Quantification of diabetes comorbidity risks across life using nation-wide big claims data

Impaired skeletal muscle regeneration in diabetes: From cellular and molecular mechanisms to novel treatments

Diabetes represents a major public health concern with a considerable impact on human life and healthcare expenditures. It is now well established that diabetes is characterized by a severe skeletal muscle pathology that limits functional capacity and quality of life. Increasing evidence indicates that diabetes is also one of the most prevalent disorders characterized by impaired skeletal muscle regeneration, yet underlying mechanisms and therapeutic treatments remain poorly established. In this review, we describe the cellular and molecular alterations currently known to occur during skeletal muscle regeneration in people with diabetes and animal models of diabetes, including its associated comorbidities, e.g., obesity, hyperinsulinemia, and insulin resistance. We describe the role of myogenic and non-myogenic cell types on muscle regeneration in conditions with or without diabetes. Therapies for skeletal muscle regeneration and gaps in our knowledge are also discussed, while proposing future directions for the field.

The Contribution of Type 2 Diabetes to Parkinson's Disease Aetiology

Type 2 diabetes (T2D) and Parkinson's disease (PD) are chronic disorders that have a significant health impact on a global scale. Epidemiological, preclinical, and clinical research underpins the assumption that insulin resistance and chronic inflammation contribute to the overlapping aetiologies of T2D and PD. This narrative review summarises the recent evidence on the contribution of T2D to the initiation and progression of PD brain pathology. It also briefly discusses the rationale and potential of alternative pharmacological interventions for PD treatment.

Association between Type 1 Diabetes Mellitus and Parkinson's Disease: A Mendelian Randomization Study

While much evidence suggests that type 2 diabetes mellitus increases the risk of Parkinson's disease (PD), the relationship between type 1 diabetes mellitus (T1DM) and PD is unclear. To study their association, we performed a two-sample Mendelian randomization (MR) using the following statistical methods: inverse variance weighting (IVW), MR-Egger, weight median, and weighted mode. Independent datasets with no sample overlap were retrieved from the IEU GWAS platform. All the MR methods found a lower risk of PD in T1DM (IVW-OR 0.93, 95% CI 0.91-0.96, p = 3.12 × 10-5; MR-Egger-OR 0.93, 95% CI 0.88-0.98, p = 1.45 × 10-2; weighted median-OR 0.93, 95% CI 0.89-0.98, p = 2.76 × 10-3; and weighted mode-OR 0.94, 95% CI 0.9-0.98, p = 1.58 × 10-2). The findings were then replicated with another independent GWAS dataset on T1DM (IVW-OR 0.97, 95% CI 0.95-0.99, p = 3.10 × 10-3; MR-Egger-OR 0.96, 95% CI 0.93-0.99, p = 1.08 × 10-2; weighted median-OR 0.97, 95% CI 0.94-0.99, p = 1.88 × 10-2; weighted mode-OR 0.97, 95% CI 0.94-0.99, p = 1.43 × 10-2). Thus, our study provides evidence that T1DM may have a protective effect on PD risk, though further studies are needed to clarify the underlying mechanisms.

Diabetes and Parkinson's Disease: Understanding Shared Molecular Mechanisms

Aging is a major risk factor for Parkinson's disease (PD). Genetic mutations account for a small percentage of cases and the majority appears to be sporadic, with yet unclear causes. However, various environmental factors have been linked to an increased risk of developing PD and, therefore, understanding the complex interplay between genetic and environmental factors is crucial for developing effective disease-modifying therapies. Several studies identified a connection between type 2 diabetes (T2DM) and PD. T2DM is characterized by insulin resistance and failure of β-cells to compensate, leading to hyperglycemia and serious comorbidities. Both PD and T2DM share misregulated processes, including mitochondrial dysfunction, oxidative stress, chronic inflammation, altered proteostasis, protein aggregation, and misregulation of glucose metabolism. Chronic or recurring hyperglycemia is a T2DM hallmark and can lead to increased methylglyoxal (MGO) production, which is responsible for protein glycation. Glycation of alpha-synuclein (aSyn), a central player in PD pathogenesis, accelerates the deleterious aSyn effects. Interestingly, MGO blood plasma levels and aSyn glycation are significantly elevated in T2DM patients, suggesting a molecular mechanism underlying the T2DM - PD link. Compared to high constant glucose levels, glycemic variability (fluctuations in blood glucose levels), can be more detrimental for diabetic patients, causing oxidative stress, inflammation, and endothelial damage. Accordingly, it is imperative for future research to prioritize the exploration of glucose variability's influence on PD development and progression. This involves moving beyond the binary classification of patients as diabetic or non-diabetic, aiming to pave the way for the development of enhanced therapeutic interventions.

Evaluation of type 1 diabetes mellitus as a risk factor of Parkinson's disease in a Drosophila model

Diabetes mellitus (DM) is a chronic metabolic disease characterized by high blood glucose levels, resulting from insulin dysregulation. Parkinson's disease (PD) is the most common neurodegenerative motor disorder caused by the selective loss of dopaminergic (DA) neurons in the substantia nigra pars compacta. DM and PD are both age-associated diseases that are turning into epidemics worldwide. Previous studies have indicated that type 2 DM might be a risk factor of developing PD. However, scarce information about the link between type 1 DM (T1DM) and PD does exist. In this work, we have generated a Drosophila model of T1DM based on insulin deficiency to evaluate if T1DM could be a risk factor to trigger PD onset. As expected, model flies exhibited T1DM-related phenotypes such as insulin deficiency, increased content of carbohydrates and glycogen, and reduced activity of insulin signaling. Interestingly, our results also demonstrated that T1DM model flies presented locomotor defects as well as reduced levels of tyrosine hydroxylase (a marker of DA neurons) in brains, which are typical PD-related phenotypes. In addition, T1DM model flies showed elevated oxidative stress levels, which could be causative of DA neurodegeneration. Therefore, our results indicate that T1DM might be a risk factor of developing PD, and encourage further studies to shed light into the exact link between both diseases.

Glucose Metabolism Impairment in Parkinson's Disease

Impairments in systematic and regional glucose metabolism exist in patients with Parkinson's disease (PD) at every stage of the disease course, and such impairments are associated with the incidence, progression, and special phenotypes of PD, which affect each physiological process of glucose metabolism including glucose uptake, glycolysis, tricarboxylic acid cycle, oxidative phosphorylation, and pentose phosphate shunt pathway. These impairments may be attributed to various mechanisms, such as insulin resistance, oxidative stress, abnormal glycated modification, blood-brain-barrier dysfunction, and hyperglycemia-induced damages. These mechanisms could subsequently cause excessive methylglyoxal and reactive oxygen species production, neuroinflammation, abnormal aggregation of protein, mitochondrial dysfunction, and decreased dopamine, and finally result in energy supply insufficiency, neurotransmitter dysregulation, aggregation and phosphorylation of α-synuclein, and dopaminergic neuron loss. This review discusses the glucose metabolism impairment in PD and its pathophysiological mechanisms, and briefly summarized the currently-available therapies targeting glucose metabolism impairment in PD, including glucagon-likepeptide-1 (GLP-1) receptor agonists and dual GLP-1/gastric inhibitory peptide receptor agonists, metformin, and thiazoledinediones.

