Insulin sensitivity in mesolimbic pathways predicts and improves with weight loss in older dieters

  1. Lena J Tiedemann
  2. Sebastian M Meyhöfer
  3. Paul Francke
  4. Judith Beck
  5. Christian Büchel
  6. Stefanie Brassen  Is a corresponding author
  1. Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Germany
  2. Institute for Endocrinology & Diabetes, University of Lübeck, Germany
  3. German Center for Diabetes Research (DZD), Germany

Abstract

Central insulin is critically involved in the regulation of hedonic feeding. Insulin resistance in overweight has recently been shown to reduce the inhibitory function of insulin in the human brain. How this relates to effective weight management is unclear, especially in older people, who are highly vulnerable to hyperinsulinemia and in whom neural target systems of insulin action undergo age-related changes. Here, 50 overweight, non-diabetic older adults participated in a double-blind, placebo-controlled, pharmacological functional magnetic resonance imaging study before and after randomization to a 3-month caloric restriction or active waiting group. Our data show that treatment outcome in dieters can be predicted by baseline measures of individual intranasal insulin (INI) inhibition of value signals in the ventral tegmental area related to sweet food liking as well as, independently, by peripheral insulin sensitivity. At follow-up, both INI inhibition of hedonic value signals in the nucleus accumbens and peripheral insulin sensitivity improved with weight loss. These data highlight the critical role of central insulin function in mesolimbic systems for weight management in humans and directly demonstrate that neural insulin function can be improved by weight loss even in older age, which may be essential for preventing metabolic disorders in later life.

Editor's evaluation

This is a strong translationally relevant study on the importance of insulin and the mesolimbic response to feeding and attempts at weight loss. It will be of great interest to not only neuroscientists but those who study metabolism and nutrition.

https://doi.org/10.7554/eLife.76835.sa0

Introduction

The prevalence of overweight and obesity rises dramatically with age and is associated with increased morbidity and reduced quality of life (Kalyani et al., 2017). Hyperinsulinemia and insulin resistance are potential causes and consequences of obesity. Both are negatively affected by age and both play a pivotal role for the development of type 2 diabetes and other age-related diseases (Palmer and Kirkland, 2016). The mechanisms and directions of these interactions are under debate. For instance, it has long been assumed that insulin resistance in aging precedes the development of hyperinsulinemia, while recent data suggest a reverse direction (Janssen, 2021). Moreover, there is evidence from animal and human research of an weight-independent effect of aging on insulin sensitivity (Ehrhardt et al., 2019; Petersen et al., 2003).

Besides the importance of peripheral insulin for the glycemic control in the body, recent findings also highlight the role of insulin action in the brain for the metabolic and hedonic control of food intake (Kullmann et al., 2020a). Findings in rodents and humans indicate that, apart from signaling in hypothalamic neurocircuits regulating energy homeostasis, central insulin mediates non-homeostatic feeding for pleasure by signaling within mesolimbic reward circuits (Davis et al., 2010; Murray et al., 2014; Tiedemann et al., 2017). Specifically, insulin action in the ventral tegmental area (VTA) reduces hedonic feeding in rodents (Labouèbe et al., 2013; Mebel et al., 2012) and decreases hedonic value signals in the VTA and nucleus accumbens (NAc) in lean subjects (Tiedemann et al., 2017). On the other hand, insulin-mediated long-term depression of VTA dopamine neurons is reduced in hyperinsulinemia (Liu et al., 2013) and aberrant insulin action in VTA–NAc pathways has been observed in insulin-resistant participants (Tiedemann et al., 2017).

There is consensus that the improvement of hyperinsulinemia and insulin resistance, as achieved by caloric restriction (CR), is key in the prevention and treatment of obesity, type 2 diabetes, or cardiovascular diseases in aging (Janssen, 2021; Ryan, 2000). Evidence from human studies for such effects, however, are sparse, especially when it comes to central nervous insulin signaling. High insulin sensitivity in MEG theta activity was predictive for long-term weight management in 15 young adults (Kullmann et al., 2020b) suggesting the critical role of central insulin action for future feeding regulation and as a major target of treatment intervention. Whether such intervention can modulate brain insulin sensitivity in older age is particularly questionable given that changes in function and distribution of adipose tissue can trigger metabolic alterations such as hyperinsulinemia (Palmer and Kirkland, 2016; Tchkonia et al., 2010) and relevant brain circuits for central insulin action like the dopaminergic mesolimbic pathway undergo age-related changes (Karrer et al., 2017).

In the current longitudinal study, we investigated the role of peripheral and central insulin resistance regarding their predictive value for dietary success in older adults and whether both can be modified by weight changes. Fifty older (>55 years) overweight, non-diabetic individuals were randomly assigned to a 3-month, CR intervention or an active waiting group (WG). Before and after the intervention, overnight fasted participants took part in a crossover, placebo-controlled, double-blind pharmacological fMRI examination in which they rated the palatability of high and low sugar food pictures and the attractiveness of non-food items (control) after receiving intranasal insulin (INI) or placebo. Fasting c-peptides and blood glucose were assessed to calculate peripheral insulin sensitivity. We tested several predictions: (1) successful weight loss can be predicted by peripheral and central insulin sensitivity, the latter indicated by an insulinergic inhibition of mesolimbic responses to hedonic food stimuli at baseline (Tiedemann et al., 2017), and (2) both, peripheral and central insulin sensitivity improve with successful weight loss at follow-up. Moreover, we explored the common and distinct impact of both markers on weight (changes) in older age.

Results

Fifty overweight and obese older adults (age: 63.7 ± 5.9 years, range 55–78 years; body mass index [BMI]: 32.7 ± 4.3 kg/m², range 25.8–32.4 kg/m²; 20 men) with an explicit wish to lose weight participated in this study. Of these, 30 randomly selected participants underwent a 3-month CR diet (diet group, DG, 14 men), while 20 participants were randomly assigned to a 3-month active WG (6 men). Before (T0) and after (T1) the intervention phase, we assessed anthropometrics and blood measures. Normal HbA1C values confirm the exclusion of manifest diabetes in overweight and obese participants who are at risk for T2D but in whom elevated insulin release may still compensate for reduced insulin sensitivity (mean HOMA-2: 2.2 ± 0.08, range 1.2–3.5). All participants underwent a double-blind, randomized, placebo-controlled fMRI paradigm on food and non-food liking combined with an INI application before and after the intervention phase (Figure 1). Thus, each participant attended a total of four scanning sessions. This longitudinal, within-subject design allowed us to evaluate peripheral and food-related central insulin action linked to overweight and weight loss in older adults.

Outline of the study design and experimental task.

(a) Timeline of the longitudinal design. Each participant attended four fMRI sessions, two each before and after the 3-month intervention interval. (b) Protocol of the experimental MRI sessions. (c) Timing of the fMRI paradigm. Example of a high sugar food trial. (d) Examples of low-sweet food and non-food items.

CR significantly reduces weight in dieters

Glucose, insulin, and c-peptide levels were assessed on all four study days in the morning after an overnight fast of at least 10 hr. Fasting glucose levels confirmed fasting state in all participants. Groups were well balanced regarding gender, age, overnight fasting times, and days between sessions (all p > 0.28). At T0, BMI, weight, bodyfat, waist, and HOMA-2 did not differ between groups (all p > 0.06, Table 1).

Table 1
Sample characteristics at baseline and follow-up.
DG (N = 30)WG (N = 20)
T0T1p timeT0T1p timep time × group
BMI (kg/m²)32.1 (0.7)30.8 (0.6)***32.8 (1.1)32.8 (1.2)N.S.***
Waist (cm)103.7 (1.8)98.1 (1.9)***103.1 (2.7)99.7 (2.8)N.S.N.S.
Bodyfat37.4 (1.3)36.7 (1.4)N.S.39.5 (1.6)39.0 (1.7)N.S.N.S.
Blood
 HOMA-22.1 (0.1)1.9 (0.1)*2.3 (0.2)2.3 (0.1)N.S.N.S.
 Glucose (mmol/l)5.5 (0.1)5.4 (0.1)N.S.15.7 (0.1)5.8 (0.1)N.S.1N.S.²
 Insulin (pmol/l)78.8 (4.0)70.3 (4.4)+95.5 (8.7)94.2 (7.7)N.S.N.S.
 C-peptide (nmol/l)0.9 (0.03)0.8 (0.04)*1.1 (0.06)1.0 (0.05)N.S.N.S.
 HbA1C5.4 (0.03)5.4 (0.04)N.S.5.5 (0.07)5.5 (0.07)N.S.N.S.
  1. ***p < 0.001, *p < 0.05, +p < 0.10, s.e.m. in parantheses

  2. DG, diet group; WG, waiting group; PL, placebo; IN, insulin; T0, baseline; T1, follow-up; 1 Wilcoxon-rank Test; ² Mann-Whitney-U-Test.

  3. BMI, body mass index; HOMA-2, c-peptide-based Homeostatic Model Assessment for Insulin Resistance; N.S, not significant.

