Most cited article - PubMed ID 29272464
Brain Age in Early Stages of Bipolar Disorders or Schizophrenia
BACKGROUND: Cardiometabolic risk factors - including diabetes, hypertension, and obesity - have long been linked with adverse health outcomes such as strokes, but more subtle brain changes in regional brain volumes and cortical thickness associated with these risk factors are less understood. Computer models can now be used to estimate brain age based on structural magnetic resonance imaging data, and subtle brain changes related to cardiometabolic risk factors may manifest as an older-appearing brain in prediction models; thus, we sought to investigate the relationship between cardiometabolic risk factors and machine learning-predicted brain age. METHODS: We performed a systematic search of PubMed and Scopus. We used the brain age gap, which represents the difference between one's predicted and chronological age, as an index of brain structural integrity. We calculated the Cohen d statistic for mean differences in the brain age gap of people with and without diabetes, hypertension, or obesity and performed random effects meta-analyses. RESULTS: We identified 185 studies, of which 14 met inclusion criteria. Among the 3 cardiometabolic risk factors, diabetes had the highest effect size (12 study samples; d = 0.275, 95% confidence interval [CI] 0.198-0.352; n = 47 436), followed by hypertension (10 study samples; d = 0.113, 95% CI 0.063-0.162; n = 45 102) and obesity (5 study samples; d = 0.112, 95% CI 0.037-0.187; n = 15 678). These effects remained significant in sensitivity analyses that included only studies that controlled for confounding effects of the other cardiometabolic risk factors. LIMITATIONS: Our study tested effect sizes of only categorically defined cardiometabolic risk factors and is limited by inconsistencies in diabetes classification, a smaller pooled sample in the obesity analysis, and limited age range reporting. CONCLUSION: Our findings show that each of the cardiometabolic risk factors uniquely contributes to brain structure, as captured by brain age. The effect size for diabetes was more than 2 times greater than the independent effects of hypertension and obesity. We therefore highlight diabetes as a primary target for the prevention of brain structural changes that may lead to cognitive decline and dementia.
- MeSH
- Diabetes Mellitus * pathology MeSH
- Hypertension * pathology MeSH
- Cardiometabolic Risk Factors * MeSH
- Humans MeSH
- Magnetic Resonance Imaging MeSH
- Brain * pathology diagnostic imaging MeSH
- Obesity * pathology MeSH
- Risk Factors MeSH
- Aging * pathology MeSH
- Machine Learning MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Meta-Analysis MeSH
Schizophrenia (SZ) is associated with an increased risk of life-long cognitive impairments, age-related chronic disease, and premature mortality. We investigated evidence for advanced brain ageing in adult SZ patients, and whether this was associated with clinical characteristics in a prospective meta-analytic study conducted by the ENIGMA Schizophrenia Working Group. The study included data from 26 cohorts worldwide, with a total of 2803 SZ patients (mean age 34.2 years; range 18-72 years; 67% male) and 2598 healthy controls (mean age 33.8 years, range 18-73 years, 55% male). Brain-predicted age was individually estimated using a model trained on independent data based on 68 measures of cortical thickness and surface area, 7 subcortical volumes, lateral ventricular volumes and total intracranial volume, all derived from T1-weighted brain magnetic resonance imaging (MRI) scans. Deviations from a healthy brain ageing trajectory were assessed by the difference between brain-predicted age and chronological age (brain-predicted age difference [brain-PAD]). On average, SZ patients showed a higher brain-PAD of +3.55 years (95% CI: 2.91, 4.19; I2 = 57.53%) compared to controls, after adjusting for age, sex and site (Cohen's d = 0.48). Among SZ patients, brain-PAD was not associated with specific clinical characteristics (age of onset, duration of illness, symptom severity, or antipsychotic use and dose). This large-scale collaborative study suggests advanced structural brain ageing in SZ. Longitudinal studies of SZ and a range of mental and somatic health outcomes will help to further evaluate the clinical implications of increased brain-PAD and its ability to be influenced by interventions.