Potential Protective Link Between Type I Diabetes and Parkinson's Disease Risk and Progression

BACKGROUND

Epidemiological studies suggested an association between Parkinson's disease (PD) and type 2 diabetes, but less is known about type 1 diabetes (T1D) and PD.

OBJECTIVE

This study sought to explore the association between T1D and PD.

METHODS

We used Mendelian randomization, linkage disequilibrium score regression, and multi-tissue transcriptome-wide analysis to examine the association between PD and T1D.

RESULTS

Mendelian randomization showed a potentially protective role of T1D for PD risk (odds ratio [OR], 0.97; 95% confidence interval [CI], 0.94-0.99; P = 0.039), as well as motor (OR, 0.94; 95% CI, 0.88-0.99; P = 0.044) and cognitive progression (OR, 1.50; 95% CI, 1.08-2.09; P = 0.015). We further found a negative genetic correlation between T1D and PD (rg = -0.17; P = 0.016), and we identified eight genes in cross-tissue transcriptome-wide analysis that were associated with both traits.

CONCLUSIONS

Our results suggest a potential genetic link between T1D and PD risk and progression. Larger comprehensive epidemiological and genetic studies are required to validate our findings. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Systematic comparisons between Lyme disease and post-treatment Lyme disease syndrome in the U.S. with administrative claims data

BACKGROUND

Post-treatment Lyme disease syndrome (PTLDS) is used to describe Lyme disease patients who have the infection cleared by antibiotic but then experienced persisting symptoms of pain, fatigue, or cognitive impairment. Currently, little is known about the cause or epidemiology of PTLDS.

METHODS

We conducted a data-driven study with a large nationwide administrative dataset, which consists of more than 98 billion billing and 1.4 billion prescription records between 2008 and 2016, to identify unique aspects of PTLDS that could have diagnostic and etiologic values. We defined PTLDS based on its symptomatology and compared the demographic, longitudinal changes of comorbidity, and antibiotic prescriptions between patients who have Lyme with absence of prolonged symptoms (APS) and PTLDS.

FINDINGS

The age and temporal distributions were similar between Lyme APS and PTLDS. The PTLDS-to-Lyme APS case ratio was 3.42%. The co-occurrence of 3 out of 19 chronic conditions were significantly higher in PTLDS versus Lyme APS-odds ratio and 95% CI for anemia, hyperlipidemia, and osteoarthrosis were 1.46 (1.11-1.92), 1.39 (1.15-1.68), and 1.62 (1.23-2.12) respectively. We did not find significant differences between PTLDS and Lyme APS for the number of types of antibiotics prescribed (incidence rate ratio = 1.009, p = 0.90) and for the prescription of each of the five antibiotics (FDR adjusted p values 0.72-0.95).

INTERPRETATION

PTLDS cases have more codes corresponding to anemia, hyperlipidemia, and osteoarthrosis compared to Lyme APS. Our finding of hyperlipidemia is consistent with a dysregulation of fat metabolism reported by other researchers, and further investigation should be conducted to understand the potential biological relationship between the two.

FUNDING

Steven & Alexandra Cohen Foundation, Global Lyme Alliance, and the Pazala Foundation; National Institutes of Health R01ES032470.

Hormonal regulation of metabolism-recent lessons learned from insulin and estrogen

Hormonal signaling plays key roles in tissue and metabolic homeostasis. Accumulated evidence has revealed a great deal of insulin and estrogen signaling pathways and their interplays in the regulation of mitochondrial, cellular remodeling, and macronutrient metabolism. Insulin signaling regulates nutrient and mitochondrial metabolism by targeting the IRS-PI3K-Akt-FoxOs signaling cascade and PGC1α. Estrogen signaling fine-tunes protein turnover and mitochondrial metabolism through its receptors (ERα, ERβ, and GPER). Insulin and estrogen signaling converge on Sirt1, mTOR, and PI3K in the joint regulation of autophagy and mitochondrial metabolism. Dysregulated insulin and estrogen signaling lead to metabolic diseases. This article reviews the up-to-date evidence that depicts the pathways of insulin signaling and estrogen-ER signaling in the regulation of metabolism. In addition, we discuss the cross-talk between estrogen signaling and insulin signaling via Sirt1, mTOR, and PI3K, as well as new therapeutic options such as agonists of GLP1 receptor, GIP receptor, and β3-AR. Mapping the molecular pathways of insulin signaling, estrogen signaling, and their interplays advances our understanding of metabolism and discovery of new therapeutic options for metabolic disorders.

Activated AMPK mitigates diabetes-related cognitive dysfunction by inhibiting hippocampal ferroptosis

Clinical and preclinical interest in Type 2 diabetes (T2D)-associated cognitive dysfunction (TDACD) has grown in recent years. However, the precise mechanisms underlying TDACD need to be further elucidated. Ferroptosis was reportedly involved in neurodegenerative diseases and diabetes-related organ injuries; however, its role in TDACD remains elusive. In this study, mice fed with a high-fat-diet combined with streptozotocin (HFD-STZ) were used as a T2D model to assess the role of ferroptosis in cognitive dysfunction. We found that ferroptosis was mainly activated in hippocampal neurons but not in microglia or astrocytes. Accordingly, increased levels of transferrin receptor and decreased levels of ferritin, GPX4, and SLC7A11 were observed in hippocampal neurons. In addition, pre-treatment with liproxstatin-1, a ferroptosis inhibitor, attenuated iron accumulation and oxidative stress response, which resulted in improved cognitive function in the HFD-STZ group. Furthermore, we found that p-AMP-activated protein kinase (AMPK) was decreased in the HFD-STZ group. Pre-treatment with AMPK agonist increased the expression of AMPK and GPX4, but decreased lipocalin 2 (LCN2) in the hippocampus that resulted in improved spatial learning ability in the HFD-STZ group. Taken together, we found that activation of neuronal ferroptosis in the hippocampus contributed to cognitive impairment of HFD-STZ mice. Furthermore, AMPK activation may reduce hippocampal ferroptosis, and consequently improve cognitive performance in diabetic mice.

Discovering comorbid diseases using an inter-disease interactivity network based on biobank-scale PheWAS data

MOTIVATION

Understanding comorbidity is essential for disease prevention, treatment and prognosis. In particular, insight into which pairs of diseases are likely or unlikely to co-occur may help elucidate the potential relationships between complex diseases. Here, we introduce the use of an inter-disease interactivity network to discover/prioritize comorbidities. Specifically, we determine disease associations by accounting for the direction of effects of genetic components shared between diseases, and categorize those associations as synergistic or antagonistic. We further develop a comorbidity scoring algorithm to predict whether diseases are more or less likely to co-occur in the presence of a given index disease. This algorithm can handle networks that incorporate relationships with opposite signs.

RESULTS

We finally investigate inter-disease associations among 427 phenotypes in UK Biobank PheWAS data and predict the priority of comorbid diseases. The predicted comorbidities were verified using the UK Biobank inpatient electronic health records. Our findings demonstrate that considering the interaction of phenotype associations might be helpful in better predicting comorbidity.