After 3 months (mean 96 ± 10 days, no group differences: p = 0.91), follow-up measurements showed a significant mean weight loss compared to baseline of 3.61 kg (±3.06, T(29) = 6.47, p < 0.001, d = 1.18) in the DG, reflecting on average a 4% loss of baseline bodyweight and BMI, respectively (T(29) = 6.66, p < 0.001, d = 1.22). Twenty-one dieters (70%) lost more than 2 kg (range: 2.5–9.6 kg), only one dieter gained more than 0.4 kg (3.1 kg). In the WG, mean weight change was 0.07 kg (±1.5 kg). Sixteen participants of the WG were able to maintain their weight ±2 kg. Two gained weight (2.3 and 2.7 kg), two lost weight of 2.3 and 4 kg, respectively. Accordingly, percentage BMI change differed significantly between groups (BMI%change: both T(48) = 5.45, p < 0.001, d = 1.39; Figure 2, Appendix 1—figure 1).

Percentage body mass index (BMI) change after 3 months in the diet (N = 30) and the waiting (N = 20) group.

Violin plots show individual data, median, interquartile range, and 1.5× interquartile range.

Baseline peripheral insulin sensitivity predicts dietary success

Fasted serum c-peptide and plasma glucose levels were used for the calculation of an effective measure of peripheral insulin resistance (HOMA-2, Levy et al., 1998; https://www.dtu.ox.ac.uk/homacalculator/index.php). To test the predictive value of baseline insulin resistance in the periphery for dietary success after 3 months, we correlated individual HOMA-2 scores assessed on the placebo session from T0 with BMI%change in dieters and found a significant correlation (r = −0.37; p = 0.046, n = 30, Pearson’s correlation). That is, higher insulin sensitivity at baseline predicted more subsequent weight loss in dieters (Figure 3a). Control analyses showed no such correlation in the WG and no correlation was observed between BMI%change and baseline BMI (all p > 0.16).

Predictors of subsequent weight loss in dieters.

(a) Higher peripheral insulin sensitivity measured via Homa-2 scores at baseline (T0) was related to higher percentage body mass index (BMI) reduction in dieters (N = 30) at follow-up (T1). (b) Correlation between insulin effects in the ventral tegmental area (VTA) at baseline and subsequent weight loss in dieters. Individual blood oxygenation level-dependent (BOLD) signals were extracted from the peak voxel in the VTA resulting from the parametric contrast PLHS>LS > INHS>LS within dieters, p < 0.05 FWE corrected for bilateral VTA mask.

Insulinergic inhibition of sweet food liking at baseline predicts dietary success

At T0, after an overnight fast of at least 10 hr (day 1: 12.5 ± 1.6 hr; day 2: 12.2 ± 1.6 hr, no group differences), all participants underwent a 2-day fMRI scanning procedure, separated by at least 1 week (9.0 ± 3.4 days) that was combined with 160 IU INI or placebo in a double-blind, randomized crossover design (Figure 1). Fasting time and hunger ratings did not differ between groups (all p > 0.32; Appendix 1—table 1).

In the scanner, participants were asked to rate the overall preference for high (HS) and low sugar (LS) food and non-food items with yes (~‘I like this’) or no (~‘I do not like this’) by button press, which was followed by a four-point rating scale where they were asked to provide a detailed rating, indicating how much they liked or disliked each item. Stimuli were presented in pseudo-randomized order. Parametric values were derived from transferring the general and the four-point rating into a single scale ranging from 1 (‘not at all’) to 8 (‘very much’) (validation study of all four sets, Appendix 1—table 2).

Placebo and insulin sessions did not differ across individuals with respect to prescan insulin, c-peptide (all p > 0.30, n = 50, t-test), glucose, hunger ratings, and time fasted (all p > 0.24, n = 50, Wilcoxon test), nor were there any group × session differences in these parameters (all p > 0.11, nDG = 30, nWG = 20, t-test, Mann–Whitney U-test). Similarly, changes in pre- compared with post-hunger ratings, as well as levels of glucose, did not differ between the placebo and the insulin session across and between groups (all p > 0.32; Wilcoxon test, Mann–Whitney U-test). As expected, plasma insulin levels across all participants decreased over time (F(1,48) = 38.79, p < 0.001, ƞ² = 0.45, repeated measures analysis of variance [rmANOVA]); there was a lower decrease at the insulin day across participants (F(1,48) = 4.08, p = 0.049, ƞ² = 0.08, rmANOVA) but not within single groups or as group interaction (p > 0.83; Appendix 1—table 1).

As expected, in the T0 placebo session, food items were liked significantly more than non-food items on the categorical (yes/no) and parametric (cumulated ratings 1–8) level (all p < 0.001). Preference values for HS and LS food did not differ across or between groups (all p > 0.15). C-peptide-based insulin sensitivity was correlated with HS liking, in a way that higher insulin sensitivity was related to lower HS liking (r = 0.38; p = 0.006; n = 50, Pearson’s correlation, Appendix 1—figure 2). There was no relationship of HOMA-2 scores with LS liking (p > 0.16).

We then investigated the effects of INI on preference values at T0. Analyses across and between groups yielded no significant differences between the placebo and the insulin session, neither for food > non-food nor for HS > LS food items (all p > 0.14, rmANOVA). There was no interaction between insulin effects and insulin sensitivity as assessed by HOMA-2. To investigate the predictive value of individual differences in insulin effects on future weight changes we added BMI%change as a covariate into the analyses (rmANCOVA). While there was no interaction with insulin effects on general food versus non-food values, analysis on sugar-specific values (HS > LS) demonstrated a significant two-way interaction session × BMI%change (F(1,48) = 6.24; p = 0.016, ƞ² = 0.12, rmANCOVA). To further explore this finding, the analysis of insulin effects was limited to participants with a minimum BMI reduction of 1% (n = 25). In this subsample, insulin decreased sugar preference (i.e., percentage of sweet foods in preferred foods) at baseline significantly (T(24) = 2.10; p = 0.046, n = 25, d = 0.42, t-test) and this effect could not be explained by BMI or HOMA-2 (all p > 0.12).

Midbrain insulin effects during sugar liking predict weight loss in dieters

To examine the neural mechanisms of how insulin influenced the brain’s mesocorticolimbic reward circuitry, we analyzed blood oxygenation level-dependent (BOLD) activity measured during the preference task using a two-level random effects model. As expected from our previous study (Tiedemann et al., 2017), the analysis of differences in BOLD responses to food compared to non-food items in the placebo session at T0 yielded highly significant activations across all participants in a network of reward-related brain regions including the bilateral insula, medial OFC, and amygdala (Figure 4a). Also in line with our previous findings, regions that encode the subjective value of items, that is, regions that show a positive correlation between the amplitude of the BOLD response and subjective preference values, comprised regions of the brain’s valuation network including the vmPFC and NAc (Figure 4b). BOLD signals in these regions did not differ between groups. Furthermore, valuation of HS compared to LS food items evoked significantly stronger correlations between BOLD signal and preference values in the ACC/vmPFC (Figure 4c), the right caudate nucleus and thalamus (all p < 0.05 FWE corrected) and as trend in the right NAc (9, 10, −7; FWE = 0.09, Figure 4c).

Paradigm-induced activation patterns during baseline placebo.

(a) Categorical effect of food stimulus presentation. Greater activity in the insula, amygdala, and orbitofrontal cortex was observed in the food compared to the non-food condition across both groups (N = 50; included contrast images: food > non-food). (b) Neural representation of preference values (parametric analysis). Regions in which the correlation with preference values was significant across participants included the ventromedial prefrontal cortex (vmPFC) and the bilateral nucleus accumbens (NAc) (included contrast images: all food × liking). (c) Sugar-specific blood oxygenation level-dependent (BOLD) signals in the vmPFC and right NAc. Bar plots show means and standard error of the mean (SEM) of contrast estimates extracted from peak voxels from the comparison HS > LS (included contrast images: HS × liking > LS × liking). All peaks and displayed p values are p < 0.05 FWE corrected. Activations are overlaid on a custom template (display threshold p < 0.005 uncorrected).

We then investigated the effects of INI on these value signals. Here, in line with behavioral findings, analyses across and between groups yielded no significant changes of neural value signals for both general food items and HS versus LS items (see uncorrected results in Appendix 1—figure 3). There was also no relation between HOMA-2 and neural insulin effects across individuals or within the DG. Following up on the behavioral findings, we next analyzed whether individual insulin effects on neural signals during HS compared to LS food valuation predicted subsequent weight loss in the DG. Simple regression analysis including BMI%change as a covariate yielded insulin-induced signal changes in the left VTA to predict subsequent weight loss across all participants as well as within subjects from the DG alone (−8, −13, −13, p < 0.05 FWE corrected; Figure 3b). Results in the left VTA were still significant when including age and BMI as covariates in the analysis (p < 0.05 FWE corrected). This indicates that participants in whom INI reduced the HS-specific valuation signal in the midbrain at T0 are more likely to benefit from CR by weight loss as assessed at T1.

Baseline central and peripheral insulin sensitivity make independent contributions to the prediction of dietary success

To assess the incremental predictive value of baseline peripheral and central insulin sensitivity for weight changes after 3 months of intervention, we then performed a multiple regression analysis using HOMA-2 scores and the extracted BOLD signal from the contrast PLHS>LS > INHS>LS in the VTA to predict BMI%change. Within participants from the DG, this model turned out to be highly significant (F(2,27) = 10.07; adjusted R² = 0.39; p < 0.001), with both predictor variables explaining substantial variability (VTA-Bold: β = 0.54; T = 3.70; p < 0.001; HOMA-2: β = −0.35; T = 2.41; p = 0.023, p < 0.025 Bonferroni corrected). Inclusion of BMI in a control model demonstrate these effects not to be driven by baseline bodyweight (Appendix 1—table 3). This indicates, that peripheral and central insulin sensitivity at baseline have an independent positive impact on subsequent weight loss in overweight older dieters.