- MeSH
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Magnetic Resonance Imaging MeSH
- Adolescent MeSH
- Young Adult MeSH
- Brain pathology MeSH
- Prospective Studies MeSH
- Schizophrenia * MeSH
- Aged MeSH
- Aging MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Adolescent MeSH
- Young Adult MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Meta-Analysis MeSH
- Research Support, Non-U.S. Gov't MeSH
- Research Support, N.I.H., Extramural MeSH
The heritable component of schizophrenia (SCH) as a polygenic trait is represented by numerous variants from a heterogeneous group of genes each contributing a relatively small effect. Various SNPs have already been found and analyzed in genes encoding the NMDAR subunits. However, less is known about genetic variations of genes encoding the AMPA and kainate receptor subunits. We analyzed sixteen iGluR genes in full length to determine the sequence variability of iGluR genes. Our aim was to describe the rate of genetic variability, its distribution, and the co-occurrence of variants and to identify new candidate risk variants or haplotypes. The cumulative effect of genetic risk was then estimated using a simple scoring model. GRIN2A-B, GRIN3A-B, and GRIK4 genes showed significantly increased genetic variation in SCH patients. The fixation index statistic revealed eight intronic haplotypes and an additional four intronic SNPs within the sequences of iGluR genes associated with SCH (p < 0.05). The haplotypes were used in the proposed simple scoring model and moreover as a test for genetic predisposition to schizophrenia. The positive likelihood ratio for the scoring model test reached 7.11. We also observed 41 protein-altering variants (38 missense variants, four frameshifts, and one nonsense variant) that were not significantly associated with SCH. Our data suggest that some intronic regulatory regions of iGluR genes and their common variability are among the components from which the genetic predisposition to SCH is composed.
- Keywords
- BrainAGE, SNP, genetic variations, haplotypes, intron, ionotropic glutamate receptor, schizophrenia, scoring model,
- Publication type
- Journal Article MeSH
Individuals with bipolar disorders (BD) frequently suffer from obesity, which is often associated with neurostructural alterations. Yet, the effects of obesity on brain structure in BD are under-researched. We obtained MRI-derived brain subcortical volumes and body mass index (BMI) from 1134 BD and 1601 control individuals from 17 independent research sites within the ENIGMA-BD Working Group. We jointly modeled the effects of BD and BMI on subcortical volumes using mixed-effects modeling and tested for mediation of group differences by obesity using nonparametric bootstrapping. All models controlled for age, sex, hemisphere, total intracranial volume, and data collection site. Relative to controls, individuals with BD had significantly higher BMI, larger lateral ventricular volume, and smaller volumes of amygdala, hippocampus, pallidum, caudate, and thalamus. BMI was positively associated with ventricular and amygdala and negatively with pallidal volumes. When analyzed jointly, both BD and BMI remained associated with volumes of lateral ventricles and amygdala. Adjusting for BMI decreased the BD vs control differences in ventricular volume. Specifically, 18.41% of the association between BD and ventricular volume was mediated by BMI (Z = 2.73, p = 0.006). BMI was associated with similar regional brain volumes as BD, including lateral ventricles, amygdala, and pallidum. Higher BMI may in part account for larger ventricles, one of the most replicated findings in BD. Comorbidity with obesity could explain why neurostructural alterations are more pronounced in some individuals with BD. Future prospective brain imaging studies should investigate whether obesity could be a modifiable risk factor for neuroprogression.
BACKGROUND: Obesity is highly prevalent in schizophrenia, with implications for psychiatric prognosis, possibly through links between obesity and brain structure. In this longitudinal study in first episode of psychosis (FEP), we used machine learning and structural magnetic resonance imaging (MRI) to study the impact of psychotic illness and obesity on brain ageing/neuroprogression shortly after illness onset. METHODS: We acquired 2 prospective MRI scans on average 1.61 years apart in 183 FEP and 155 control individuals. We used a machine learning model trained on an independent sample of 504 controls to estimate the individual brain ages of study participants and calculated BrainAGE by subtracting chronological from the estimated brain age. RESULTS: Individuals with FEP had a higher initial BrainAGE than controls (3.39 ± 6.36 vs 1.72 ± 5.56 years; β = 1.68, t(336) = 2.59, P = .01), but similar annual rates of brain ageing over time (1.28 ± 2.40 vs 1.07±1.74 estimated years/actual year; t(333) = 0.93, P = .18). Across both cohorts, greater baseline body mass index (BMI) predicted faster brain ageing (β = 0.08, t(333) = 2.59, P = .01). For each additional BMI point, the brain aged by an additional month per year. Worsening of functioning over time (Global Assessment of Functioning; β = -0.04, t(164) = -2.48, P = .01) and increases especially in negative symptoms on the Positive and Negative Syndrome Scale (β = 0.11, t(175) = 3.11, P = .002) were associated with faster brain ageing in FEP. CONCLUSIONS: Brain alterations in psychosis are manifest already during the first episode and over time get worse in those with worsening clinical outcomes or higher baseline BMI. As baseline BMI predicted faster brain ageing, obesity may represent a modifiable risk factor in FEP that is linked with psychiatric outcomes via effects on brain structure.
