scholarly journals Genetic contributions to self-reported tiredness

2016 ◽  
Author(s):  
Vincent Deary ◽  
Saskia P Hagenaars ◽  
Sarah E Harris ◽  
W David Hill ◽  
Gail Davies ◽  
...  
Keyword(s):  
Mental Health ◽  
Type 2 Diabetes ◽  
Bipolar Disorder ◽  
Major Depressive Disorder ◽  
Smoking Status ◽  
Hdl Cholesterol ◽  
Physical And Mental Health ◽  
Major Depressive ◽  
Genome Wide

Self-reported tiredness and low energy, often called fatigue, is associated with poorer physical and mental health. Twin studies have indicated that this has a heritability between 6% and 50%. In the UK Biobank sample (N = 108 976) we carried out a genome-wide association study of responses to the question, ″Over the last two weeks, how often have you felt tired or had little energy?″ Univariate GCTA-GREML found that the proportion of variance explained by all common SNPs for this tiredness question was 8.4% (SE = 0.6%). GWAS identified one genome-wide significant hit (Affymetrix id 1:64178756_C_T; p = 1.36 x 10-11). LD score regression and polygenic profile analysis were used to test for pleiotropy between tiredness and up to 28 physical and mental health traits from GWAS consortia. Significant genetic correlations were identified between tiredness and BMI, HDL cholesterol, forced expiratory volume, grip strength, HbA1c, longevity, obesity, self-rated health, smoking status, triglycerides, type 2 diabetes, waist-hip ratio, ADHD, bipolar disorder, major depressive disorder, neuroticism, schizophrenia, and verbal-numerical reasoning (absolute rg effect sizes between 0.11 and 0.78). Significant associations were identified between tiredness phenotypic scores and polygenic profile scores for BMI, HDL cholesterol, LDL cholesterol, coronary artery disease, HbA1c, height, obesity, smoking status, triglycerides, type 2 diabetes, and waist-hip ratio, childhood cognitive ability, neuroticism, bipolar disorder, major depressive disorder, and schizophrenia (standardised β′s between -0.016 and 0.03). These results suggest that tiredness is a partly-heritable, heterogeneous and complex phenomenon that is phenotypically and genetically associated with affective, cognitive, personality, and physiological processes.

scholarly journals Genetic contributions to self-reported tiredness

2017 ◽  
Vol 23 (3) ◽  
pp. 609-620 ◽  
Author(s):  
V Deary ◽  
◽  
S P Hagenaars ◽  
S E Harris ◽  
W D Hill ◽  
...  
Keyword(s):  
Mental Health ◽  
Smoking Status ◽  
Density Lipoprotein ◽  
Hdl Cholesterol ◽  
Physical And Mental Health ◽  
C Reactive Protein ◽  
Major Depressive ◽  
Reactive Protein ◽  
Genome Wide

Abstract Self-reported tiredness and low energy, often called fatigue, are associated with poorer physical and mental health. Twin studies have indicated that this has a heritability between 6 and 50%. In the UK Biobank sample (N=108 976), we carried out a genome-wide association study (GWAS) of responses to the question, ‘Over the last two weeks, how often have you felt tired or had little energy?’ Univariate GCTA-GREML found that the proportion of variance explained by all common single-nucleotide polymorphisms for this tiredness question was 8.4% (s.e.=0.6%). GWAS identified one genome-wide significant hit (Affymetrix id 1:64178756_C_T; P=1.36 × 10−11). Linkage disequilibrium score regression and polygenic profile score analyses were used to test for shared genetic aetiology between tiredness and up to 29 physical and mental health traits from GWAS consortia. Significant genetic correlations were identified between tiredness and body mass index (BMI), C-reactive protein, high-density lipoprotein (HDL) cholesterol, forced expiratory volume, grip strength, HbA1c, longevity, obesity, self-rated health, smoking status, triglycerides, type 2 diabetes, waist–hip ratio, attention deficit hyperactivity disorder, bipolar disorder, major depressive disorder, neuroticism, schizophrenia and verbal-numerical reasoning (absolute r g effect sizes between 0.02 and 0.78). Significant associations were identified between tiredness phenotypic scores and polygenic profile scores for BMI, HDL cholesterol, low-density lipoprotein cholesterol, coronary artery disease, C-reactive protein, HbA1c, height, obesity, smoking status, triglycerides, type 2 diabetes, waist–hip ratio, childhood cognitive ability, neuroticism, bipolar disorder, major depressive disorder and schizophrenia (standardised β’s had absolute values<0.03). These results suggest that tiredness is a partly heritable, heterogeneous and complex phenomenon that is phenotypically and genetically associated with affective, cognitive, personality and physiological processes.