Improvement of peripheral insulin sensitivity is related to increased insulin effects on NAc HS value signals after weight loss

We finally investigated metabolic and neurobehavioral changes due to successful weight loss. Within participants from the DG, HOMA-2 scores were significantly improved at follow-up (T(29) = 2.33; p = 0.027, d = 0.43). Moreover, the improvement in HOMA-2 scores was directly correlated with successful weight change within dieters (r = 0.43; p = 0.017; N = 30, Pearson’s correlation) and across all participants (r = 0.33; p = 0.020; N = 50, Pearson’s correlation, Figure 5).

Weight loss is related to improvements in peripheral insulin sensitivity.

Within dieters (N = 30) and across participants (N = 50) a higher percentage of body mass index (BMI) changes was correlated with an increase of insulin sensitivity as measured via the Homa-2 scores.

We next tested whether successful weight loss also improved central insulin sensitivity as assessed with our pharmacological fMRI design (for characteristics of the T1 fMRI sessions see Appendix 1—table 1, Appendix 1—table 4). In behavior, participants from the DG showed a significantly reduced sweet food preference (i.e., percentage of sweet foods in preferred foods) under insulin compared to placebo at follow-up (T(29) = 2.59; p = 0.015, d = 0.47) that tended to be stronger compared to the WG (T(49) = 1.80; p = 0.08) and to baseline (T(29) = 1.67; p = 0.11) (Figure 6a).

Central insulin effects on behavior and brain activity before and after 3 months.

(a) Behavioral insulin effects on sweet food preference. While there was no insulin effect observed at baseline T0 in both groups, the percentage of preferred sweet food items decreased significantly under insulin compared to placebo at follow-up in dieters. (b) General linear modeling of sweet versus non-sweet value signals under insulin compared to placebo revealed a significantly stronger signal decrease in the diet group (N = 30) compared to the waiting group (N = 20) at follow-up (T1) compared to baseline (T0) in the right nucleus accumbens (NAc) included contrast images: T1 [PLHS>LS > INHS>LS] > T0 [PLHS>LS > INHS>LS]. Bar plots show group means and standard error of the mean (SEM) of mean contrast estimates extracted from significant peak voxel. p < 0.05 FWE corrected for bilateral NAc mask. Activations are overlaid on a custom template (display threshold p < 0.005 uncorrected).

On the neural level, behavioral insulin effects in the DG at follow-up were reflected by a stronger reduction of sugar-specific value signals in the NAc under insulin in the DG compared to the WG (peak right: 10, 8, −7, p = 0.028 FWE corrected and peak left: −10, 12, −8, p = 0.043 FWE corrected, t-test). The effect in the right NAc was also significantly stronger when directly comparing follow-up to baseline valuation responses between groups (peak: 10, 8, −6, p = 0.019 FWE corrected; two-sample t-test). Exploration of extracted BOLD signals (Figure 6b) indicate that this effect was at least partly driven by an opposite effect in the WG, that is, sweet food signals in the NAc relatively increased under insulin at T1. There was no significant insulin effect across all participants at T1 (see uncorrected results in Figure 3b).

We therefore more directly focused on the DG and explored whether insulin-mediated neural signal changes after weight loss were related to changes in peripheral insulin sensitivity following dietary intervention. To this end, changes in HOMA-2 scores (post > pre) were entered as a covariate in a simple regression of BOLD signals from the contrast HS > LSPL>IN_post > HS > LSPL>IN_pre within participants from the DG. Results revealed positive correlations between improvement of insulin sensitivity measured in the blood and increased insulin effects in the NAc (peak left: −10, 10, −7, p = 0.03 FWE corrected; peak right: 10, 10, −7; Figure 7). No significant brain correlates for this analysis were found in the WG. Results in the left NAc were still significant when including age and BMI as covariates in the analysis.

Interaction between peripheral and central insulin changes in dieters (N = 30).

Improved (T1 > T0) insulinergic inhibition of sweet food value signals in the left nucleus accumbens (NAc) correlated with improved peripheral insulin sensitivity. Individual blood oxygenation level-dependent (BOLD) signals were extracted from the peak voxel in the right NAc resulting from simple regression analysis including the parametric contrast HS > LSPL>IN_post > HS > LSPL>IN_pre and HOMA-2 changes (post > pre) as covariate of interest. p < 0.05 FWE corrected for bilateral NAc mask. Activations are overlaid on a custom template (display threshold p < 0.005 uncorrected).

Discussion

Our findings indicate an independent predictive value of peripheral and central insulin sensitivity for dietary success in overweight elderlies and an improvement of both after losing weight. In non-diabetic, overweight, and obese older participants who underwent a 3-month CR, significant weight loss could be predicted by baseline measures of c-peptide-based insulin sensitivity as well as acute insulinergic inhibition of VTA responses to high sugar food items. Both markers of insulin function made an independent contribution to weight loss prediction emphasizing the necessity to take both aspects into account when assessing predictors and consequences of overweight in the elderly. At follow-up, weight loss in dieters was associated with improved peripheral insulin sensitivity which was directly related to a stronger insulinergic inhibition in the NAc during hedonic food valuation. These findings extend work in rodents (Mebel et al., 2012) and first studies in humans (Kullmann et al., 2020b) about the critical role of insulin sensitivity for future feeding regulation. It is also, to our knowledge, the first study that demonstrates positive effects of CR on central insulin function in humans using a longitudinal within-subject design. The observation of such an effect in older adults is particularly important given age-related metabolic and neural changes and the potential, detrimental consequences of hyperinsulinemia, overweight, and obesity in later life (Janssen, 2021).

As was expected from overweight and obese individuals, both peripheral and central markers of insulin sensitivity were low at baseline and both selectively improved with weight loss after CR in dieters. At baseline, participants with higher hyperinsulinemia demonstrated a specifically enhanced sugar preference which fits animal and human data on the critical role of sugar-enriched diets on whole-body insulin functioning (Macdonald, 2016). Baseline insulin markers in the blood, however, were not related to behavioral or neural INI responses to HS items. This may be driven by the general lack of central insulin effects across participants at baseline which fits with data in overweight younger adults (Tiedemann et al., 2017) and which might result from an attenuated insulin transport into the cerebrospinal fluid in individuals with reduced whole-body insulin sensitivity (Heni et al., 2014). However, there was also no such association in the subgroup of successful dieters in whom there was a significant response to INI already before the intervention. This indicates that, even though peripheral insulin might be a good proxy for central insulin functioning in younger lean adults (Tiedemann et al., 2017), pathophysiological changes due to overweight and aging might at least in part independently affect peripheral and central insulin effects. This is further underlined by the independent predictive value of both markers for weight changes after 3 months of CR. The impact of these predictive values could not be explained by body mass, which demonstrates that insulin sensitivity, but not necessarily obesity, is predictive for future weight management. Indeed, there is evidence from animal and human research of an adiposity-independent effect of aging on insulin sensitivity (Ehrhardt et al., 2019; Petersen et al., 2003), that, for instance, may result from age-related changes in mitochondrial energy metabolism (Petersen et al., 2003) or increased systemic inflammation (Ehrhardt et al., 2019).

Inhibitory INI effects on VTA value signals to sweet food items were selectively predictive for subsequent success of CR. The VTA plays a central role in the insulinergic modulation of hedonic eating behavior (Labouèbe et al., 2013; Mebel et al., 2012; Tiedemann et al., 2017). Direct administration of insulin into the VTA reduces hedonic feeding and depresses somatodendritic DA in the VTA which has been attributed to the upregulation of the number or function of DA transporter in the VTA (Mebel et al., 2012). Connectivity analyses of fMRI data further suggest that INI can suppress food value signals in the mesolimbic pathway by negatively modulating projections from the VTA to the NAc (Tiedemann et al., 2017). Insulinergic effects at baseline and follow-up were specifically restricted to HS food stimuli. The palatability of sugar has been linked to DA release in the NAc in rodents (Hajnal et al., 2004) and there is evidence for neural adaptations in the NAc in response to excessive sugar intake (Klenowski et al., 2016). For instance, higher sugar preference in overweight individuals has been related to stronger white matter connectivity within the VTA–NAc pathway (Francke et al., 2019). Our data indicate that insulinergic functionality in this network may be critical for hedonic feeding regulation as the reduction of sugar intake is substantial for the success of a dietary intervention.

