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Randomized Controlled Trial
. 2023 Apr 6:12:e83604.
doi: 10.7554/eLife.83604.

The effect of weight loss following 18 months of lifestyle intervention on brain age assessed with resting-state functional connectivity

Gidon Levakov #  1 , Alon Kaplan #  2   3 , Anat Yaskolka Meir  2 , Ehud Rinott  2 , Gal Tsaban  2 , Hila Zelicha  2 , Matthias Blüher  4 , Uta Ceglarek  4 , Michael Stumvoll  4 , Ilan Shelef  5 , Galia Avidan #  6 , Iris Shai #  2   4   7
Affiliations
Randomized Controlled Trial

The effect of weight loss following 18 months of lifestyle intervention on brain age assessed with resting-state functional connectivity

Gidon Levakov et al. Elife. .

Abstract

Background: Obesity negatively impacts multiple bodily systems, including the central nervous system. Retrospective studies that estimated chronological age from neuroimaging have found accelerated brain aging in obesity, but it is unclear how this estimation would be affected by weight loss following a lifestyle intervention.

Methods: In a sub-study of 102 participants of the Dietary Intervention Randomized Controlled Trial Polyphenols Unprocessed Study (DIRECT-PLUS) trial, we tested the effect of weight loss following 18 months of lifestyle intervention on predicted brain age based on magnetic resonance imaging (MRI)-assessed resting-state functional connectivity (RSFC). We further examined how dynamics in multiple health factors, including anthropometric measurements, blood biomarkers, and fat deposition, can account for changes in brain age.

Results: To establish our method, we first demonstrated that our model could successfully predict chronological age from RSFC in three cohorts (n=291;358;102). We then found that among the DIRECT-PLUS participants, 1% of body weight loss resulted in an 8.9 months' attenuation of brain age. Attenuation of brain age was significantly associated with improved liver biomarkers, decreased liver fat, and visceral and deep subcutaneous adipose tissues after 18 months of intervention. Finally, we showed that lower consumption of processed food, sweets and beverages were associated with attenuated brain age.

Conclusions: Successful weight loss following lifestyle intervention might have a beneficial effect on the trajectory of brain aging.

Funding: The German Research Foundation (DFG), German Research Foundation - project number 209933838 - SFB 1052; B11, Israel Ministry of Health grant 87472511 (to I Shai); Israel Ministry of Science and Technology grant 3-13604 (to I Shai); and the California Walnuts Commission 09933838 SFB 105 (to I Shai).

Keywords: MRI; Mediterranean diet; brain age; epidemiology; functional connectivity; global health; human; lifestyle intervention; obesity.

Plain language summary

Obesity is linked with the brain aging faster than would normally be expected. Researchers are able to capture this process by calculating a person’s ‘brain age’ – how old their brain appears on detailed scans, regardless of chronological age. This approach also helps to monitor how certain factors, such as lifestyle, can influence brain aging over relatively short time scales. It is not clear whether lifestyle interventions that promote weight loss can help to slow obesity-driven brain aging. To answer this question, Levakov et al. studied 102 individuals who met the criteria for obesity and took part in a lifestyle intervention aimed to improve diet and physical activity levels over 18 months. The participants received a brain scan at the beginning and the end of the program; additional tests and measurements were also conducted at these times to capture other biological processes impacted by obesity, such as liver health. Levakov et al. used the brain scans taken at the start and end of the study to examine the impact of the lifestyle intervention on the aging trajectory. The results revealed that a reduction in body weight of 1% led to the participants’ brain age being nearly 9 months younger than the expected brain age after 18 months. This attenuated aging was associated with changes in other biological measures, such as decreased liver fat and liver enzymes. Increases in liver fat and production of specific liver enzymes were previously shown to negatively impact brain health in Alzheimer’s disease. Finally, examining more closely the food consumption reports completed by participants showed that reduced consumption of processed food, sweets and beverages were linked to attenuated brain aging. The findings show that lifestyle interventions which promote weight loss can have a beneficial impact on the aging trajectory of the brain observed with obesity. The next steps will include determining whether slowing down obesity-driven brain aging results in better clinical outcomes for patients. In addition, the work by Levakov et al. demonstrates a potential strategy to evaluate the success of lifestyle changes on brain health. With global rates of obesity rising, identifying interventions that have a positive impact on brain health could have important clinical, educational and social impacts.

