Practical diabetes impact factor, Publication award – Students' Scientific Association
BDMM onset time dependence.
BDMM of depression and metabolic disorders and hypertension. S5 Fig. The high posteriors in the full analysis, and their sharp decrease in the restricted analysis may indicate that these disorders are heterogeneous themselves: in some subgroups of disorder the symptoms are part of the depression phenotype with high biological overlap but other subgroups maybe independent of depression or adversities that non-specifically predispose to depression. Discussion Large-scale cohort studies collecting life style, environmental, physiological, clinical and molecular level data, provide unprecedented opportunity for understanding health, pre-disease states, multimorbid conditions and progressions, especially to use epidemilogical level information to complement molecular level discoveries [ 238 — 1347 ].
However, the hypothesis-free, omic level use of comorbidites is hindered by multiple factors, such as by errors and biases in disease coding and collection of clinical information and by confounders like therapies, drug consumption cukor cukorbetegség kezelése 1 2 paradoxically the shared genetic factors themselves.
Volume 59 Issue 10
A further imminent challenge is the presence of disease-mediated or disease-confounded comorbidity relations, i. In network science, algebraic solutions were proposed to attenuate indirect relations [ 19 ], but these solutions do not take into account the complex system of probabilistic dependencies between morbidities. We proposed to use probabilistic graphical models, specifically Bayesian networks to discriminate direct and indirect relations, because their semantics perfectly captures this aspect [ 434849 ].
Indeed, the exact probabilistic treatment of a direct relation with respect to a given set of variables relies on the practical assumption of stability [ 4850 ], less demanding than assumptions for a causal interpretation [ 51 ]. Additionally, we examined the comorbidity relations of psychiatric and metabolic disorders, specifically for depression in BDMMs.
In Fig 6 we further evaluated the connections with significant Bonferroni-corrected χ2 p-values but below the 0. This shows that most of these connections have high BDMM structural association posterior, which suggest that BDMM indeed filtered mediated and confounded relations.
These disorder pairs rarely occur together in patients mean co-occurrence For detailed description of the molecular level methods and results see S1 Appendix and [ 216 ]. Our results showed another expected aspect, namely that this relationship is independent of the order of the onset of these disorders.
This observation is in line with a longitudinal study which showed that generalised anxiety disorder GAD and major depressive disorder MDD are strongly comorbid with an equal probability of GAD or MDD occurring first or simultaneously suggesting they might not be distinct disorders [ 54 ].
Although overlapping genetic risk factors for anxiety and depression have not yet been identified, common genetic vulnerability has been found for other comorbid psychiatric disorders [ 323355 ]. Multimorbidity pattern of metabolic disorders and hypertension with depression Epidemiologic studies repeatedly report high comorbidity between depression and diabéteszes láb pedikür disorders [ 57 ], depression and diabetes [ 58 ], depression and cardiovascular disorders [ 37 ], depression and hypertension [ 5960 ], and depression and obesity [ 6162 ].
Treatment and Management of Type 2 Diabetes
However, there have been several contradictory results, and this suggests a more complex relationship. Indeed, recent GWAS results found no shared genetic risk between these disorders and depression [ 55 ].
When we excluded occurrences after the onset of depression the direct relationship between obesity and depression remained as expected but entirely new links with high posterior probability emerged suggesting a strong relationship between the consequences of metabolic syndromes and depression. Studies of the genetic relationship between obesity and depression suggest that atypical depression, characterised by increase in appetite and weight, is associated with genetic risk factors and polygenic risk scores of increased BMI and triglycerides, while typical depression, with decreased appetite and weight, show more similarities with other psychiatric disorders [ 6364 ].
Thus, in line with our results comorbid obesity and metabolic disorders may identify a specific subtype of depression with a distinct biological background. We cannot currently exclude the possibility that lifestyle factors, such as diet, physical activity and stress, or medication used to treat hypertension, hypercholesterolaemia and obesity may contribute to the later development of depression [ 65 ].
Hypoglycaemia in Clinical Diabetes
As a specific example, a previous study demonstrated that current psychological distress amplified the effect of genetic risk of high BMI [ 66 ]. Patients with increased genetic risk to become overweight showed worse physical outcome higher BMIand quite probably more comorbid psychological symptoms, when life stress was present. Furthermore, it has been reported that statins, drugs with cholesterol-lowering effect, have antidepressant effect in patients with comorbid depression and coronary artery disease while the same drugs can have pro-depressive effect or no effect on depression when comorbidities and depression subtypes were not taken into account [ 67 ].
It is therefore puzzling that they involve different etiological mechanisms.
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In addition, their symptoms often overlap making it difficult to apply diagnostic categories. The probable explanation is that in general, these disorders are related to consequences of depression and only specific subtypes of these disorders can be expected practical diabetes impact factor have causal relations, e.
For example, a genetic risk score analysis demonstrated that migraine with comorbid depression was more genetically related to depression than to pure migraine, which suggests that migraine might develop as a consequence of different polygenic backgrounds [ 71 ].
Limitations One of the main limitations is that all disorders were self-reported, although trained nurses evaluated and corrected all entries during face-to-face interviews. The second one is that the applied treatments or medications were not included in the analysis which could highlight comorbidities due to the side effect of treatments.
We will address this problem in follow-up studies. Note that we only used a subset of the UK Biobank dataset selecting those participants who filled out the Mental Health Questionnaire and provided online dietary information, which may introduce confounding through selection bias.
However, limiting our study to this subpopulation enabled us to test different definitions of depression and will allow us to connect this comorbidity network to relevant environmental risk factors.
Conclusion The use of large-scale health data sets, such as the UK Biobank dataset hold the promise of complementing and practical diabetes impact factor the molecular level research of complex diseases. Adopting an intermediary approach between statistical association analysis and causal discovery we investigated the use of Bayesian networks in the Bayesian model averaging framework to explore direct probabilistic relations with respect to a given set of variables, i. We demonstrated the applicability of BDMMs, especially their principled capability of discriminating direct and indirect comorbidities.
In summary, PGMs practical diabetes impact factor maximally sparse dependency models and utilize the omic nature of the epidemiologic data jointly modelling all the morbidities; while the Bayesian approach through posteriors provides an explicit representation for the uncertainties in a dataset.