Increased brain age in adults with Prader-Willi syndrome

Abstract

Prader-Willi syndrome (PWS) is the most common genetic obesity syndrome, with associated learning difficulties, neuroendocrine deficits, and behavioural and psychiatric problems. As the life expectancy of individuals with PWS increases, there is concern that alterations in brain structure associated with the syndrome, as a direct result of absent expression of PWS genes, and its metabolic complications and hormonal deficits, might cause early onset of physiological and brain aging. In this study, a machine learning approach was used to predict brain age based on grey matter (GM) and white matter (WM) maps derived from structural neuroimaging data using T1-weighted magnetic resonance imaging (MRI) scans. Brain-predicted age difference (brain-PAD) scores, calculated as the difference between chronological age and brain-predicted age, are designed to reflect deviations from healthy brain aging, with higher brain-PAD scores indicating premature aging. Two separate adult cohorts underwent brain-predicted age calculation. The main cohort consisted of adults with PWS (n = 20; age mean 23.1 years, range 19.8-27.7; 70.0% male; body mass index (BMI) mean 30.1 kg/m², 21.5-47.7; n = 19 paternal chromosome 15q11-13 deletion) and age- and sex-matched controls (n = 40; age 22.9 years, 19.6-29.0; 65.0% male; BMI 24.1 kg/m², 19.2-34.2) adults (BMI PWS vs. control P = .002). Brain-PAD was significantly greater in PWS than controls (effect size mean ± SEM +7.24 ± 2.20 years [95% CI 2.83, 11.63], P = .002). Brain-PAD remained significantly greater in PWS than controls when restricting analysis to a sub-cohort matched for BMI consisting of n = 15 with PWS with BMI range 21.5-33.7 kg/m², and n = 29 controls with BMI 21.7-34.2 kg/m² (effect size +5.51 ± 2.56 years [95% CI 3.44, 10.38], P = .037). In the PWS group, brain-PAD scores were not associated with intelligence quotient (IQ), use of hormonal and psychotropic medications, nor severity of repetitive or disruptive behaviours. A 24.5 year old man (BMI 36.9 kg/m²) with PWS from a SNORD116 microdeletion also had increased brain PAD of 12.87 years, compared to 0.84 ± 6.52 years in a second control adult cohort (n = 95; age mean 34.0 years, range 19.9-55.5; 38.9% male; BMI 28.7 kg/m², 19.1-43.1). This increase in brain-PAD in adults with PWS indicates abnormal brain structure that may reflect premature brain aging or abnormal brain development. The similar finding in a rare patient with a SNORD116 microdeletion implicates a potential causative role for this PWS region gene cluster in the structural brain abnormalities associated primarily with the syndrome and/or its complications. Further longitudinal neuroimaging studies are needed to clarify the natural history of this increase in brain age in PWS, its relationship with obesity, and whether similar findings are seen in those with PWS from maternal uniparental disomy.

Keywords: Body mass index; MRI; Obesity; PWS; SNORD116; Structural neuroimaging.

Fig. 1

Outline of methods for brain-age prediction using machine learning. The data includes a training set and 2 testing sets (cohort1: controls vs. PWS; cohort 2: controls vs. SNORD116 microdeletion). The data was pre-processed using the Statistical Parametric Mapping software (SPM). The T1 images underwent tissue segmentation after initial quality control, separating GM, WM and CSF. The segmented images were then normalized using DARTEL for nonlinear registration, then resampled to the MNI152 template using a 4 mm smoothing kernel. GM and WM maps were concatenated into a single vector of data relating to brain size. All data underwent voxel-wise similarity analysis to generate a similarity kernel using PRoNTo. The training set was used to generate the brain-age model via supervised machine learning that produced a Gaussian Processes Regression (GPR) model trained to recognize structural patterns from imaging data associated with chronological age. The accuracy of the model generated from the training set was assessed using a 10-fold cross-validation method whereby 10% of samples were used for testing in all possible iterations to generate age predictions on all samples. The trained and validated GPR model was applied to the two test groups.

Fig. 2

Brain-predicted age in individuals with Prader-Willi syndrome (PWS) and controls (cohort 1). (A,B) Scatterplot of (A) brain-predicted age or (B) brain-predicted age difference (brain-PAD) (y-axis) vs. chronological real age (x-axis) in controls (blue triangles, blue dashed linear regression line) and individuals with PWS (red circles, red solid linear regression line), with line of identity (black dotted line). (C) Boxplot shows brain-PAD scores distribution with median, interquartile range, and bars showing 5th and 95th percentiles, with outliers as symbols, and mean as a cross. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 3

Brain-predicted age difference in individuals with Prader-Willi syndrome (PWS) and controls (cohort 1). Scatterplot of brain-predicted age difference (brain-PAD) (y-axis) vs. (A) GM volumes, (B) WM volumes, (C) BMI, or (D) IQ (x-axis), in individuals with PWS (red circles, red solid linear regression line) and controls (blue triangles, blue dashed linear regression line). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 4

Brain-age prediction in man with SNORD116 microdeletion and controls (cohort 2). Male with a SNORD116 micodeletion (red filled circle) shows an increase in (A) brain age, and (B,C) brain-PAD scores when adjusting for (B) chronological real age or (C) BMI, compared to the control group (unfilled blue circles, and in (A) solid blue regression line). Black dotted line is line of equality (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.).