- Keywords
- antipsychotics, brain age, first-episode psychosis, longitudinal study, machine learning, obesity,
- MeSH
- Adult MeSH
- Body Mass Index MeSH
- Humans MeSH
- Longitudinal Studies MeSH
- Magnetic Resonance Imaging MeSH
- Adolescent MeSH
- Young Adult MeSH
- Obesity complications diagnostic imaging pathology physiopathology MeSH
- Aging, Premature diagnostic imaging etiology pathology physiopathology MeSH
- Disease Progression * MeSH
- Psychotic Disorders diagnostic imaging pathology physiopathology MeSH
- Risk Factors MeSH
- Machine Learning * MeSH
- Check Tag
- Adult MeSH
- Humans MeSH
- Adolescent MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
BACKGROUND: Schizophrenia and bipolar disorder share genetic liability, and some structural brain abnormalities are common to both conditions. First-degree relatives of patients with schizophrenia (FDRs-SZ) show similar brain abnormalities to patients, albeit with smaller effect sizes. Imaging findings in first-degree relatives of patients with bipolar disorder (FDRs-BD) have been inconsistent in the past, but recent studies report regionally greater volumes compared with control subjects. METHODS: We performed a meta-analysis of global and subcortical brain measures of 6008 individuals (1228 FDRs-SZ, 852 FDRs-BD, 2246 control subjects, 1016 patients with schizophrenia, 666 patients with bipolar disorder) from 34 schizophrenia and/or bipolar disorder family cohorts with standardized methods. Analyses were repeated with a correction for intracranial volume (ICV) and for the presence of any psychopathology in the relatives and control subjects. RESULTS: FDRs-BD had significantly larger ICV (d = +0.16, q < .05 corrected), whereas FDRs-SZ showed smaller thalamic volumes than control subjects (d = -0.12, q < .05 corrected). ICV explained the enlargements in the brain measures in FDRs-BD. In FDRs-SZ, after correction for ICV, total brain, cortical gray matter, cerebral white matter, cerebellar gray and white matter, and thalamus volumes were significantly smaller; the cortex was thinner (d < -0.09, q < .05 corrected); and third ventricle was larger (d = +0.15, q < .05 corrected). The findings were not explained by psychopathology in the relatives or control subjects. CONCLUSIONS: Despite shared genetic liability, FDRs-SZ and FDRs-BD show a differential pattern of structural brain abnormalities, specifically a divergent effect in ICV. This may imply that the neurodevelopmental trajectories leading to brain anomalies in schizophrenia or bipolar disorder are distinct.
- Keywords
- Bipolar disorder, Familial risk, Imaging, Meta-analysis, Neurodevelopment, Schizophrenia,
- MeSH
- Bipolar Disorder * genetics pathology MeSH
- Adult MeSH
- Genetic Predisposition to Disease * MeSH
- Cohort Studies MeSH
- Middle Aged MeSH
- Humans MeSH
- Young Adult MeSH
- Brain pathology MeSH
- Schizophrenia * genetics pathology MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Meta-Analysis MeSH
- Research Support, Non-U.S. Gov't MeSH
- Research Support, N.I.H., Extramural MeSH
- Research Support, N.I.H., Intramural MeSH
- Research Support, U.S. Gov't, Non-P.H.S. MeSH
- Twin Study MeSH