A reduced insulinergic functionality of this network in older overweight individuals may not only be the consequence of adiposity (Mattson and Arumugam, 2018) but may also result from age-related metabolic and neural changes. Aging is associated with a decrease of cortical insulin concentration, reduced insulin receptor binding ability and reduced insulin transport across the blood–brain barrier (Cholerton et al., 2011). Moreover, target systems of metabolic–hedonic networks relevant for insulin action undergo age-related changes (Mattson and Arumugam, 2018; Smith et al., 2020). The dopamine system, for example, is particularly vulnerable to aging which might lead to functional changes in subcortical reward circuits (Dreher et al., 2008; Karrer et al., 2017). There is a significant loss of dopaminergic neurons in the basal ganglia including the VTA (Siddiqi et al., 1999). Given that insulin acts via glutamatergic synaptic transmission onto VTA DA neurons (Labouèbe et al., 2013) this might have direct consequences on the insulinergic suppression of subsequent DA release in mesolimbic regions. The potential negative impact of adiposity and age on described dysfunctions are thereby probably not simply additive. For instance, chronic metabolic morbidities like obesity can further accelerate brain aging (Mattson and Arumugam, 2018). A chronic positive energy balance thereby adversely affect brain function (Beyer et al., 2017) and structure (Janowitz et al., 2015) and is related to many of the cellular and molecular hallmarks of brain aging such as oxidative damage and neuroinflammation (Mattson and Arumugam, 2018).

Intriguingly, while there was no association between blood parameters and central insulin action at baseline, weight-change related improvements in peripheral and central insulin sensitivity in our sample of older dieters were directly correlated at follow-up, indicating a common modulator. Moreover, changes in central insulin sensitivity were restricted to an increased inhibition of value signals in the NAc but not the VTA. Thus, one could speculate that weight change specifically normalized adiposity-related dysfunctions while variability due to aging itself were less affected. Improvement in insulin sensitivity and glucose homeostasis is a broadly observed metabolic effect of CR in rodents (Yu et al., 2019; Zhang et al., 2021) as well as young and older adults (Fontana and Klein, 2007; Johnson et al., 2016; Most and Redman, 2020; Most et al., 2017). The mechanisms behind these effects are not fully understood yet but have been related to significantly increased hepatic insulin clearance (Bosello et al., 1990), reduced levels of thioredoxin-interacting protein (TXNIP; Johnson et al., 2016), and generally decreased oxidative stress and inflammatory processes (Fontana and Klein, 2007). Animal data about CR effects on brain functioning suggest that CR can induce adaptive cellular responses that can enhance neuroplasticity and stress resistance, for example, by the upregulation of neurotrophic factor signaling, suppression of oxidative stress and inflammation, stabilization of neuronal calcium homeostasis, and stimulation of mitochondrial biogenesis (Mattson, 2012; Mattson and Arumugam, 2018). In addition, recent work in rodents demonstrate improved insulin sensitivity following CR that was associated with enhanced brain monoamine concentrations such as increased DA levels in the striatum (Portero-Tresserra et al., 2020). Our data extend these beneficial neural effects of CR in animals to improved central insulin functioning in the human brain. This is particularly intriguing with regard to our non-diabetic sample of elderlies, in whom weight-related brain dysfunction is not only a risk factor for metabolic disorders but also for cognitive decline and neurodegeneration (Ekblad et al., 2017; Janssen, 2021; Mattson and Arumugam, 2018).

We chose a relatively mild dietary intervention that reduced participants’ individual caloric intake by 10–15% with a minimal intake set to 1200 kcal per day. This was done to increase compliance and to provide elderlies with a feasible long-term strategy to lose and maintain weight. Accordingly, there was only a mild-to-moderate average weight loss of 4%. Even this mild weight change was related to significant improvement of insulin sensitivity in the periphery and in the brain which underline that adiposity-related dysfunctions in later life are able to normalize. This is especially promising given new evidence for hyperinsulinemia preceding insulin resistance (Janssen, 2021) which makes it a key target for early interventions. It is now critical to understand the long-term effects of such changes with a special focus on food intake assuming that long-term effects are probably particularly dependent on prefrontal mediated psychological strategies including self-control during eating decisions (Hare et al., 2009; Phelan et al., 2020). In conclusion, we provide data demonstrating that peripheral insulin sensitivity as well as central hedonic feeding regulation predict and normalize with dietary success in overweight elderlies. Our results of an independent contribution peripheral and central insulin sensitivity make for successful feeding regulation emphasize the necessity to control for both when treating individuals at risk for metabolic disorders.

Materials and methods

Participants

Sixty-four overweight and obese participants (age >55, BMI >25 kg/m²) with an explicit wish to lose weight were recruited for this study. Thirty-eight participants were randomized to the dietary intervention group while 26 were randomly assigned to the WG. Randomization was based on a predefined randomization list (allocation scheme 60:40) and was applied consecutively. Out of these 38 participants from the DG, two did not come back for the follow-up measurement, three individuals showed elevated glucose levels (cutoff ≥126) before at least one scanning session indicating they were not fasted, two showed incomplete task understanding (i.e., always pressed the same button), and one participant had to be excluded due to massive movement artifacts in the scanner. Out of the initial 26 members of the WG, two did not show up for the follow-up measurement, three had substantially increased insulin levels, and from one participant no task behavior could be recorded in the T1 insulin session due to technical issues. This led to a final sample size of 50 complete datasets (55–78 years, M = 63.7, standard error [SD] = 5.9, 20 men), 30 derived from the DG and 20 derived from the WG. Mean BMI was 32.4 kg/m² (25.9–43.6, SD = 4.3). Sample characteristics are summarized in Table 1.

Sample sizes were based on previous findings on successful CR in older adults (Witte et al., 2009). A dropout rate of 25% was considered in our recruitment scheme. Sensitivity measures derived from G*Power 3.1.9 for the final sample sizes indicate our design to be sensitive to detect small (N = 50) to medium (N = 30) effects in one-sample and paired t-tests and large effects in two-sample t-tests (N = 30, N = 20) given an α of 0.05 and β of 0.80.

Participants were recruited via newspapers and online announcements. Exclusion criteria were current or previous psychiatric or neurological disorders, chronic and acute physical illness including diabetes, current psychopharmacological medication as well as MR-specific exclusion criteria. Initial screening as well as all clinical measurements in this study were performed by a physician (P.F. and K.G.). No participant followed any specific diet at the start of the experiment. To exclude systematic confounds during food evaluation, severe food allergies and adherence to a vegan diet constituted further exclusion criteria. All participants had normal or corrected-to-normal visual acuity. The study was approved by the local ethics committee of the Ethical Board, Hamburg, Germany. All participants gave informed consent and were financially compensated for their participation. Additional financial incentives (50€) were provided to participants from the DG for successful weight loss (≥4 kg) and to participants from the WG for keeping their weight stable (weight changes ≤2 kg). The whole study was conducted at the Department of Systems Neuroscience, University Medical Centre Hamburg-Eppendorf. The study has been registered at DRKS (DRKS00028576).

Experimental protocol

Baseline (T0)

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After successful screening, participants attended two experimental sessions, separated by at least 1 week. On each day, participants arrived in the morning between 7:30 and 10:30 hr after an overnight fast of at least 10 hr. After anthropometric measurements, ratings of feelings of current hunger and collection of blood samples, participants received 160 IU of insulin (Insuman Rapid, 100 IU/ml) or vehicle (0.27% m-Kresol, 1.6% glycerol, and 98.13% water) by intranasal application. Participants received eight puffs per nostril, each puff consisting of 0.1 ml solution containing 10 IU human insulin or 0.1 ml placebo. The order of insulin and placebo was randomized and balanced, and the application was double blind. Before scanning, participants were familiarized with the task during a training session. Participants began the preference paradigm (in the fMRI scanner) 30 min after the nasal spray was applied; this delay was introduced to ensure that the insulin had time to take its full effect (Born et al., 2002). After completion of the scans, participants again rated their feeling of hunger and a second set of blood samples was collected (Figure 1).

Intervention

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Directly following the second scanning session, participants were randomly assigned to either the DG or the WG following a 60:40 randomization scheme. Participants of the DG received a 12-week professional diet program that consisted of (1) individual nutrition counseling by experienced clinical dieticians, who were blinded to the underlying study hypothesis, and (2) a psychological group intervention (Appendix 1—figure 4).

Within individual sessions and based on individual dietary records from the previous 7 days, dieters received an individually planned dietary regimen that reduced each subject’s individual caloric intake by 10–15%. Minimal intake was set to 1200 kcal per day. The regimen was based on the 10 guidelines of the German Nutrition Society (https://www.dge.de/). Compliance was assured by three telephone contacts after 2, 6, and 12 weeks during which participants confirmed that they continued to adhere to the diet and had the opportunity to clarify any questions regarding the dietary regimen. After 2 weeks, participants additionally attended a 90-min group session consisting of psychoeducation regarding obesity and T2D, a mindfulness-based eating awareness training and a training of self-monitoring and -control strategies (e.g., use of goal-related eating protocols). In a final counseling, the dietary regimen was reviewed and future eating behavior was discussed.

Participants from the WG were instructed to not change previous eating habits during the 3-month period. In week 6, they attended a 90-min group session which consisted of a psychoeducational unit about stress and stress management as well as a training of progressive muscle relaxation. After finishing all experimental sessions, participants of the WG were offered gratis dietary counseling identical to the one offered to the DG.

Follow-up (T1)

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Three months after the last baseline measurement, participants from both groups repeated the double-blind randomized crossover design from T0, that is they attended another three study days (screening, fMRI + placebo, fMRI + insulin).