AVAILABILITY AND IMPLEMENTATION

The source code and data of this study are available at https://github.com/dokyoonkimlab/DiseaseInteractiveNetwork.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The association between Parkinson's disease and autoimmune diseases: A systematic review and meta-analysis

Parkinson's disease (PD) is a neurodegenerative disorder that frequently occurs in the older population. Previous epidemiological studies have suggested an association between PD and autoimmune diseases (AIDs). However, some studies have shown conflicting results. This study aimed to summarize existing epidemiological studies on the association between PD with AIDs and to conduct a meta-analysis of combinable results. Four electronic databases (PubMed, Embase, Web of Science Core Collection, and MEDLINE) were searched from each database's inception date until December 12, 2022. All studies that explored the relationship between PD and AIDs were included for quantitative analysis and qualitative review. The pooled relative risk with 95% confidence intervals (CIs) was calculated using a random or fixed effects model. A total of 46 observational studies involving 873,643 patients and 13,402,821 controls were included; ultimately, 38 studies were included in the meta-analysis. The risk of PD combined with AIDs was significantly higher (odds ratio [OR]=1.55, 95% CI: 1.33-1.81), and subgroup analysis found no significant differences in risk by study type, gender, age, and race. Regarding the AID types, the results showed an increased risk of PD combined with bullous pemphigoid (OR=2.67, 95% CI: 2.15-3.31), inflammatory bowel disease (OR=1.30, 95% CI: 1.18-1.45), Crohn's disease (OR=1.30, 95% CI: 1.20-1.42), ulcerative colitis (OR=1.31, 95% CI: 1.14-1.50), Sjögren's syndrome (OR=1.61, 95% CI: 1.24-2.09), and Graves' disease (OR=1.45, 95% CI: 1.24-1.70) than controls. However, there appeared to be no significant association between PD and systemic lupus erythematosus (OR=0.82, 95% CI: 0.66-1.03), multiple sclerosis (OR=2.02, 95% CI: 0.87-4.70), rheumatoid arthritis (OR=0.79, 95% CI: 0.61-1.03), or celiac disease (OR=1.16, 95% CI: 0.79-1.69). This study supports the existence of a strong link between AIDs and PD. When PD and AIDs are identified, clinicians need to be aware of the possibility of coexistence. However, there are some limitations of this study, such as the apparent heterogeneity of some of the results and the fact that most of the included study types were retrospective. Therefore, future larger prospective cohort studies are needed to further explore the interaction between PD and AIDs.

Systematic review registration

INPLASY, identifier INPLASY202280088.

Potential Clinical Applications for Continuous Ketone Monitoring in the Hospitalized Patient with Diabetes

PURPOSE OF REVIEW

In this review, the authors discuss potential clinical applications for continuous ketone monitoring (CKM) in a broad continuum of clinical settings from pre-hospital care and the emergency department to acute inpatient management and post-discharge follow-up.

RECENT FINDINGS

Though in its early stages, the concept of a novel continuous ketone sensing technology exerts great potential for use in the detection and hospital management of DKA, namely to overcome diagnostic barriers associated with ketoacidosis in patients with diabetes and obtain real-time BOHB levels, which may be useful in understanding both patients' response to treatment and DKA trajectory. Peri- and intra-operative use of CKM technology can potentially be applied in a number of urgent and elective surgical procedures frequently underwent by patients with diabetes and in the observation of patients during peri-operative fasting. In transitional care management, CKM technology could potentially facilitate patients' safe transition through levels of care, following hospital discharge from a DKA episode. This evaluation of the literature presents the potential advantages of adopting CKM and integrating this technology into the care algorithm of patients at risk for ketoacidosis.

Examining Disease Multimorbidity in U.S. Hospital Visits Before and During COVID-19 Pandemic: A Graph Analytics Approach

Enduring effects of the COVID-19 pandemic on healthcare systems can be preempted by identifying patterns in diseases recorded in hospital visits over time. Disease multimorbidity or simultaneous occurrence of multiple diseases is a growing global public health challenge as populations age and long-term conditions become more prevalent. We propose a graph analytics framework for analyzing disease multimorbidity in hospital visits. Within the framework, we propose a graph model to explain multimorbidity as a function of prevalence, category, and chronic nature of the underlying disease. We apply our model to examine and compare multimorbidity patterns in public hospitals in Arizona, U.S., during five six-month time periods before and during the pandemic. We observe that while multimorbidity increased by 34.26% and 41.04% during peak pandemic for mental disorders and respiratory disorders respectively, the gradients for endocrine diseases and circulatory disorders were not significant. Multimorbidity for acute conditions is observed to be decreasing during the pandemic while multimorbidity for chronic conditions remains unchanged. Our graph analytics framework provides guidelines for empirical analysis of disease multimorbidity using electronic health records. The patterns identified using our proposed graph model informs future research and healthcare policy makers for pre-emptive decision making.

Parkinson's disease and diabetes mellitus: common mechanisms and treatment repurposing

In the last decade, attention has become greater to the relationship between neurodegeneration and abnormal insulin signaling in the central nervous system, as insulin in the brain is implicated in neuronal survival, plasticity, oxidative stress and neuroinflammation. Diabetes mellitus and Parkinson’s disease are both aging-associated diseases that are turning into epidemics worldwide. Diabetes mellitus and insulin resistance not only increase the possibility of developing Parkinson’s disease but can also determine the prognosis and progression of Parkinsonian symptoms. Today, there are no available curative or disease modifying treatments for Parkinson’s disease, but the role of insulin and antidiabetic medications in neurodegeneration opens a door to treatment repurposing to fight against Parkinson’s disease, both in diabetic and nondiabetic Parkinsonian patients. Furthermore, it is essential to comprehend how a frequent and treatable disease such as diabetes can influence the progression of neurodegeneration in a challenging disease such as Parkinson’s disease. Here, we review the present evidence on the connection between Parkinson’s disease and diabetes and the consequential implications of the existing antidiabetic molecules in the severity and development of Parkinsonism, with a particular focus on glucagon-like peptide-1 receptor agonists.

Frequency of depression and quality of life in patients with diabetes mellitus in public health facilities in Metropolitan Lima

INTRODUCTION

Diabetes is one of the main pandemics in recent years. Its association with depression increases the risk of mortality and morbidity. The coexistence of both diseases leads to poor management of diabetes, which leads to a worse quality of life.

OBJECTIVE

To determine the frequency of depression in patients with diabetes mellitus and the effect of both pathologies on the quality of life in patients who attend outpatient appointments at public health facilities in Lima and Callao.

METHODOLOGY

Secondary analysis of the Epidemiological Study of Mental Health of depression in diabetic adults. The instrument used to determine the depressive episode was the MINI (Mini-International Neuropsychiatric Interview) while quality of life was measured using the Mezzich Quality of Life Index. Diagnosis information of type 1 or 2 diabetes was obtained from the daily medical record (HIS) of care.