scholarly journals Major depressive disorder but not bipolar disorder and schizophrenia is a causal factor for type 2 diabetes as determined by Mendelian randomization

2020 ◽  
Author(s):  
Heejin Jin ◽  
Jeewon Lee ◽  
Oh Sohee ◽  
Sanghun Lee ◽  
Sungho Won
Keyword(s):  
Type 2 Diabetes ◽  
Bipolar Disorder ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Reverse Causality ◽  
Major Depressive ◽  
Mr Study

Objective: In many epidemiologic studies, type 2 diabetes has been reported to be associated with severe mental illness (SMI) such as schizophrenia (SCZ), bipolar disorder (BPD), and major depressive disorder (MDD). However, the relationship between SMI and type 2 diabetes is bi-directional, and the causal relationship remains unclear due to various confounders. Therefore, a Mendelian randomization (MR) study is necessary to identify the causality between them. Research Design and Methods: We conducted a two−sample MR study to identify the causal effect of SMI on type 2 diabetes using the inverse-variance weighted (IVW), MR−Egger, MR− Egger with a simulation extrapolation, weighted median approach, and MR-Pleiotropy RESidual Sum and Outlier methods. The most appropriate method was selected according to the instrument variables assumption. Results: We found that MDD had a significant causal effect on type 2 diabetes from the results obtained using the IVW method (Odds ratio (OR): 1.191, 95% CI: 1.036−1.372, P = 0.014); however, this was not observed for BPD (IVW, OR: 1.006, 95% CI: 0.918−1.104, P = 0.892) or SCZ (IVW, OR: 1.016, 95% CI: 0.974−1.059, P = 0.463). The absence of reverse-causality between MDD and type 2 diabetes was also demonstrated from bidirectional MR studies. Conclusions: These results clearly reveal important knowledge on the causal role of MDD in the risk of type 2 diabetes without a residual confounding, whereas the causality of BPD and SCZ was not shown. Therefore, careful attention should be paid to MDD patients in type 2 diabetes prevention and treatment.

scholarly journals Grip Strength, Neurocognition, and Social Functioning in People WithType-2 Diabetes Mellitus, Major Depressive Disorder, Bipolar Disorder, and Schizophrenia

2020 ◽  
Vol 11 ◽  
Author(s):  
María Aliño-Dies ◽  
Joan Vicent Sánchez-Ortí ◽  
Patricia Correa-Ghisays ◽  
Vicent Balanzá-Martínez ◽  
Joan Vila-Francés ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Bipolar Disorder ◽  
Type 2 Diabetes Mellitus ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Social Functioning ◽  
Cognitive Performance ◽  
Major Depressive