Blood measures

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On each scanning day before insulin/placebo application, blood samples were collected, containing the following: 2.7 ml blood in a sodium fluoride for analysis of blood glucose, 7.5 ml blood in a serum tube for analysis of insulin and c-peptide. After completion of MR scans blood sample collection for insulin and glucose analysis was repeated. After 10 min of centrifugation (2800 × g and room temperature), the supernatants of the blood samples were stored at −80°C until further processing. Concentrations of insulin and c-peptide were measured using an electro-chemiluminescence immunoassay (Roche, ECLIA). Blood glucose was quantified through photometry (Beckman Coulter). We used fasted c-peptide serum levels for the calculation of an effective measure of insulin resistance (HOMA-2; Levy et al., 1998, https://www.dtu.ox.ac.uk/homacalculator/index.php). A c-peptide-based index is thought to be a more reliable indicator of insulin secretion that is minimally affected by hepatic insulin clearance, has longer half-life and that is more sensitive to incident T2D (Jones and Hattersley, 2013; Leighton et al., 2017; Okura et al., 2018). Indeed, in a control analysis we observed a significantly higher within-subject variability (coefficient of variance, COV) in prescan insulin levels at T0 as well as T1 compared to c-peptide levels (T0: T(49) = 4.79; p < 0.001; T1: T(49) = 4.47; p < 0.001).

Statistical analyses

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Behavioral and metabolic data processing were conducted using MATLAB (Mathworks, MA) and SPSS 27 (IBM, NY). We report statistical tests from the general linear model framework, including one-sample t-tests, two-sample t-tests, rmANOVA, multiple regression, and Pearson’s correlations. We used the Kolmogorov–Smirnov test to test the null hypothesis that our data come from a normal distribution. In case when data were not normally distributed non-parametric testing was applied using the Wilcoxon test and the Mann–Whitney U-test. Statistical significance was assumed based on an alpha value of 0.05. Bonferroni correction was applied on multiple regression coefficients.

fMRI food-rating paradigm

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Four sets of stimuli were randomly presented on the four scanning days. Each one of the four parallel versions consisted of 70 food and 70 non-food color images selected from the internet. All pictures had a size of 400 × 400 pixels and were presented on a white background. Food pictures featured both sweet and savory items. Pictures were specifically selected to cover common high- and low-palatable foods. The four food sets did not differ in sugar content (see Appendix 1—figure 5 for distribution of sugar content). Non-food pictures, such as trinkets and accessories were chosen to evoke similar degrees of attractiveness. Validation of all four sets was conducted in an independent sample (n = 16) and revealed that the four sets did not differ significantly regarding the mean preference ratings (all p > 0.43). Importantly, preference ratings as well as picture saliency for HS and LS stimuli did not differ between sets (Appendix 1—table 2).

On each scanning day, food and non-food stimuli were pseudo-randomly presented (not more than three pictures from one category in a row) during three runs; each run lasted ~12 min and runs were separated by a 1-min relaxation break. Every run began with the instructions (‘We will soon start with the question: Do you like the presented item or not?’) (Figure 1).

MRI data acquisition

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All imaging data were acquired on a Siemens PRISMA 3T scanner (Erlangen, Germany) using a 32-channel head coil. Functional data were obtained using a multiband echo-planar imaging sequence. Each volume of the experimental data contained 60 slices (voxel size 1.5 × 1.5 × 1.5 mm) and was oriented 30° steeper than the anterior to posterior commissure (AC–PC) line (repetition time [TR] = 2.26 s, echo time [TE] = 30 ms, flip angle = 80°, field of view [FoV] = 225 mm, multiband mode, number of bands: 2). An additional structural image (magnetization prepared rapid acquisition gradient echo [MPRAGE]) was acquired for functional preprocessing and anatomical overlay (240 slices, voxel size 1 × 1 × 1 mm).

fMRI data analysis

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Structural and functional data were analyzed using SPM12 (Welcome Department of Cognitive Neurology, London, UK) and custom scripts in MATLAB. All functional volumes were corrected for rigid body motion and susceptibility artifacts (realign and unwarp). The individual structural T1 image was coregistered to the mean functional image generated during realignment. Image diagnostics was performed using visual inspection of image-to-image variability (tsdiffana, https://imaging.mrc-cbu.cam.ac.uk/imaging/DataDiagnostics). The functional images were spatially normalized and smoothed with a 4-mm full-width at half maximum isotropic Gaussian kernel.

A two-level random effects approach utilizing the general linear model as implemented in SPM12 was used for statistical analyses. At the single subject level, onsets of HS, LS, and non-food stimuli presentation were modeled as separate regressors convolving delta functions with a canonical hemodynamic response function. In addition, combined rating scores were entered as parametric modulators of HS, LS, and non-food regressors separately. Onsets of HS and LS were defined based on median splits on sugar content (g/100 g) of the respective stimulus set. Importantly, sugar medians did not differ between sets for placebo/insulin sessions before and after the intervention (all p > 0.25; mean sugar median = 12.8 g sugar/100 g). Data from the placebo and the insulin sessions were defined as single models. In all analyses, we accounted for the expected distribution of errors in the within-subject (dependency) and the between-group factors (unequal variance).

For each subject, contrast images of interest were then entered into second-level group analyses, that is one sample and two-sample t-tests. Contrast images of interest comprised onset and parametric regressors for food >non-food and HS > LS from the placebo session at T0 (general paradigm-induced activation) and parametric regressors for HS > LS covering insulin effects at baseline (PL > IN) and compared to follow-up (T1 > T0). We report results corrected for FWE due to multiple comparisons. We conducted this correction at the peak level within small volume of interest (ROI) for which we had an a priori hypothesis or at the whole-brain level. Based on findings in our previous work using the identical pharmacological fMRI setup in younger individuals (Tiedemann et al., 2017), we focused on the NAc and the VTA. We applied the identical functional ROIs (4 mm spheres) centered on the bilateralized peak voxels in the NAc (±12, 10, −8) and the VTA (±4, −14, −12) as in this previous work and as identified via meta-analyses conducted on the neurosynth.org platform.

Appendix 1

Appendix 1—figure 1
Individual weight change in dieters and controls.

Weight change in kg after 3-month dietary intervention/waiting phase.

Appendix 1—table 1
Fasting duration before each study day and hunger ratings before each MRI scan.
WGT0pT1pp
PLIN(session)PLIN(session)(session × time)
Fasting time (hr)12.4 (0.3)12.8 (0.4)N.S.12.5 (0.3)12.6 (0.4)N.S.N.S.
Hunger rating2.5 (0.6)2.2 (0.5)N.S.2.8 (0.5)3.1 (0.7)N.S.N.S.
DGT0pT1pp
PLIN(session)PLIN(session)(session × time)
Fasting time (hr)12.3 (0.3)12.2 (0.3)N.S.12.4 (0.3)12.3 (0.3)N.S.N.S.
Hunger rating2.2 (0.4)2.3 (0.4)N.S.2.9 (0.5)2.8 (0.5)N.S.N.S.
  1. Neither did the fasting times between the last food intake and the beginning of the study day differ between sessions (PL/IN, T0/T1) or groups, nor was there a group x session effect. Before entering the scanner, participants rated their current feelings of hunger on a scale from 0 (“not hungry at all”) to 10 (“extremely hungry”). Values did not differ between sessions or groups and there was no group x session interaction. Values indicate means with s.e.m. in parentheses. DG: diet group, WG: waiting group, PL: placebo, IN = insulin, T0: baseline, T1: follow-up. N.S. not significant. 1 Because at least one measure of this variable (across all time-points) was not normally distributed, a Wilcoxon-Rank-Testing was applied.

Appendix 1—table 2
Stimuli characteristics.
Set 1Set 2Set 3Set 4p
Parametric liking score
All food items
2.86 (0.07)2.90 (0.07)2.98 (0.06)2.96 (0.06)N.S.
HS food items3.00 (0.09)2.80 (0.10)3.03 (0.08)2.87 (0.08)N.S.
LS food items2.70 (0.09)3.00 (0.10)2.91 (0.10)3.04 (0.10)N.S.
Picture saliency
All food items
0.19 (0.01)0.21 (0.01)0.18 (0.01)0.20 (0.01)N.S.
HS food items0.19 (0.01)0.21 (0.01)0.18 (0.01)0.20 (0.01)N.S.
LS food items0.20 (0.01)0.20 (0.01)0.21 (0.01)0.20 (0.01)N.S.
  1. In a validation study, an independent sample of 16 participants rated the preference of items on a scale from 1 (~ “I do not like this at all”) to 4 (~ “I like this very much”). Saliency is calculated based on the Image Signature algorithm, as described by Hou et al., 2012. One-way ANOVAs showed that the four sets did not differ in regard to the parametric liking (all P > 0.07) and saliency (all P > 0.43) scores across food items and for HS and LS food items separately. Values indicate means with s.e.m. in parentheses. N.S. not significant.

Appendix 1—figure 2
Correlation between peripheral insulin sensitivity and sweet food liking.

Lower insulin sensitivity as measured via the HOMA-2 score was related to higher sugar liking. No correlation with insulin sensitivity was found for low sugar liking (p > 0.17).

Appendix 1—figure 3
Uncorrected whole-brain response to insulin (HS > LS) at T0 and T1 across all participants.

Activations are overlaid on a custom template (display threshold p < 0.001, k = 10).