RESULTS

The frequency of depression in the 471 patients with diabetes was 5.8% in the last two weeks. While the annual frequency was 8.6% and 31.8% at some point in life. Being a woman was associated with a greater frequency of depression. Quality of life was lower in patients with diabetes and depression (p < 0.005).

CONCLUSIONS

The frequency of depression in patients with diabetes who are treated on an outpatient basis in public health centres is higher than the general population and their quality of life is significantly reduced, which raises the need for considering depression as an additional factor to the burden of morbidity of this condition.

Elucidate multidimensionality of type 1 diabetes mellitus heterogeneity by multifaceted information

Type 1 diabetes (T1D) is an autoimmune disease. Different factors, including genetics and viruses may contribute to T1D, but the causes of T1D are not fully known, and there is currently no cure. The advent of high-throughput technologies has revolutionized the field of medicine and biology, and analysis of multi-source data along with clinical information has brought a better understanding of the mechanisms behind disease pathogenesis. The aim of this work was the development of a data repository linking clinical information and interactome studies in T1D. To address this goal, we analyzed the electronic health records and online databases of genes, proteins, miRNAs, and pathways to have a global view of T1D. There were common comorbid diseases such as anemia, hypertension, vitreous diseases, renal diseases, and atherosclerosis in the phenotypic disease networks. In the protein-protein interaction network, CASP3 and TNF were date-hub proteins involved in several pathways. Moreover, CTNNB1, IGF1R, and STAT3 were hub proteins, whereas miR-155-5p, miR-34a-5p, miR-23-3p, and miR-20a-5p were hub miRNAs in the gene-miRNA interaction network. Multiple levels of information including genetic, protein, miRNA and clinical data resulted in multiple results, which suggests the complementarity of multiple sources. With the integration of multifaceted information, it will shed light on the mechanisms underlying T1D; the provided data and repository has utility in understanding phenotypic disease networks for the potential development of comorbidities in T1D patients as well as the clues for further research on T1D comorbidities.

Diabetes, insulin and new therapeutic strategies for Parkinson's disease: Focus on glucagon-like peptide-1 receptor agonists

Parkinson's disease and diabetes mellitus are two chronic disorders associated with aging that are becoming increasingly prevalent worldwide. Parkinson is a multifactorial progressive condition with no available disease modifying treatments at the moment. Over the last few years there is growing interest in the relationship between diabetes (and impaired insulin signaling) and neurodegenerative diseases, as well as the possible benefit of antidiabetic treatments as neuroprotectors, even in non-diabetic patients. Insulin regulates essential functions in the brain such as neuronal survival, autophagy of toxic proteins, synaptic plasticity, neurogenesis, oxidative stress and neuroinflammation. We review the existing epidemiological, experimental and clinical evidence that supports the interplay between insulin and neurodegeneration in Parkinson's disease, as well as the role of antidiabetic treatments in this disease.

Effects of antipsychotics on circadian rhythms in humans: a systematic review and meta-analysis

Antipsychotics are widely used to treat psychiatric illness and insomnia. However, the etiology of insomnia is multifactorial, including disrupted circadian rhythms. Several studies show that antipsychotics might modulate even healthy circadian rhythms. The purpose of this systematic review is to integrate current knowledge about the effects of antipsychotics on the circadian rhythms in humans, and to conduct a meta- analysis with the available data. Nine electronic databases were searched. We followed the PRISMA guidelines and included randomized controlled trials (RCTs), non-RCTs, case-control studies, case series, and case reports. Of 7,217 articles, 70 were included. The available data was mainly from healthy individuals, or patients having schizophrenia, but the findings showed a transdiagnostic impact on circadian parameters. This was consistently seen as decreased amplitude of cortisol, melatonin, and body temperature. Particularly, a meta-analysis of 16 RCTs measuring cortisol rhythm showed that antipsychotics, especially atypical antipsychotics, decreased the cortisol area under the curve and morning cortisol level, compared to placebo. The data with melatonin or actigraphy was limited. Overall, this evidence about the circadian effect of antipsychotics showed a need for longitudinal, real-time monitoring of specific circadian markers to differentiate a change in amplitude from a shift in phasing, and for knowledge about optimal timing of administration of antipsychotics, according to individual baseline circadian parameters. Standardizing selection criteria and outcome methods could facilitate good quality intervention studies and evidence-based treatment guidelines. This is relevant considering the accumulating evidence of the high prevalence and unfavorable impact of disrupted circadian rhythms in psychiatric disorders.

Diabetes Mellitus is Associated with a Higher Relative Risk for Parkinson’s Disease in Women than in Men

Background: In general, the risk to develop Parkinson’s disease (PD) is higher in men compared to women. Besides male sex and genetics, research suggests diabetes mellitus (DM) is a risk factor for PD as well. Objective: In this population-level study, we aimed at investigating the sex-specific impact of DM on the risk of developing PD. Methods: Medical claims data were analyzed in a cross-sectional study in the Austrian population between 1997 and 2014. In the age group of 40–79 and 80+, 235,268 patients (46.6%females, 53.4%males) with DM were extracted and compared to 1,938,173 non-diabetic controls (51.9%females, 48.1%males) in terms of risk of developing PD. Results: Men with DM had a 1.46 times increased odds ratio (OR) to be diagnosed with PD compared to non-diabetic men (95%CI 1.38–1.54, p < 0.001). The association of DM with newly diagnosed PD was significantly greater in women (OR = 1.71, 95%CI 1.60–1.82, p < 0.001) resulting in a relative risk increase of 1.17 (95%CI 1.11–1.30) in the age group 40 to 79 years. In 80+-year-olds the relative risk increase is 1.09 (95%CI 1.01–1.18). Conclusion: Although men are more prone to develop PD, women see a higher risk increase in PD than men amongst DM patients.

How Specialist Aftercare Impacts Long-Term Readmission Risks in Elderly Patients With Metabolic, Cardiac, and Chronic Obstructive Pulmonary Diseases: Cohort Study Using Administrative Data

BACKGROUND

The health state of elderly patients is typically characterized by multiple co-occurring diseases requiring the involvement of several types of health care providers.

OBJECTIVE

We aimed to quantify the benefit for multimorbid patients from seeking specialist care in terms of long-term readmission risks.

METHODS

From an administrative database, we identified 225,238 elderly patients with 97 different diagnosis (ICD-10 codes) from hospital stays and contact with 13 medical specialties. For each diagnosis associated with the first hospital stay, we used multiple logistic regression analysis to quantify the sex-specific and age-adjusted long-term all-cause readmission risk (hospitalizations occurring between 3 months and 3 years after the first admission) and how specialist contact impacts these risks.

RESULTS

Men have a higher readmission risk than women (mean difference over all first diagnoses 1.9%, P<.001), but similar reduction in readmission risk after receiving specialist care. Specialist care can reduce readmission risk by almost 50%. We found the greatest reductions in risk when the first hospital stay was associated with diagnoses corresponding to complex chronic diseases such as acute myocardial infarction (57.6% reduction in readmission risk, SE 7.6% for men [m]; 55.9% reduction, SE 9.8% for women [w]), diabetic and other retinopathies (m: 62.3%, SE 8.0; w: 60.1%, SE 8.4%), chronic obstructive pulmonary disease (m: 63.9%, SE 7.8%; w: 58.1%, SE 7.5%), disorders of lipoprotein metabolism (m: 64.7%, SE 3.7%; w: 63.8%, SE 4.0%), and chronic ischemic heart diseases (m: 63.6%, SE 3.1%; w: 65.4%, SE 3.0%).