Background: Frailty is a common syndrome among older adults and patients with several comorbidities. Grip strength (GS) is a representative parameter of frailty because it is a valid indicator of current and long-term physical conditions in the general population and patients with severe mental illnesses (SMIs). Physical and cognitive capacities of people with SMIs are usually impaired; however, their relationship with frailty or social functioning have not been studied to date. The current study aimed to determine if GS is a valid predictor of changes in cognitive performance and social functioning in patients with type-2 diabetes mellitus and SMIs. Methods: Assessments of social functioning, cognitive performance, and GS (measured with an electronic dynamometer) were conducted in 30 outpatients with type 2 diabetes mellitus, 35 with major depressive disorder, 42 with bipolar disorder, 30 with schizophrenia, and 28 healthy controls, twice during 1-year, follow-up period. Descriptive analyses were conducted using a one-way analysis of variance for continuous variables and the chi-squared test for categorical variables. Differences between groups for the motor, cognitive, and social variables at T1 and T2 were assessed using a one-way analysis of covariance, with sex and age as co-variates (p &lt; 0.01). To test the predictive capacity of GS at baseline to explain the variance in cognitive performance and social functioning at T2, a linear regression analysis was performed (p &lt; 0.05). Results: Predictive relationships were found among GS when implicated with clinical, cognitive, and social variables. These relationships explained changes in cognitive performance after one year of follow-up; the variability percentage was 67.7%, in patients with type-2 diabetes mellitus and 89.1% in patients with schizophrenia. Baseline GS along with other variables, also predicted changes in social functioning in major depressive disorder, bipolar disorder, and schizophrenia, with variability percentages of 67.3, 36, and 59%, respectively. Conclusion: GS combined with other variables significantly predicted changes in cognitive performance and social functioning in people with SMIs or type-2 diabetes mellitus. Interventions aimed to improve the overall physical conditions of patients who have poor GS could be a therapeutic option that confers positive effects on cognitive performance and social functioning.

scholarly journals Transdiagnostic neurocognitive deficits in patients with type 2 diabetes mellitus, major depressive disorder, bipolar disorder, and schizophrenia: A 1-year follow-up study

2021 ◽  
Author(s):  
Patricia Correa-Ghisays ◽  
Joan Vicent Sánchez-Ortí ◽  
Vicent Balanzá-Martínez ◽  
Gabriel Selva-Vera ◽  
Joan Vila-Francés ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Bipolar Disorder ◽  
Type 2 Diabetes Mellitus ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Social Functioning ◽  
Major Depressive

AbstractBackgroundImpairments in neurocognition are critical factors in patients with major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ), and also in those with somatic diseases such as type 2 diabetes mellitus (T2DM). Intriguingly, these severe mental illnesses are associated with an increased co-occurrence of diabetes (direct comorbidity). This study sought to investigate the neurocognition and social functioning across T2DM, MDD, BD, and SZ using a transdiagnostic and longitudinal approach.MethodsA total of 165 subjects, including 30 with SZ, 42 with BD, 35 with MDD, 30 with T2DM, and 28 healthy controls (HC), were assessed twice at a 1-year interval using a comprehensive, integrated test battery on neuropsychological and social functioning.ResultsCommon neurocognitive impairments in somatic and psychiatric disorders were identified, including deficits in short-term memory and cognitive reserve (p < 0.01; η2p = 0.08-0.31). Social functioning impairments were observed in almost all the disorders (p < 0.0001; η2p = 0.29-0.49). Transdiagnostic deficits remained stable across the 1-year follow-up (p < 0.001; η2p = 0.13-0.43) and could accurately differentiate individuals with somatic and psychiatric disorders (χ2 = 48.0, p < 0.0001).ConclusionsThis longitudinal study provides evidence of the overlap in neurocognition deficits across somatic and psychiatric diagnostic categories, such as T2DM, MDD, BD, and SZ, which have high comorbidity. This overlap may be a result of shared genetic and environmental etiological factors. The findings further lay forth promising avenues for research on transdiagnostic phenotypes of neurocognition in these disorders, in addition to their biological bases.

scholarly journals A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jakub Tomasik ◽  
Sung Yeon Sarah Han ◽  
Giles Barton-Owen ◽  
Dan-Mircea Mirea ◽  
Nayra A. Martin-Key ◽  
...  
Keyword(s):  
Mental Health ◽  
Bipolar Disorder ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Diagnostic Algorithm ◽  
Health Questionnaire ◽  
Major Depressive ◽  
Blood Biomarker ◽  
Mental Health Questionnaire ◽  
Biomarker Data