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Conflict of interest statement

GL, AK, AY, ER, GT, HZ, UC, MS, IS, GA, IS No competing interests declared, MB has received consulting fees from Amgen, Astra Zeneca, Boehringer-Ingelheim, Bayer, Lilly, Novo Nordisk, Novartis, Sanofi and Pfizer; and fees for lectures/ presentations from Amgen, Astra Zeneca, Boehringer-Ingelheim, Bayer, Daiichi-Sankyo, Lilly, Novo Nordisk, Novartis, Sanofi and Pfizer. The author is also on the advisory board for Boehringer-Ingelheim. The author has no other competing interests to declare

Figures

Figure 1.
Figure 1.. Study design and workflow.
The Dietary Intervention Randomized Controlled Trial Polyphenols Unprocessed Study (DIRECT-PLUS) trial examined the effect of successful weight loss following 18-month lifestyle intervention on adiposity, cardiometabolic, and brain health across intervention groups. (a) Participants in the functional connectivity sub-study (N=132) completed the baseline measurements at T0. They were randomly assigned to three intervention groups: healthy dietary guidelines (HDG), an active control group, Mediterranean diet (MED), and green-MED. All groups were combined with physical activity (PA). Eighteen months following intervention onset, all measurements were retaken (T18). (b) Measurements included anthropometric measurements, blood biomarkers, fat deposition, and structural and functional brain imaging. (c) Functional brain imaging was conducted while subjects were at rest and was used to estimate resting-state functional connectivity (RSFC). RSFC measures the correlation between the time series of pairs of brain regions. (d) We fitted a linear support vector regression to predict chronological age from all pairwise correlations. We fitted the model on the Nathan Kline Institute (NKI) dataset, then tested and applied it to the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) and the DIRECT-PLUS data. (e, left scatter plot) Based on the T0 data, we first computed the expected aging trajectory as the linear relation between the chronological and predicted age of all subjects. The fitted line represents the increase in the predicted age in relation to chronological age in the absence of an intervention. (e, right scatter plot) The fitted line was used to estimate the expected brain age at T18, given each participant’s T0 brain age and the time passed between the T0 and T18 magnetic resonance imaging (MRI) scans. We computed the observed brain age by applying the brain age model to the T18 scans. Brain age attenuation was calculated as the expected brain age minus the observed at T18.
Figure 2.
Figure 2.. Prediction accuracy within the validation and test cohorts.
The scatter plots depict the data points and regression line between the predicted (y-axis) and observed (x-axis) age. The predicted-observed correlation is presented for the validation data (left), the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) test data (middle), and the Dietary Intervention Randomized Controlled Trial Polyphenols Unprocessed Study (DIRECT-PLUS) data at baseline. The shaded area around the regression lines represents a 95% confidence interval estimated using bootstrapping. Pearson’s correlation, MAE (mean absolute error), and corresponding p-values are shown at the bottom of each plot. The dotted lines represent a perfect correlation for reference (predicted = observed).
Figure 2—figure supplement 1.
Figure 2—figure supplement 1.. Brain age prediction accuracy of individual nodes.
We reiterated the model training procedure for each of the nodes separately. We extracted the row of each node in the resting-state functional connectivity (RSFC) matrix, which represents all the nodes’ connections with the rest of the brain, or its ‘connectivity fingerprint’. As in the original model, we used a linear support vector regression fitted on the Nathan Kline Institute (NKI) dataset, then tested it on the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) dataset. Prediction accuracy was quantified as the correlation between chronological age and predicted age. (a) Prediction accuracy was depicted on the brain surface on a lateral (top) and medial (bottom) view. (b) Box plot showing the prediction accuracy (y-axis) in each of the seven canonical resting-state networks Zelicha et al., 2019. Each dot represents a single node.
Figure 3.
Figure 3.. Observed compared to expected brain age at T18.
The upper panel depicts the chronological age (x-axis) and the observed (empty circles) and expected (full circles) brain age (y-axis) of each subject. The dashed line represents the expected brain age trajectory fitted based on the T0 data (see regression line in Figure 1e, left). Arrows point from the expected to the observed age of a single individual, corresponding to brain age attenuation. Arrows’ colors correspond to the extent of brain age attenuation (blue shades indicate attenuation, red shades indicate an acceleration in brain age). The observed age was lower than expected in 56.8% of the subjects, while the opposite was found in 43.1% (X2=1.922, p=0.166). In the lower panel, arrows were reordered by subjects’ body mass index (BMI) change over the 18 months of intervention. A significant correlation was found between the BMI and brain age change (r=0.319, p<0.001). This is evident in the graph, such that most of the blue arrows are located on the left side of the x-axis (negative values), and most of the red arrows appear on the right side (positive values).
Figure 3—figure supplement 1.
Figure 3—figure supplement 1.. Brain age attenuation compared to percent weight reduction from baseline.
A scatter plot depicting the linear relationship between percent weight reduction from baseline (x-axis) and years of brain age attenuation (y-axis). Also shown on the graph are the correlation coefficient and the parameters of the linear relation between the two variables. One percent of body weight loss corresponded to an 8.9 months’ attenuation of brain age. The shaded area around the regression line represent a 95% confidence interval estimated using bootstrap.
Figure 4.
Figure 4.. Brain age attenuation association with clinical measurements.
The scatter plots depict the data points and regression line between brain age attenuation (y-axis) and each clinical measurement (x-axis). Clinical measurements include anthropometry (blue), liver markers (orange), glycemic markers (brown), lipid profile (red), and fat deposition measured using magnetic resonance imaging (MRI) (green). The shaded area around the regression line represents a 95% confidence interval estimated using bootstrapping. Pearson’s correlation and the corresponding p-value are shown at the bottom of each plot. Significant associations following false discovery rate (FDR) correction are marked in bold (*=p < 0.05, **=p < 0.01).
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