CONCLUSIONS

Specialist care can greatly reduce long-term readmission risk for patients with chronic and multimorbid diseases. Further research is needed to identify the specific reasons for these findings and to understand the detected sex-specific differences.

Frequency of Depression and Quality of Life in Patients with Diabetes Mellitus in Public Health Facilities in Metropolitan Lima

INTRODUCTION

Diabetes is one of the main pandemics in recent years. Its association with depression increases the risk of mortality and morbidity. The coexistence of both diseases leads to poor management of diabetes, which leads to a worse quality of life.

OBJECTIVE

To determine the frequency of depression in patients with diabetes mellitus and the effect of both pathologies on the quality of life in patients who attend outpatient appointments at public health facilities in Lima and Callao.

METHODOLOGY

Secondary analysis of the Epidemiological Study of Mental Health of depression in diabetic adults. The instrument used to determine the depressive episode was the MINI (Mini-International Neuropsychiatric Interview) while quality of life was measured using the Mezzich Quality of Life Index. Diagnosis information of type 1 or 2 diabetes was obtained from the daily medical record (HIS) of care.

RESULTS

The frequency of depression in the 471 patients with diabetes was 5.8% in the last two weeks. While the annual frequency was 8.6% and 31.8% at some point in life. Being a woman was associated with a greater frequency of depression. Quality of life was lower in patients with diabetes and depression (p <0.005).

CONCLUSIONS

The frequency of depression in patients with diabetes who are treated on an outpatient basis in public health centres is higher than the general population and their quality of life is significantly reduced, which raises the need for considering depression as an additional factor to the burden of morbidity of this condition.

Drivers for the comorbidity of type 2 diabetes mellitus and epilepsy: A scoping review

Epilepsy, a common neurologic condition, is associated with a greater prevalence of type 2 diabetes mellitus (T2DM). We examined potential drivers for the comorbidity of epilepsy and T2DM in an attempt to elucidate possible biological mechanisms underlying the development of processes in individuals. We searched PubMed and Medline up to December 2019. Our search yielded 3361 articles, of which 82 were included in the scoping review. We reviewed articles focusing on the association of epilepsy and T2DM, drivers, and biological mechanisms. We found that epilepsy is associated with obesity and obesity is associated with T2DM. Treatment with valproate (either sodium or acid) is associated with weight increase and hyperinsulinemia, while topiramate causes weight loss. People with epilepsy are less likely to exercise, which is protective against obesity. Mitochondrial dysfunction and adiponectin deficiency are common to epilepsy and T2DM. One possible mechanism for the comorbidity is mitochondrial dysfunction and adiponectin deficiency, which promotes epilepsy, obesity, and T2DM. Another possible mechanism is that people with epilepsy are more likely to be obese because of the lack of exercise and the effects of some antiseizure medications (ASMs), which makes them susceptible to T2DM because of the development of mitochondrial dysfunction and adiponectin deficiency. A third mechanism is that people with epilepsy have greater mitochondrial dysfunction and lower adiponectin levels than people without epilepsy at baseline, which may exacerbate after treatment with ASMs. Future research involving a combined genetic and molecular pathway approach will likely yield valuable insight regarding the comorbidity of epilepsy and T2DM.

Identification of bioactive constituents in Coldenia procumbens L. and its antidiabetic activity against streptozotocin induced Wistar albino rats

Background Type II diabetes, a multifactorial progressive disorder is the prime concern of the twenty-first century. Modern medicine is proven effective in delaying the effects of diabetes. However, the side effects are amplified over time. In order to find relief from side effects, people are rigorously searching for alternative treatment. Objective In this study, we aim to identify the bioactive components in the Coldenia procumbens L. and assess its anti-diabetic effect. Materials and Methods Initially, the plant was extracted using chloroform and methanol. Both the extracts were analysed using IR Spectrum and NMR. The methanol extract of Coldenia procumbens L. was assessed for its anti-hyperglycaemic activity against streptozotocin induced animals. Results The IR spectrum of the extracts was compared with standard compounds and four compounds, α-amyrin, β-sitosterol, β-stigmasterol and wedelolactone was identified. Methanol extract of Coldenia procumbens L. decreased glucose levels in serum and enzymes levels. Histopathology of pancreas showed excellent recovery from the damage induced by streptozotocin. Conclusion The compounds identified in Coldenia procumbens L. have significant anti-diabetic, insulin mimetic and insulin secretory activities with their complete mechanisms already studied in detail. Also, Coldenia procumbens L. methanol extract showed significant anti-hyperglycaemic activity. The plant should be further studied to be developed as an alternative medicine.

Healthcare utilization, psychiatric medication and risk of rehospitalization in suicide-attempting patients with common mental disorders

OBJECTIVE

Common mental disorders are strong risk factors for suicide attempt. We compared common mental disorder patients with and without suicide attempt regarding health care utilization and psychiatric medication, assessed gender differences and identified how psychotropic medication of attempters is associated with subsequent rehospitalization.

METHODS

We used administrative claims data of 22,276 common mental disorder patients with inpatient care in Lower Austria between 1 January 2006 and 31 December 2011. Suicide attempters (cases, n = 615) and non-attempters (controls, n = 21,661) were compared regarding specific healthcare utilization by calculating mean differences of time-dependent contact probabilities and psychiatric medication (i.e. prescribed defined daily doses) ± 0.5 years around their suicide attempt (cases)/common mental disorder diagnosis (controls). Cluster analysis was used to group suicide attempters according to their psychiatric medication. The risk of rehospitalization 0.5-3 years after the attempt was calculated with regression analysis controlling for sex, age and morbidity-related factors.

RESULTS

Contacts with general practitioners were lower for attempters than non-attempters (mean difference of contact probabilities over observation period, males = -0.05, 95% confidence interval = [-0.07, -0.03]; females: mean difference = -0.04, 95% confidence interval = [-0.05, -0.03]). Regarding psychiatrists, female attempters had markedly higher contact probabilities after the attempt compared to female non-attempters (mean difference = 0.02, 95% confidence interval = [0.007, 0.04]); male attempters had lower contact probabilities before the attempt compared to male non-attempters (mean difference = -0.01, 95% confidence interval = [-0.004, -0.02]). Attempters had higher dosages of psychiatric medication across the entire period. Antidepressant and antipsychotic medication peaked at the time of common mental disorder diagnosis/attempt. Benzodiazepine prescriptions were considerably higher for male attempters than their female counterparts and were constantly elevated for male attempters across the observation period. A cluster of attempters with long-term benzodiazepine prescriptions had an increased risk of rehospitalization (adjusted odds ratio = 2.4, 95% confidence interval = [1.1, 5.5]).