AbstractThe vast personal and economic burden of mood disorders is largely caused by their under- and misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. Here, we aimed to develop a diagnostic algorithm, based on an online questionnaire and blood biomarker data, to reduce the misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD). Individuals with depressive symptoms (Patient Health Questionnaire-9 score ≥5) aged 18–45 years were recruited online. After completing a purpose-built online mental health questionnaire, eligible participants provided dried blood spot samples for biomarker analysis and underwent the World Health Organization World Mental Health Composite International Diagnostic Interview via telephone, to establish their mental health diagnosis. Extreme Gradient Boosting and nested cross-validation were used to train and validate diagnostic models differentiating BD from MDD in participants who self-reported a current MDD diagnosis. Mean test area under the receiver operating characteristic curve (AUROC) for separating participants with BD diagnosed as MDD (N = 126) from those with correct MDD diagnosis (N = 187) was 0.92 (95% CI: 0.86–0.97). Core predictors included elevated mood, grandiosity, talkativeness, recklessness and risky behaviour. Additional validation in participants with no previous mood disorder diagnosis showed AUROCs of 0.89 (0.86–0.91) and 0.90 (0.87–0.91) for separating newly diagnosed BD (N = 98) from MDD (N = 112) and subclinical low mood (N = 120), respectively. Validation in participants with a previous diagnosis of BD (N = 45) demonstrated sensitivity of 0.86 (0.57–0.96). The diagnostic algorithm accurately identified patients with BD in various clinical scenarios, and could help expedite accurate clinical diagnosis and treatment of BD.

scholarly journals Factors associated with the onset of major depressive disorder in adults with type 2 diabetes living in 12 different countries: results from the INTERPRET-DD prospective study

2020 ◽  
Vol 29 ◽  
Author(s):  
C. E. Lloyd ◽  
N. Sartorius ◽  
H. U. Ahmed ◽  
A. Alvarez ◽  
S. Bahendeka ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Major Depressive Disorder ◽  
Depressive Symptoms ◽  
Depressive Disorder ◽  
Life Events ◽  
Stressful Life Events ◽  
Regression Analyses ◽  
Major Depressive

Abstract Aims To examine the factors that are associated with changes in depression in people with type 2 diabetes living in 12 different countries. Methods People with type 2 diabetes treated in out-patient settings aged 18–65 years underwent a psychiatric assessment to diagnose major depressive disorder (MDD) at baseline and follow-up. At both time points, participants completed the Patient Health Questionnaire (PHQ-9), the WHO five-item Well-being scale (WHO-5) and the Problem Areas in Diabetes (PAID) scale which measures diabetes-related distress. A composite stress score (CSS) (the occurrence of stressful life events and their reported degree of ‘upset’) between baseline and follow-up was calculated. Demographic data and medical record information were collected. Separate regression analyses were conducted with MDD and PHQ-9 scores as the dependent variables. Results In total, there were 7.4% (120) incident cases of MDD with 81.5% (1317) continuing to remain free of a diagnosis of MDD. Univariate analyses demonstrated that those with MDD were more likely to be female, less likely to be physically active, more likely to have diabetes complications at baseline and have higher CSS. Mean scores for the WHO-5, PAID and PHQ-9 were poorer in those with incident MDD compared with those who had never had a diagnosis of MDD. Regression analyses demonstrated that higher PHQ-9, lower WHO-5 scores and greater CSS were significant predictors of incident MDD. Significant predictors of PHQ-9 were baseline PHQ-9 score, WHO-5, PAID and CSS. Conclusion This study demonstrates the importance of psychosocial factors in addition to physiological variables in the development of depressive symptoms and incident MDD in people with type 2 diabetes. Stressful life events, depressive symptoms and diabetes-related distress all play a significant role which has implications for practice. A more holistic approach to care, which recognises the interplay of these psychosocial factors, may help to mitigate their impact on diabetes self-management as well as MDD, thus early screening and treatment for symptoms is recommended.

scholarly journals Caregiving Stress Among American Indians With Type 2 Diabetes: The Importance of Awareness of Connectedness and Family Support