Appendix 1—table 3
Regression models for the prediction of dietary success.
Dependent variableModel (R² adjusted)Predictor variable (standardized β-coefficients)
Insulin sensitivity (HOMA-2)Insulin effects on VTA signalBMI
Model 1
BMI change (%)0.39***−0.35*c0.54***c
Model 2
BMI change (%)0.37**−0.38*0.51**c0.07
  1. *

    **p < 0.001, **p < 0.01, *p < 0.05, csignificant after Bonferroni correction.

Appendix 1—table 4
Pre–post blood values at baseline and follow-up.
DG_T0PLpINpp
PrePostPrePost(interaction)
Insulin (pmol/l)78.8 (4.0)62.8 (4.6)0.00380.6 (4.5)72.9 (6.0)0.16N.S.
Glucose (mmol/l)5.5 (0.1)5.6 (0.1)N.S.5.4 (0.1)5.4 (0.1)N.S.N.S.
DG_T1
Insulin (pmol/l)70.3 (4.4)57.7 (3.8)0.00172.9 (4.9)62.6 (4.9)0.036N.S.
Glucose (mmol/l)5.4 (0.1)5.4 (0.1)N.S.15.5 (0.1)5.3 (0.1)0.02N.S.1
WG_T0PLpINpp
PrePostPrePost(interaction)
Insulin (pmol/l)97. 9 (8.6)70.2 (8.1)0.00188.6 (7.4)70.2 (6.5)0.001N.S.
Glucose (mmol/l)5.7 (0.1)5.7 (0.1)N.S.5.7 (0.1)5.6 (0.1)N.S.N.S.
WG_T1
Insulin (pmol/l)97.3 (7.9)65.3 (6.0)0.001103.0 (8.9)80.5 (7.8)0.001N.S.
Glucose (mmol/l)5.8 (0.1)5.7 (0.1)N.S.5.9 (0.1)5.7 (0.1)0.02N.S.
  1. Blood samples were sampled after arrival and after completion of the scanning sessions (see Figure 1b). There was a significant insulin level x session interaction across participants at T0 (F(1,49) = 4.1; P = .047, rmANOVA) driven by a stronger insulin decrease in the placebo session. This effect was not significant within single groups, in interaction with groups, nor were there any significant session effects at T1 (all P > .30). Values indicate means with s.e.m. in parentheses. DG: diet group, WG: waiting group, PL: placebo, IN = insulin, T0: baseline, T1: follow-up. N.S. not significant. 1 Because that at least one measure of this variable (across all time-points) was not normally distributed, a Wilcoxon-Rank-Testing was applied.

Appendix 1—figure 4
Roadmap dietary intervention.
Appendix 1—figure 5
Distribution of sugar content in stimuli sets.

Sugar content was assessed using the food database on fddb.info and did not differ between sets (p > 0.61, analysis of variance [ANOVA]).

Data availability

All data for analyses and figures in this study are provided at Dryad.

The following data sets were generated
    1. Brassen S
    (2022) Dryad Digital Repository
    Data from: Insulin sensitivity in mesolimbic pathways predicts and improves with weight loss in older dieters.
    https://doi.org/10.5061/dryad.8cz8w9gsn

References

    1. Hajnal A
    2. Smith GP
    3. Norgren R
    (2004) Oral sucrose stimulation increases accumbens dopamine in the rat
    American Journal of Physiology. Regulatory, Integrative and Comparative Physiology 286:R31–R37.
    https://doi.org/10.1152/ajpregu.00282.2003
    1. Hou X
    2. Harel J
    3. Koch C
    (2012) Image signature: highlighting sparse salient regions
    IEEE Transactions on Pattern Analysis and Machine Intelligence 34:194–201.
    https://doi.org/10.1109/TPAMI.2011.146

Decision letter

  1. Carlos Isales
    Senior and Reviewing Editor; Medical College of Georgia at Augusta University, United States

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Insulin sensitivity in mesolimbic pathways predicts and improves with weight loss in older dieters" for consideration by eLife. Your article has been reviewed by 1 peer reviewer, and the evaluation has been overseen by a Reviewing Editor and Carlos Isales as the Senior Editor. The reviewer has opted to remain anonymous.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

Abstract

(1) Line 22: Whole body insulin sensitivity was not assessed, please write peripheral (same for line 406).

Results

(2) Line 76 ff: Please indicate the age range, BMI range, and HOMA IR range of participants. Please add the number of men and women in the DG and WG group separately. Was sex equally distributed for the intervention and control groups? Were the women in menopause?

(3) The authors present the nucleus accumbens and VTA BOLD response to insulin. As this is quite a novel study, I would recommend showing the whole brain response (uncorrected for display) for Insulin versus placebo for T0 and T1 for both groups (for sweet versus non-sweet contrast). This could be presented in the supplementary material.

Discussion

(4) Line 302: the first statement of the discussion is quite strong. I agree that the regression model shows that peripheral insulin sensitivity and central insulin response explained variance independent of one another, this is a first indication that peripheral and central responses might be independent, but the study also shows that they correlate. Furthermore, insulin resistance was only based on fasting measurements and not during a clamp or oGTT.

Methods

(5) Participants: in the abstract and in the discussion (line 392), the authors refer to their study sample as prediabetic. However, based on the description of the participants in the methods section, persons overweight and with obesity at the age of 55 and higher were recruited for the study. No official criteria for prediabetes (as described for example by the ADA: https://www.diabetes.org/diabetes/a1c/diagnosis) were employed. Please use the term non-diabetic as in other passages of the manuscript.

(6) I agree that C-peptide-based assessments of peripheral insulin resistance can be superior to insulin-based calculations. However, the CPR-IR is rarely used (only 9 citations of the original paper on that index). I suggest using the commonly used C-peptide-based HOMA-2 instead (DOI: 10.2337/diacare.21.12.2191 see: https://www.dtu.ox.ac.uk/homacalculator/index.php).

Why did the authors use plasma glucose of 128 mg/dl as a cut-off for non-fasting? A commonly used cut-off is 126 mg/dl. Another reliable way to detect non-fasted individuals is unusually high C-peptide concentrations.

(7) Line 420: "two showed task behavior indicating...". I think there is a word missing.

(8) Line 422: "substantial movement". How was this defined? This could also be indicated in paragraph 522 ff.

(9) The authors conducted a median split on sugar content to divide the stimulus set into a high sugar (HS) and low sugar (LS) condition. What was the distribution of the sugar content in the food images? Who evaluated the sugar content of the stimuli?

(10) Line 433: The exclusion criteria include certain illnesses. Was this assessed by a physician or self-reported? Please add this information.

(11) Line 440 ff: What is meant by "additional financial incentives were provided [..] for successful weight loss"? Did participants receive more money if they lost more weight? Please clarify.

(12) Line 465: How was the individual reduction by 10 to 15% calculated? Please specify. Was resting energy expenditure used? Or was it based on their daily prior consumption? The authors mention dietary records. How many days were recorded and at which time points of the study? How was compliance assured?

(13) Line 516: it is not clear what 30_ steeper means. Please clarify.

(14) Line 502: The fMRI food cue paradigm relies on images selected from the internet. Please indicate from what platform these images were selected. The images were then categorized based on sweetness. Were the pictures matched for physical and psychological variables? The authors only mention that the mean preference ratings of the stimuli were investigated in an independent sample, but other properties could play an important role (e.g. arousal, valence, and complexity of image).

(15) The fMRI data analyses section is described in detail up to the second level model.

Line 540: For the second level model, the authors indicate that they used one-sample t-tests and two-sample t-tests. Please be more specific about which contrasts (differential images) went into the model. (16) Based on the described results, I would have expected paired t-tests (T0 versus T1) or a flexible factorial model to investigate within-subject and between-subject effects in one model. Please clarify.

(16) The method section is lacking a statistical methods paragraph for the regression analysis and behavioral data analyses. What tool was used, etc.?

(17) The authors used one-way ANOVAs for analyzing parametric liking scores for the different picture sets. What is the distribution of the liking ratings? Are they normally distributed? The authors computed several rmANOVAs. Were the assumptions for such analyses tested?

BMI reduction was correlated with several measures (predictors of subsequent weight loss). In this (18) case, you must correct your p-values in relation to the number of tests (Bonferroni correction). Were the correlations between the BOLD response and peripheral insulin resistance still significant after adjusting for BMI and age?

(19) Was the study registered, for example at clinical trials.gov? Were the hypothesis and primary outcomes or study design preregistered? How was weight loss success defined?

(20) The study by Tiedmann et al., 2017 Nat Comm used the same fMRI paradigm to investigate insulin response of the reward circuitry in young adults of normal weight as well as overweight and obesity. In that study, the authors used HOMA-IR as a measure of peripheral insulin resistance. Why change this in the current study? Furthermore, in the previous study by Tiedemann, DCM model was used to show insulin-induced changes in connectivity between the nucleus accumbens and VTA. This connection was significantly modulated by intranasal insulin. Why wasn't this evaluated in the current study?

(21) The study of Tiedemann published in 2017 investigated the response of intranasal insulin in young individuals, while the current study included elderly persons. The impact of age independent of BMI has not been investigated thus far on neural insulin processing. Have you looked at the effect of age on the nucleus accumbens or VTA BOLD response to insulin?