CONCLUSION

Despite lower contact probabilities, common mental disorder patients with suicide attempt are prescribed more psychiatric medication, particularly benzodiazepines, with an elevated risk of rehospitalization. Strong sex differences were found.

MorbiNet: multimorbidity networks in adult general population. Analysis of type 2 diabetes mellitus comorbidity

Multimorbidity has great impact on health care. We constructed multimorbidity networks in the general population, extracted subnets focused on common chronic conditions and analysed type 2 diabetes mellitus (T2DM) comorbidity network. We used electronic records from 3,135,948 adult people in Catalonia, Spain (539,909 with T2DM), with at least 2 coexistent chronic conditions within the study period (2006-2017). We constructed networks from odds-ratio estimates adjusted by age and sex and considered connections with OR > 1.2 and p-value < 1e-5. Directed networks and trajectories were derived from temporal associations. Interactive networks are freely available in a website with the option to customize characteristics and subnets. The more connected conditions in T2DM undirected network were: complicated hypertension and atherosclerosis/peripheral vascular disease (degree: 32), cholecystitis/cholelithiasis, retinopathy and peripheral neuritis/neuropathy (degree: 31). T2DM has moderate number of connections and centrality but is associated with conditions with high scores in the multimorbidity network (neuropathy, anaemia and digestive diseases), and severe conditions with poor prognosis. The strongest associations from T2DM directed networks were to retinopathy (OR: 23.8), glomerulonephritis/nephrosis (OR: 3.4), peripheral neuritis/neuropathy (OR: 2.7) and pancreas cancer (OR: 2.4). Temporal associations showed the relevance of retinopathy in the progression to complicated hypertension, cerebrovascular disease, ischemic heart disease and organ failure.

Diabetes, a Contemporary Risk for Parkinson's Disease: Epidemiological and Cellular Evidences

Diabetes mellitus (DM), a group of diseases characterized by defective glucose metabolism, is the most widespread metabolic disorder affecting over 400 million adults worldwide. This pathological condition has been implicated in the pathogenesis of a number of central encephalopathies and peripheral neuropathies. In further support of this notion, recent epidemiological evidence suggests a link between DM and Parkinson’s disease (PD), with hyperglycemia emerging as one of the culprits in neurodegeneration involving the nigrostriatal pathway, the neuroanatomical substrate of the motor symptoms affecting parkinsonian patients. Indeed, dopaminergic neurons located in the mesencephalic substantia nigra appear to be particularly vulnerable to oxidative stress and degeneration, likely because of their intrinsic susceptibility to mitochondrial dysfunction, which may represent a direct consequence of hyperglycemia and hyperglycemia-induced oxidative stress. Other pathological pathways induced by increased intracellular glucose levels, including the polyol and the hexosamine pathway as well as the formation of advanced glycation end-products, may all play a pivotal role in mediating the detrimental effects of hyperglycemia on nigral dopaminergic neurons. In this review article, we will examine the epidemiological as well as the molecular and cellular clues supporting the potential susceptibility of nigrostriatal dopaminergic neurons to hyperglycemia.

Using Data Science to Understand Complexity and Quantify Heterogeneity in the Onset and Progression of Chronic Disease

Novel discoveries in genomics and other omics sciences are rapidly redefining our understanding of health and disease as well as advancing the development of targeted therapeutics for improving health outcomes. The scale of these findings, fueled by vast increases in computing power and new techniques in data analytics, easily supersedes that of phenomena observed using more traditional research approaches. Until recently, the classification and diagnosis of disease has involved rather subjective processes, whereby signs and late symptom patterns are linked with clinical outcomes. However, symptom patterns, disease trajectories, and health outcomes are complex entities characterized by a wide range of clinical manifestations and progression patterns. The burgeoning fields of data science and bioinformatics are opening opportunities for nurse scientists to quantify disease heterogeneity by defining and categorizing disease phenotypes and endotypes. Nurse scientists and clinicians can play a critical role in engaging patients and the larger scientific community in these efforts. The purpose of this article is to provide an introduction to concepts critical to understanding and quantifying heterogeneity in the onset and progression of chronic disease. To present and exemplify key concepts, we (1) discuss evidence for heterogeneity in the onset and progression of Type 1 diabetes, (2) link emerging research approaches in data science with principles in network science and systems biology to lay the groundwork for stratifying subclinical and advanced chronic disease, thus expanding the purview of symptom science, and (3) describe the computational skills needed to engage in these analyses.

Key considerations when using health insurance claims data in advanced data analyses: an experience report

Health claims have become a popular source of data for healthcare analytics, with numerous applications ranging from disease burden estimation and policy evaluation to drug event detection and advanced predictive analytics. Independent of the application, a researcher utilising claims information will likely encounter challenges in using the data, which include dealing with several coding systems and coding irregularities. We highlight some of these challenges and approaches for successful analysis that may reduce implementation time and help in avoiding common pitfalls. We describe the experiences of a group of academic researchers in using an extensive seven-year repository of US medical and pharmaceutical claims data in a research study, and provide an overview of the challenges encountered with handling claims records for data analysis while sharing suggestions on how to address these challenges. To illustrate our experiences, we use the example of defining episodes of care for a bundled payment reimbursement system in the US context.

Glucose Homeostasis Is Not Affected in a Murine Model of Parkinson's Disease Induced by 6-OHDA

There is a mutual relationship between metabolic and neurodegenerative diseases. However, the causal relationship in this crosstalk is unclear and whether Parkinson’s disease (PD) causes a posterior impact on metabolism remains unknown. Considering that, this study aimed to evaluate the appearance of possible changes in metabolic homeostasis due to 6-hydroxydopamine (6-OHDA) administration, a neurotoxin that damage dopaminergic neurons leading to motor impairments that resemble the ones observed in PD. For this, male Wistar rats received bilateral 6-OHDA administration in the dorsolateral striatum, and the motor and metabolic outcomes were assessed at 7, 21, or 35 days post-surgical procedure. Dexamethasone, a diabetogenic glucocorticoid (GC), was intraperitoneally administered in the last 6 days to challenge the metabolism and reveal possible metabolic vulnerabilities caused by 6-OHDA. Controls received only vehicles. The 6-OHDA-treated rats displayed a significant decrease in locomotor activity, exploratory behavior, and motor coordination 7 and 35 days after neurotoxin administration. These motor impairments paralleled with no significant alteration in body mass, food intake, glucose tolerance, insulin sensitivity, and biochemical parameters (plasma insulin, triacylglycerol, and total cholesterol levels) until the end of the experimental protocol on days 35–38 post-6-OHDA administration. Moreover, hepatic glycogen and fat content, as well as the endocrine pancreas mass, were not altered in rats treated with 6-OHDA at the day of euthanasia (38th day after neurotoxin administration). None of the diabetogenic effects caused by dexamethasone were exacerbated in rats previously treated with 6-OHDA. Thus, we conclude that bilateral 6-OHDA administration in the striatum causes motor deficits in rats with no impact on glucose and lipid homeostasis and does not exacerbate the adverse effects caused by excess GC. These observations indicate that neurodegeneration of dopaminergic circuits in the 6-OHDA rats does not affect the metabolic outcomes.

