) fMRI to explore changes between the hypothalamus seed region and other brain areas after ingestion of sucralose relative to sucrose and water.Linear mixed-effects models were used for comparisons of drink contrasts. Linear regressions were used to examine associations between peripheral glucose levels, hypothalamic blood flow, and hunger ratings. Models were adjusted for age, sex, BMI, and race/ethnicity. Post hoc comparisons were adjusted for multiple comparisons using a Bonferroni correction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e There was a significant effect of drink on hypothalamic blood flow, adjusting for age, sex, BMI, and race/ethnicity (F = 5.05; p\u0026lt;.007). Compared to sucrose, intake of drinks sweetened with sucralose produced greater hypothalamic blood flow (Mean diff = .079, ± 0.03, p \u0026lt; .018) and greater hunger responses (Mean diff = .575 ± 0.16, p \u0026lt; .001). Sucralose vs water also increased hypothalamic blood flow (Mean diff = .078 ± 0.03, p \u0026lt; .019), but produced no difference in hunger ratings. Sucrose, but not sucralose, produced increases in peripheral glucose levels, which were associated with reductions in medial hypothalamic blood flow (beta =-.005 ± .002, p \u0026lt; .007). Sucralose, compared to sucrose and water, resulted in increased functional connections between the hypothalamus and brain regions involved in motivation and somatosensory processing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e These results underscore the notable differences in sucralose, a non-caloric sweetener, on hypothalamic signaling pathways linked to appetite regulation when compared to sugar or water. The findings suggest that non-caloric sweeteners could affect key mechanisms in the hypothalamus responsible for appetite regulation.\u003c/p\u003e","manuscriptTitle":"Non-Caloric Sweetener Effects on Brain Appetite Regulation in Individuals Across Varying Body Weights","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-23 12:55:46","doi":"10.21203/rs.3.rs-4684043/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-metabolism","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"natmetab","sideBox":"Learn more about [Nature Metabolism](http://www.nature.com/natmetab/)","snPcode":"","submissionUrl":"","title":"Nature Metabolism","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ce8144cb-0156-4427-a26f-771ec482c34f","owner":[],"postedDate":"July 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":34689974,"name":"Health sciences/Medical research/Clinical trial design/Randomized controlled trials"},{"id":34689975,"name":"Biological sciences/Neuroscience/Feeding behaviour/Hypothalamus"},{"id":34689976,"name":"Health sciences/Endocrinology/Endocrine system and metabolic diseases/Obesity"}],"tags":[],"updatedAt":"2025-03-27T07:08:02+00:00","versionOfRecord":{"articleIdentity":"rs-4684043","link":"https://doi.org/10.1038/s42255-025-01227-8","journal":{"identity":"nature-metabolism","isVorOnly":false,"title":"Nature Metabolism"},"publishedOn":"2025-03-26 04:00:00","publishedOnDateReadable":"March 26th, 2025"},"versionCreatedAt":"2024-07-23 12:55:46","video":"","vorDoi":"10.1038/s42255-025-01227-8","vorDoiUrl":"https://doi.org/10.1038/s42255-025-01227-8","workflowStages":[]},"version":"v1","identity":"rs-4684043","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4684043","identity":"rs-4684043","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}