(22) The authors use the term insulinergic inhibition. Is it possible to evaluate neural inhibition with BOLD fMRI?

https://doi.org/10.7554/eLife.76835.sa1

Author response

Essential revisions:

Abstract

(1) Line 22: Whole body insulin sensitivity was not assessed, please write peripheral (same for line 406).

This has been changed.

Results

(2) Line 76 ff: Please indicate the age range, BMI range, and HOMA IR range of participants. Please add the number of men and women in the DG and WG group separately. Was sex equally distributed for the intervention and control groups? Were the women in menopause?

We have now added age range, BMI range, and HOMA-2 (+range). Number of men and women in DG and WG were also added. We also mention that there were no sex differences between groups (chi-square = 1.39, P = .38).

We did not specifically assess menopause status by hormonal measurements. The youngest woman was 55 years old, so it is likely that most, if not all, women in this study were in the middle or postmenopausal phase.

(3) The authors present the nucleus accumbens and VTA BOLD response to insulin. As this is quite a novel study, I would recommend showing the whole brain response (uncorrected for display) for Insulin versus placebo for T0 and T1 for both groups (for sweet versus non-sweet contrast). This could be presented in the supplementary material.

We agree that sub-threshold activation patterns are helpful to inform future designs given the novelty of our study. Consistent with the reviewer’s suggestion, we have therefore included uncorrected SPMs for each time point for all participants in the appendix (Appendix Figure 3). In addition, we would like to point out that whole-brain T-maps of these and the other results are available at Dryad (https://datadryad.org/stash/share/_sUG1RvcWB3uCpSxeiYRWcKBtUZmCnzSjir4dWGv16c) which can be used, for example, to suggest neural assumptions in future studies.

Discussion

(4) Line 302: the first statement of the discussion is quite strong. I agree that the regression model shows that peripheral insulin sensitivity and central insulin response explained variance independent of one another, this is a first indication that peripheral and central responses might be independent, but the study also shows that they correlate. Furthermore, insulin resistance was only based on fasting measurements and not during a clamp or oGTT.

We have toned down this statement in accordance with the reviewer’s suggestion in “Our findings indicate…”. However, we would like to point out that the regression model indeed shows independent effects of the two parameters (based on partial correlations) and that the two variables indeed were not correlated at T0. Both aspects strongly suggest that we observed two independent predictive mechanisms in this context.

We agree that an OGTT or clamp based assessment of insulin sensitivity would have been an interesting addition to this study When we designed the study, we decided against an euglycemic clamp procedure or oGTT, mainly for logistic and compliance reasons. The latter, in particular, could have had an impact on participants’ performance in the scanner. Participants already had to fast overnight for at least 10 hours, meaning that scanning took place in the early morning. Considering that one study day already lasted 2 hours (before participants were finally allowed to eat), a preceding clamp or oGTT would have probably overstretched the physical and psychological compliance of some participants. Reassuringly, there is an extensive literature demonstrating the high validity (e.g., reported correlations of HOMA-IR with clamp assessment ~.88) of fasting measurements in non-diabetic participants (reviewed in Wallace et al., Diabetes Care, 2004).

Methods

(5) Participants: in the abstract and in the discussion (line 392), the authors refer to their study sample as prediabetic. However, based on the description of the participants in the methods section, persons overweight and with obesity at the age of 55 and higher were recruited for the study. No official criteria for prediabetes (as described for example by the ADA: https://www.diabetes.org/diabetes/a1c/diagnosis) were employed. Please use the term non-diabetic as in other passages of the manuscript.

This has been changed.

(6) I agree that C-peptide-based assessments of peripheral insulin resistance can be superior to insulin-based calculations. However, the CPR-IR is rarely used (only 9 citations of the original paper on that index). I suggest using the commonly used C-peptide-based HOMA-2 instead (DOI: 10.2337/diacare.21.12.2191 see: https://www.dtu.ox.ac.uk/homacalculator/index.php).

In accordance with this suggestion, we now report C-peptide-based HOMA-2 values. This resulted in minor changes in the results (HOMA-2 contains a non-linear transformation that altered the ranking of the T0-T1 difference values) but did not affect the main findings of the study. CPR-IR and HOMA-IR values were removed.

Why did the authors use plasma glucose of 128 mg/dl as a cut-off for non-fasting? A commonly used cut-off is 126 mg/dl. Another reliable way to detect non-fasted individuals is unusually high C-peptide concentrations.

Please excuse that this phrase was misleading. Our cut-off was actually 126, but what was meant was that all three excluded individuals had a value of > 128 (i.e., 129, 131, 134). In the remaining sample, there was no glucose value higher than 125 on any of the four study days and we have now included this information in the Methods section: Three participants were excluded because of elevated glucose levels (≥126)

(7) Line 420: "two showed task behavior indicating...". I think there is a word missing.

This has been corrected.

(8) Line 422: "substantial movement". How was this defined? This could also be indicated in paragraph 522 ff.

The following information was added to the suggested paragraph: Image diagnostics was performed using visual inspection of image-to-image variability (tsdiffana, https://imaging.mrc-cbu.cam.ac.uk/imaging/DataDiagnostics).

This visualization revealed massive distortions in one participant that matched the logged observations from the scanning sessions. The reason for these artifacts was most likely due to the pronounced head circumference of this very large participant, which prevented him from being optimally positioned in the head coil.

(9) The authors conducted a median split on sugar content to divide the stimulus set into a high sugar (HS) and low sugar (LS) condition. What was the distribution of the sugar content in the food images? Who evaluated the sugar content of the stimuli?

The sugar content in all four stimulus sets was evaluated using the food database on fddb.info. Importantly, the four parallel stimulus sets did not differ significantly w.r.t. mean and median sugar content. This is particularly important for the sets used at placebo and insulin at T0 and T1 respectively. The use of the first two sets was counterbalanced for IN/PL at T0, the third and fourth sets were counterbalanced for IN/PL at T1. Information on the distribution of the sugar content for each set has now been added to the Methods and the Appendix (Appendix Figure 5).

(10) Line 433: The exclusion criteria include certain illnesses. Was this assessed by a physician or self-reported? Please add this information.

Initial screening as well as all clinical measurements in this study were performed by a physician (P.F., K.G.). This information was added in lines 433 ff.

(11) Line 440 ff: What is meant by "additional financial incentives were provided [..] for successful weight loss"? Did participants receive more money if they lost more weight? Please clarify.

Participants received an extra bonus of 50€. We have now added this information.

(12) Line 465: How was the individual reduction by 10 to 15% calculated? Please specify. Was resting energy expenditure used? Or was it based on their daily prior consumption? The authors mention dietary records. How many days were recorded and at which time points of the study? How was compliance assured?

The individual reduction was based on participants’ daily prior consumption as assessed by dietary records. For these records, participants protocolled their daily food intake for one week (7 days).

Compliance was assured by three telephone contacts after 2, 6 and 12 weeks during which participants confirmed that they continued to adhere to the diet and had the opportunity to clarify any questions regarding the dietary regimen. We have added this information.

(13) Line 516: it is not clear what 30_ steeper means. Please clarify.

Thank you, it should be 30° steeper, and has been corrected.

(14) Line 502: The fMRI food cue paradigm relies on images selected from the internet. Please indicate from what platform these images were selected. The images were then categorized based on sweetness. Were the pictures matched for physical and psychological variables? The authors only mention that the mean preference ratings of the stimuli were investigated in an independent sample, but other properties could play an important role (e.g. arousal, valence, and complexity of image).

Stimuli were not selected from a particular platform. They were selected via a Google search from a predefined list that we had created to ensure that a similar amount of savory and sweet foods were included. Sugar in all four stimulus sets was evaluated using the food database on fddb.info. Sugar content did not differ between sets. The correspondence of “valence” between the four stimulus sets was previously validated in an independent sample of 16 participants using liking ratings. We have now made a further subdivision into HS and LS stimuli and, as a kind of approximation to “arousal” / visual complexity, we have done the same for picture saliency (line 516 ff). The data are presented in the appendix (Appendix Table 2), and show no difference between stimulus categories and the sets.

(15) The fMRI data analyses section is described in detail up to the second level model.

Line 540: For the second level model, the authors indicate that they used one-sample t-tests and two-sample t-tests. Please be more specific about which contrasts (differential images) went into the model.

The following was added to the Methods section: Contrast images of interest comprised onset and parametric regressors for food > non-food and HS > LS from the placebo session at T0 (general paradigm-induced activation) and parametric regressors for HS > LS covering insulin effects at baseline (PL > IN) and compared to follow-up (T1 > T0).

In addition, we made sure that all contrasts that went into second level summary statistics are now specified in the result section and the figure legends.

(16) Based on the described results, I would have expected paired t-tests (T0 versus T1) or a flexible factorial model to investigate within-subject and between-subject effects in one model. Please clarify.

We preferred the more conservative summary statistics (T-Tests) to factorial designs primarily for the following reasons: Mixed-effects models (i.e., with both within-subjects and between-subject factors, as in our study) require full variance partitioning. Repeated measures ANOVAs (i.e., flexible designs) in SPM provide only one variance term, in this case, within-subject variance. This means that the results of group main effects as well as individual condition main effects can be inflated and therefore require separate models (for more details on this topic, see McFarquhar: Modeling group-level repeated measurements of neuroimaging data using the univariate general linear model. Frontiers in Neuroscience, 13, 2019). Summary statistics are robust to potential misspecification, and since we were primarily interested in analyses with covariates (i.e., simple regressions), we set up all the relevant contrasts at the single subject level and submitted each of the contrast images in a separate one- or two-sample T-test which corresponds to results from an ANOVA with partitioned error terms.

