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- Landén, Mikael, 1966, et al.
(författare)
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Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group
- 2021
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Ingår i: Molecular Psychiatry. - : Springer Science and Business Media LLC. - 1359-4184 .- 1476-5578. ; 26, s. 5124-5139
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Tidskriftsartikel (refereegranskat)abstract
- Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this process is associated with clinical characteristics in a large multicenter international dataset. We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 19 samples worldwide. Healthy brain aging was estimated by predicting chronological age (18–75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 952 male and 1236 female controls from the ENIGMA MDD working group. The learned model coefficients were applied to 927 male controls and 986 depressed males, and 1199 female controls and 1689 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted “brain age” and chronological age was calculated to indicate brain-predicted age difference (brain-PAD). On average, MDD patients showed a higher brain-PAD of +1.08 (SE 0.22) years (Cohen’s d = 0.14, 95% CI: 0.08–0.20) compared with controls. However, this difference did not seem to be driven by specific clinical characteristics (recurrent status, remission status, antidepressant medication use, age of onset, or symptom severity). This highly powered collaborative effort showed subtle patterns of age-related structural brain abnormalities in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the clinical value of these brain-PAD estimates. © 2020, The Author(s).
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- Belov, Vladimir, et al.
(författare)
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Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures
- 2024
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Ingår i: Scientific Reports. - : NATURE PORTFOLIO. - 2045-2322. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.
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- Thanabalasingham, G., et al.
(författare)
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A large multi-centre European study validates high-sensitivity C-reactive protein (hsCRP) as a clinical biomarker for the diagnosis of diabetes subtypes
- 2011
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Ingår i: Diabetologia. - : Springer Science and Business Media LLC. - 1432-0428 .- 0012-186X. ; 54:11, s. 2801-2810
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Tidskriftsartikel (refereegranskat)abstract
- An accurate molecular diagnosis of diabetes subtype confers clinical benefits; however, many individuals with monogenic diabetes remain undiagnosed. Biomarkers could help to prioritise patients for genetic investigation. We recently demonstrated that high-sensitivity C-reactive protein (hsCRP) levels are lower in UK patients with hepatocyte nuclear factor 1 alpha (HNF1A)-MODY than in other diabetes subtypes. In this large multi-centre study we aimed to assess the clinical validity of hsCRP as a diagnostic biomarker, examine the genotype-phenotype relationship and compare different hsCRP assays. High-sensitivity CRP levels were analysed in individuals with HNF1A-MODY (n = 457), glucokinase (GCK)-MODY (n = 404), hepatocyte nuclear factor 4 alpha (HNF4A)-MODY (n = 54) and type 2 diabetes (n = 582) from seven European centres. Three common assays for hsCRP analysis were evaluated. We excluded 121 participants (8.1%) with hsCRP values > 10 mg/l. The discriminative power of hsCRP with respect to diabetes aetiology was assessed by receiver operating characteristic curve-derived C-statistic. In all centres and irrespective of the assay method, meta-analysis confirmed significantly lower hsCRP levels in those with HNF1A-MODY than in those with other aetiologies (z score -21.8, p < 5 x 10(-105)). HNF1A-MODY cases with missense mutations had lower hsCRP levels than those with truncating mutations (0.03 vs 0.08 mg/l, p < 5 x 10(-5)). High-sensitivity CRP values between assays were strongly correlated (r (2) a parts per thousand yenaEuro parts per thousand 0.91, p a parts per thousand currency signaEuro parts per thousand 1 x 10(-5)). Across the seven centres, the C-statistic for distinguishing HNF1A-MODY from young adult-onset type 2 diabetes ranged from 0.79 to 0.97, indicating high discriminative accuracy. In the largest study to date, we have established that hsCRP is a clinically valid biomarker for HNF1A-MODY in European populations. Given the modest costs and wide availability, hsCRP could translate rapidly into clinical practice, considerably improving diagnosis rates in monogenic diabetes.
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- Thanabalasingham, Gaya, et al.
(författare)
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Mutations in HNF1A Result in Marked Alterations of Plasma Glycan Profile
- 2013
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Ingår i: Diabetes. - : American Diabetes Association. - 0012-1797 .- 1939-327X. ; 62:4, s. 1329-1337
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Tidskriftsartikel (refereegranskat)abstract
- A recent genome-wide association study identified hepatocyte nuclear factor 1-alpha (HNF1A) as a key regulator of fucosylation. We hypothesized that loss-of-function HNF1A mutations causal for maturity-onset diabetes of the young (MOD?) would display altered fucosylation of N-linked glycans on plasma proteins and that glycan biomarkers could improve the efficiency of a diagnosis of HNF1A-MODY. In a pilot comparison of 33 subjects with HNF1A-MODY and 41 subjects with type 2 diabetes, 15 of 29 glycan measurements differed between the two groups. The DG9-glycan index, which is the ratio of fucosylated to nonfucosylated triantennary glycans, provided optimum discrimination in the pilot study and was examined further among additional subjects with HNF1A-MODY (n = 188), glucokinase (GCE)-MODY (n = 118), hepatocyte nuclear factor 4-alpha (HNF4A)-MODY (n = 40), type 1 diabetes (n = 98), type 2 diabetes (n = 167), and nondiabetic controls (n = 98). The DG9-glycan index was markedly lower in HNF1A-MODY than in controls or other diabetes subtypes, offered good discrimination between HNF1A-MODY and both type 1 and type 2 diabetes (C statistic >= 0.90), and enabled us to detect three previously undetected HNF1A mutations in patients with diabetes. In conclusion, glycan profiles are altered substantially in HNF1A-MODY, and the DG9-glycan index has potential clinical value as a diagnostic biomarker of HNF1A dysfunction.
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