2018 ◽  
Vol 24 (4) ◽  
pp. 621-639 ◽  
Author(s):  
Ashleigh Coser ◽  
Kelley J. Sittner ◽  
Melissa L. Walls ◽  
Tina Handeland
Keyword(s):  
Mental Health ◽  
Type 2 Diabetes ◽  
Physical Health ◽  
Family Support ◽  
American Indians ◽  
Diabetes Management ◽  
Cumulative Exposure ◽  
Physical And Mental Health ◽  
Community Connectedness

American Indian (AI) communities experience a disproportionate rate of Type 2 diabetes (T2D) and cumulative exposure to stress. Although this link is well researched among various populations, it has not been examined among AI communities. Path analysis was used to examine a multiple-mediator model to explain how caregiver stress influences self-reported mental and physical health among 100 AI participants with T2D. Caregiver stress was negatively associated with physical and mental health. Physical health was positively associated with family/community connectedness and mental health was positively associated with both family support and connectedness. The relationship between caregiver stress and mental health was partially mediated by family/community connectedness; caregiver stress had no indirect effects on physical health via either hypothesized mediator. Findings demonstrate the importance of integrating individuals’ connection to family and community and its influence on caregiver stress and mental health in intervention programs targeting diabetes management and care among AI communities.

scholarly journals Eleven-year multimorbidity burden among 637 255 people with and without type 2 diabetes: a population-based study using primary care and linked hospitalisation data

BMJ Open ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. e033866
Author(s):  
Salwa S Zghebi ◽  
Douglas T Steinke ◽  
Martin K Rutter ◽  
Darren M Ashcroft
Keyword(s):  
Mental Health ◽  
Type 2 Diabetes ◽  
Cardiovascular Disease ◽  
Population Based ◽  
Mental Illnesses ◽  
Secondary Outcome ◽  
Clinical Practice Research Datalink ◽  
Physical And Mental Health ◽  
Health Comorbidities

ObjectivesTo compare the patterns of 18 physical and mental health comorbidities between people with recently diagnosed type 2 diabetes (T2D) and people without diabetes and how these change by age, gender and deprivation over time between 2004 and 2014. Also, to develop a metric to identify most prevalent comorbidities in people with T2D.DesignPopulation-based cohort study.SettingPrimary and secondary care, England, UK.Participants108 588 people with T2D and 528 667 comparators registered in 391 English general practices. Each patient with T2D aged ≥16 years between January 2004 and December 2014 registered in Clinical Practice Research Datalink GOLD practices was matched to up to five comparators without diabetes on age, gender and general practice.Primary and secondary outcome measuresPrevalence of 18 physical and mental health comorbidities in people with T2D and comparators categorised by age, gender and deprivation. Odds for association between T2D diagnosis and comorbidities versus comparators. A metric for comorbidities with prevalence of ≥5% and/or odds ≥2 in patients with T2D.ResultsOverall, 77% of patients with T2D had ≥1 comorbidity and all comorbidities were more prevalent in patients with T2D than in comparators. Across both groups, prevalence rates were higher in older people, women and those most socially deprived. Conditional logistic regression models fitted to estimate (OR, 95% CI) for association between T2D diagnosis and comorbidities showed that T2D diagnosis was significantly associated with higher odds for all conditions including myocardial infarction (OR 2.13, 95% CI 1.85 to 2.46); heart failure (OR 2.12, 1.84 to 2.43); depression (OR 1.75, 1.62 to 1.89), but non-significant for cancer (OR 1.12, 0.98 to 1.28). In addition to cardiovascular disease, the metric identified osteoarthritis, hypothyroidism, anxiety, schizophrenia and respiratory conditions as highly prevalent comorbidities in people with T2D.ConclusionsT2D diagnosis is associated with higher likelihood of experiencing other physical and mental illnesses. People with T2D are twice as likely to have cardiovascular disease as the general population. The findings highlight highly prevalent and under-reported comorbidities in people with T2D. These findings can inform future research and clinical guidelines and can have important implications on healthcare resource allocation and highlight the need for more holistic clinical care for people with recently diagnosed T2D.

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