Sex-related differences in frailty factors in older persons with type 2 diabetes: a cross-sectional study

BACKGROUND

This cross-sectional study aimed to describe sex-related differences in diabetes-specific factors underlying the development of frailty in older persons with type 2 diabetes.

METHODS

Older persons aged 60-80 years were sequentially enrolled. Frailty and sarcopenia were evaluated using the validated Kihon checklist (KCL) and Asian Working Group for Sarcopenia algorithm, respectively. Physical function and characteristics were measured by trained nurses independently.

RESULTS

This study included 213 participants. The mean age, body mass index (BMI), and glycated hemoglobin (HbA1c) level were 70.4 years, 24.3 kg/m², and 7.4%, respectively. Prevalence of frailty was higher in women. Social and cognitive functions were lower in the prefrailty stage, while physical function was lower in the frailty stage, although there was no decrease in skeletal muscle mass. After adjustment for age, the KCL score was significantly associated with peripheral neuropathy, diet score, and coronary artery disease (CAD); frailty, with CAD and inoccupation; prefrailty, with diet score; and sarcopenia, with living alone in men. Meanwhile, the KCL score was significantly associated with living alone and skeletal muscle percentage; prefrailty, with peripheral neuropathy; and sarcopenia, with diabetes duration, LDL-cholesterol level, diet score, and irregular lifestyle in women.

CONCLUSIONS

Sex differences in the risk factors of frailty should be considered when selecting preventive strategies for older persons with type 2 diabetes, early in the prefrailty stage. In particular, it is important to evaluate social participation and diet therapy in men and skeletal muscle mass and psychosocial function in women.

Conditions Associated with Essential Tremor in Veterans: A Potential Role for Chronic Stress

Background

Increased depression, hearing loss, dementia, alcoholism, and mortality in essential tremor patients remain unexplained. We investigated whether conditions associated with tremor are linked to chronic stress.

Methods

The FY2013 Veterans Affairs database was queried for 38 selected dual diagnosis combinations in 5,854,223 veterans aged 21–95 years.

Results

Post-traumatic stress disorder, anxiety, and depression were the most common psychiatric diagnoses in tremor patients, with the odds ratio exceeding 2 in all 15-year cohorts. Depending on age, patients with essential tremor were more likely than those without to have obsessive–compulsive disorder, bipolar illness, schizophrenia, use tobacco and abuse alcohol, have hypertension, obesity, hyperlipidemia, diabetes, vitamin D deficiency, coronary and cerebrovascular diseases, congestive heart failure, stroke, asthma, hypothyroidism, irritable bowel syndrome, renal insufficiency, alcoholic liver disease, hearing loss, glaucoma, macular degeneration, migraine, epilepsy, idiopathic polyneuropathy, history of head trauma, and ‘Alzheimer’s dementia. In contrast, lung and colorectal cancer, amyotrophic lateral sclerosis, psychostimulant abuse, and rheumatoid arthritis were not more common.

Discussion

Post-traumatic stress disorder, anxiety, and depression, strongly associated with essential tremor, are known risk factors for poor health habits, tobacco use and alcohol abuse; collectively these are risk factors for vascular disease, with further negative health consequences for multiple organ systems. As essential tremor is associated with all these conditions, we propose that chronic stress is not only responsible for the conditions associated with tremor but in some cases itself directly and indirectly induces essential tremor, so that tremor and poor health share a common cause.

Predicting High-Cost Patients at Point of Admission Using Network Science

Data mining models for high-cost patient encounter prediction at the point-of-admission (HPEPP) in inpatient wards are scarce in the literature. This is due to the lack of availability of relevant features at such an early stage of treatment. In this study, we create a disease co-occurrence network (DCN) using a subset of the state inpatient database of Arizona. We explore this network for community formation and structural properties to create new input features for HPEPP models. Tree-based data mining models are trained using input feature sets including these new network features, and distinct disease communities in the DCN are identified. We propose community membership and high-cost propensity scores as two network-based features for HPEPP modeling. We compare the performance of models with different input feature sets and find that the new features significantly improve the accuracy sensitivity and specificity of prediction models. This model has the potential to improve targeted care management and reduce health care expenditure.

Minimally Disruptive Medicine for Patients with Diabetes

PURPOSE OF REVIEW

Patients with diabetes must deal with the burden of symptoms and complications (burden of illness). Simultaneously, diabetes care demands practical and emotional work from patients and their families, work to access and use healthcare and to enact self-care (burden of treatment). Patient work must compete with the demands of family, job, and community life. Overwhelmed patients may not have the capacity to access care or enact self-care and will thus experience suboptimal diabetes outcomes.

RECENT FINDINGS

Minimally disruptive medicine (MDM) is a patient-centered approach to healthcare that prioritizes patients' goals for life and health while minimizing the healthcare disruption on patients' lives. In patients with diabetes, particularly in those with complex lives and multimorbidity, MDM coordinates healthcare and community responses to improve outcomes, reduce treatment burden, and enable patients to pursue their life's hopes and dreams.

Sex and gender differences in therapy of type 2 diabetes

Clinical guidelines for the management of type 2 diabetes recommend individual therapy considering age, duration of disease, presence of complication and risk of hypoglycaemia. However, at present, the patient's sex has no impact on clinical decisions. Yet, there is mounting data pointing at biological and psychosocial differences between men and women with great impact on progression of disease and complications. Moreover, choices and preferences of therapeutic strategies as well as adherence to lifestyle and pharmacological interventions differ in both sexes. In addition, drug therapy may have sex-specific side effects. Therefore, there is need of more research on biological differences and of evidence-based individualised targeted sex-sensitive therapeutic concepts. Clinical guidelines must consider relevant sex-differences. Development and implementation of sex-specific programs may help to improve adherence to therapy and to reduce progression of disease and development of complications. A more gender-sensitive clinical approach may improve quality of life and increase health and life expectancy in men and women with type 2 diabetes.

Comorbid Conditions in Canadians Hospitalized Because of Diabetes

OBJECTIVES

The number of hospital discharges involving type 1 or type 2 diabetes as the first-listed diagnosis has increased over the past few decades. Such cases are commonly associated with various comorbidities. We analyzed data collected from hospital separations in Canada to determine the number and proportion of comorbid conditions in Canadians hospitalized because of diabetes.

METHODS

The most responsible diagnosis (MRDx) of diabetes (ICD-10-CA: E10 [type 1 diabetes] or E11 [type 2 diabetes]) was identified from acute care hospital separations (Discharge Abstract Database) from 2013 to 2014 in all Canadian jurisdictions except Quebec. Hospital separations were calculated by 5-year age groups and sex. The most common comorbid (secondary) diagnoses were identified for hospital separations with diabetes as the MRDx, and the average lengths of stay associated with diabetes were determined.

RESULTS

The total number of hospital separations in 2013-2014 in Canada (excluding Quebec) with diabetes as the MRDx in all ages was 30,422, and males represented more than half (58.7%) of the separations. The median age group when diabetes is the MRDx is 60 to 64. For all patients hospitalized with an MRDx of diabetes, there were 2.2% deaths. Hypertension was the most common comorbid diagnosis in patients with diabetes as the MRDx, followed by glomerular disorders, peripheral angiopathy and acute renal failure.