(16) The method section is lacking a statistical methods paragraph for the regression analysis and behavioral data analyses. What tool was used, etc.?

Thank you, this paragraph has been added.

(17) The authors used one-way ANOVAs for analyzing parametric liking scores for the different picture sets. What is the distribution of the liking ratings? Are they normally distributed? The authors computed several rmANOVAs. Were the assumptions for such analyses tested?

There are three main assumptions for rmANOVAs:

1) Independent observations: Given for individual subjects.

2) Sphericity of all difference scores among test variables: We used only two-level within subject factors (i.e., pre vs. post, PL vs. IN, HS vs. LS), that is, testing for sphericity of all differences (e.g., using Mauchly’s Test) is not applicable. In other words, there is no possible violation of this assumption.

3) Normality: We now have tested for normality for all variables of interest both across participants and within groups using the Kolmorgorov-Smirnov-Test (line 502 ff). The main results here are:

HS and LS liking scores before and after the scan, at T0 and T1, are normally distributed across participants and within groups (all P >.098).

HOMA-2 scores at T0 and T1 and difference scores across and within groups are normally distributed (all P >.20)

We also tested all other variables included in control analyses (hunger, time fasted, glucose, insulin, c-peptides). Here, we found at least one single measure (from pre or post, PL or IN, T0 or T1) was not normally distributed for fasting time, hunger and glucose. Nonparametric tests are now given for these variables (Table 1, Methods line 503 ff, Appendix Tables 1 and 5). As expected, the results did not differ from those of the parametric testing.

BMI reduction was correlated with several measures (predictors of subsequent weight loss). In this (18) case, you must correct your p-values in relation to the number of tests (Bonferroni correction).

We have now restricted the reported correlations to those related to our main hypotheses on weight loss prediction, i.e., one correlation with peripheral insulin sensitivity and one correlation with neural insulin sensitivity. The neural results are already corrected by family wise error (FWE) correction. In addition, the multiple regression model was used to test the predictive value of both parameters within a single model (partial correlations). To address the reviewer’s concern, however, we Bonferroni corrected the regression coefficients. Both coefficients survived the corrected threshold in our model of interest. In the control analysis with BMI as a third predictor, peripheral insulin sensitivity survived only an uncorrected threshold.

Were the correlations between the BOLD response and peripheral insulin resistance still significant after adjusting for BMI and age?

As suggested by the Reviewer, we performed a control analysis including BMI and age as covariates in the regression model of central and peripheral insulin changes. This analysis again revealed significant results in the left NAc with an identical peak voxel (-10, 10, -7, P <.05, FWE corrected). This information was added to the results. For standardization, we also performed this control analysis for baseline findings in the VTA. Again, the addition of the covariates age and BMI did not result in a difference for the significant BOLD response in the VTA.

(19) Was the study registered, for example at clinical trials.gov? Were the hypothesis and primary outcomes or study design preregistered? How was weight loss success defined?

Unfortunately, when the study was initiated in 2015, we did not pre-register the project (as we do by default today). This study is based on a peer-reviewed grant proposal where hypotheses are specified and that can be provided by request. It is now post-hoc –registered, but we admit that this is not very satisfactory. Basically, the project was not designed as a clinical trial with no primary focus on clinical outcomes but as a basic research study of how peripheral and central insulin function relates to weight changes in this particular population. For this reason, weight loss (outcome) was only defined by BMI changes.

(20) The study by Tiedmann et al., 2017 Nat Comm used the same fMRI paradigm to investigate insulin response of the reward circuitry in young adults of normal weight as well as overweight and obesity. In that study, the authors used HOMA-IR as a measure of peripheral insulin resistance. Why change this in the current study?

The reason for using a c-peptide based measure was that in the current study we observed a surprisingly high within-subject variability in insulin compared to c-peptide levels in some participants. This was also statistically relevant, as evidenced by significantly higher coefficients of variance compared with c-peptide levels. Due to this variability together with reports that c-peptide based indices are a more reliable indicator of insulin secretion, we preferred a c-peptide based measure and are grateful for the reviewer’s suggestion to use HOMA-2 as a further updated model of HOMA-IR.

Furthermore, in the previous study by Tiedemann, DCM model was used to show insulin-induced changes in connectivity between the nucleus accumbens and VTA. This connection was significantly modulated by intranasal insulin. Why wasn't this evaluated in the current study?

In Tiedemann et al., 2017, we were interested in whether consistent animal findings on insulin action in mesolimbic networks / connections can be translated into the human brain and our DCM results mainly confirmed this translation. In the current study, we used these results to define regions of interest for studying individual predictors and consequences of weight changes. In fact, we had no hypotheses for a generally different basic insulinergic mechanism in this particular sample. But if there are hypotheses of age-related changes in basic insulin function, this should first be tested in healthy, lean older (compared with younger) adults, perhaps involving structural measures. We agree that this could be an interesting question to address in the future. However, the focus of the current project was on longitudinal aspects of the interactions between insulin and weight, and the amount of data and analyses reported here are already quite complex.

(21) The study of Tiedemann published in 2017 investigated the response of intranasal insulin in young individuals, while the current study included elderly persons. The impact of age independent of BMI has not been investigated thus far on neural insulin processing. Have you looked at the effect of age on the nucleus accumbens or VTA BOLD response to insulin?

We agree that age effects per se are very interesting. At the moment, we do not have data from healthy, lean, older adults to examine this aspect, but we hope to address this question in an upcoming project. At least, as suggested by the reviewer and addressed in response #18, our results hold when including age and BMI as covariates.

(22) The authors use the term insulinergic inhibition. Is it possible to evaluate neural inhibition with BOLD fMRI?

The term insulinergic inhibition is based primarily on our neural and behavioral findings showing (a) reduced food preference, and (b) reduced BOLD signal under insulin compared to placebo. This fits strikingly with data in rodents showing insulinergic inhibition of neural activity in eating-relevant mesolimbic systems (e.g., Stuber and Wise, Nature Neuroscience 2016). Of course, we are unable to differentiate direct or indirect inhibitory effects of insulin, but we believe that our current results, together with our previous data and animal findings, support this “inhibitory” interpretation.

https://doi.org/10.7554/eLife.76835.sa2

Article and author information

Author details

  1. Lena J Tiedemann

    Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
    Contribution
    Data curation, Formal analysis, Investigation, Visualization, Writing - original draft, Project administration
    Competing interests
    No competing interests declared
  2. Sebastian M Meyhöfer

    1. Institute for Endocrinology & Diabetes, University of Lübeck, Lübeck, Germany
    2. German Center for Diabetes Research (DZD), Ingolstädter Landstraße, Germany
    Contribution
    Methodology, Writing - original draft
    Competing interests
    No competing interests declared
  3. Paul Francke

    Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
    Contribution
    Data curation, Investigation, Writing - original draft, Project administration
    Competing interests
    No competing interests declared
  4. Judith Beck

    Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
    Contribution
    Data curation, Investigation, Project administration
    Competing interests
    No competing interests declared
  5. Christian Büchel

    Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
    Contribution
    Conceptualization, Funding acquisition, Writing - original draft
    Competing interests
    Senior editor, eLife
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1965-906X
  6. Stefanie Brassen

    Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
    Contribution
    Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Visualization, Methodology, Writing - original draft, Project administration
    For correspondence
    sbrassen@uke.de
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8884-7593

Funding

Deutsche Forschungsgemeinschaft (TRR 134)

  • Stefanie Brassen
  • Lena J Tiedemann
  • Sebastian M Meyhöfer
  • Paul Francke
  • Judith Beck
  • Christian Büchel

Deutsche Forschungsgemeinschaft (TRR 289 - ID 422744262)

  • Christian Büchel
  • Stefanie Brassen

The funders had no role in study design, data collection, and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank V Ott and M Hallschmid for advice on the INI application setup and K Giesen for help in data acquisition. We gratefully acknowledge funding from the German Research Foundation (DFG, TRR 134, TRR 289 – Project-ID 422744262).

Ethics

Informed consent and consent to publish was obtained in all participants. The study was approved by the local ethics committee of the Ethical Board, Hamburg, Germany (Hamburger Ärztekammer, ID: PV4463).

Senior and Reviewing Editor

  1. Carlos Isales, Medical College of Georgia at Augusta University, United States

Publication history

  1. Received: January 6, 2022
  2. Preprint posted: March 22, 2022 (view preprint)
  3. Accepted: September 8, 2022
  4. Version of Record published: September 27, 2022 (version 1)

Copyright

© 2022, Tiedemann et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Lena J Tiedemann
  2. Sebastian M Meyhöfer
  3. Paul Francke
  4. Judith Beck
  5. Christian Büchel
  6. Stefanie Brassen
(2022)
Insulin sensitivity in mesolimbic pathways predicts and improves with weight loss in older dieters
eLife 11:e76835.
https://doi.org/10.7554/eLife.76835

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