CONCLUSIONS

Hospitalizations resulting from diabetes are associated with vascular comorbidities, and hypertension is the most common. Prevention of shared predisposing factors should reduce the burden of hospitalizations associated with diabetes.

Use of statins offsets insulin-related cancer risk

AIM

There is firm evidence of a relation between type 2 diabetes (T2DM) and increased risks of cancer at various sites, but it is still unclear how different antihyperglycaemic therapies modify site-specific cancer risks. The aim of this study was to provide a complete characterization of all possible associations between individual T2DM therapies, statin use and site-specific cancers in the Austrian population.

METHODS

Medical claims data of 1 847 051 patients with hospital stays during 2006-2007 were used to estimate age- and sex-dependent co-occurrences of site-specific cancer diagnoses and treatment with specific glucose-lowering drugs and statins.

RESULTS

Patients treated with insulin or insulin secretagogues showed up to ninefold increased risks for cancers of the colon [males only (m)], liver (m), pancreas, lung (m) and brain (m), as well as a strongly decreased risk for prostate cancer (m). In patients taking statins, the risks were generally decreased, with a greater risk reduction in patients not receiving antihyperglycaemic therapies. The strongest effects were observed for use of insulin and pancreatic cancer [m: OR 4.5, 95% CI: 3.1-6.6; females (f): OR 4.2, 95% CI: 2.5-7.1], sulfonylureas (m: OR 2.8, 95% CI: 1.7-4.6; f: OR 3.0, 95% CI: 2.1-4.2) or glitazones and skin cancer (f: OR 0.54, 95% CI: 0.36-0.80), as well as metformin and cancer of the prostate (m: OR 0.82, 95% CI: 0.75-0.91) and corpus uteri (f: OR 1.7, 95% CI: 1.4-2.0) and non-Hodgkin's lymphoma (f: OR 0.76, 95% CI: 0.64-0.91).

CONCLUSIONS

The use of statins offsets insulin-related cancer risks in patients with diabetes independently of sex and age. Overall, our data support the hyperglycaemia-cancer hypothesis. A reduction in endogenous or exogenous hyperinsulinaemia may be beneficial for cancer prevention. Therefore, insulin-sparing and insulin-sensitizing drugs should be the preferred treatment choices.

[Sex- and gender-aspects in regard to clinical practice recommendations for pre-diabetes and diabetes]

Metabolic diseases dramatically affect life of men and women from infancy up to old age and are a major challenge for clinicians. Health professionals are confronted with different needs of women and men. This article aims at an increase of gender awareness and the implementation of current knowledge of gender medicine in daily clinical practice with regard to pre-diabetes and diabetes. Sex and gender affect screening and diagnosis of metabolic diseases as well as treatment strategies and outcome. Impaired glucose and lipid metabolism, regulation of energy balance and body fat distribution are related to steroid hormones and therefore impose their influence on cardiovascular health in both men and women. Furthermore, education, income and psychosocial factors relate to development of obesity and diabetes differently in men and women. Males appear to be at greater risk of diabetes at younger age and at lower BMI compared to women, but women feature a dramatic increase of their cardiometabolic risk after menopause. The estimated future years of life lost owing to diabetes is somewhat higher in women than men, with higher increase of vascular death in women, but higher increase of cancer death in men. In women pre-diabetes or diabetes are more distinctly associated with a higher number of vascular risk factors, such as inflammatory parameters, unfavourable changes of coagulation and blood pressure. Pre-diabetic and diabetic women are at much higher relative risk for vascular disease. Women are more often obese and less physically active, but may even have greater benefit from increased physical activity than males. Whereas men predominantly feature impaired fasting glucose, women often show impaired glucose tolerance. A history of gestational diabetes or the presence of a PCOS or increased androgen levels in women, on the other hand the presence of erectile dysfunction (ED) or decreased testosterone levels in men are sex specific risk factors for diabetes development. ED is a common feature of obese men with the Metabolic Syndrome and an important predictor of cardiovascular disease. Diabetic women also feature sexual dysfunctions much more frequently than non-diabetic women which should be addressed in clinical care. Several studies showed that diabetic women reach their targets of metabolic control (HbA1c), blood pressure and LDL-cholesterol less often than their male counterparts, although the reasons for worse treatment outcome in diabetic females are not clear. Furthermore, sex differences in action, pharmacokinetics, and side effects of pharmacological therapy have to be taken into account.

Disentangling genetic and environmental risk factors for individual diseases from multiplex comorbidity networks

Most disorders are caused by a combination of multiple genetic and/or environmental factors. If two diseases are caused by the same molecular mechanism, they tend to co-occur in patients. Here we provide a quantitative method to disentangle how much genetic or environmental risk factors contribute to the pathogenesis of 358 individual diseases, respectively. We pool data on genetic, pathway-based, and toxicogenomic disease-causing mechanisms with disease co-occurrence data obtained from almost two million patients. From this data we construct a multiplex network where nodes represent disorders that are connected by links that either represent phenotypic comorbidity of the patients or the involvement of a certain molecular mechanism. From the similarity of phenotypic and mechanism-based networks for each disorder we derive measure that allows us to quantify the relative importance of various molecular mechanisms for a given disease. We find that most diseases are dominated by genetic risk factors, while environmental influences prevail for disorders such as depressions, cancers, or dermatitis. Almost never we find that more than one type of mechanisms is involved in the pathogenesis of diseases.

Sleep characteristics in type 1 diabetes and associations with glycemic control: systematic review and meta-analysis

OBJECTIVES

The association between inadequate sleep and type 2 diabetes has garnered much attention, but little is known about sleep and type 1 diabetes (T1D). Our objectives were to conduct a systematic review and meta-analysis comparing sleep in persons with and without T1D, and to explore relationships between sleep and glycemic control in T1D.

METHODS

Studies were identified from Medline and Scopus. Studies reporting measures of sleep in T1D patients and controls, and/or associations between sleep and glycemic control, were selected.

RESULTS

A total of 22 studies were eligible for the meta-analysis. Children with T1D had shorter sleep duration (mean difference [MD] = -26.4 minutes; 95% confidence interval [CI] = -35.4, -17.7) than controls. Adults with T1D reported poorer sleep quality (MD in standardized sleep quality score = 0.51; 95% CI = 0.33, 0.70), with higher scores reflecting worse sleep quality) than controls, but there was no difference in self-reported sleep duration. Adults with TID who reported sleeping >6 hours had lower hemoglobin A1c (HbA1c) levels than those sleeping ≤6 hours (MD = -0.24%; 95% CI = -0.47, -0.02), and participants reporting good sleep quality had lower HbA1c than those with poor sleep quality (MD = -0.19%; 95% CI = -0.30, -0.08). The estimated prevalence of obstructive sleep apnea (OSA) in adults with TID was 51.9% (95% CI = 31.2, 72.6). Patients with moderate-to-severe OSA had a trend toward higher HbA1c (MD = 0.39%, 95% CI = -0.08, 0.87).

CONCLUSION

T1D was associated with poorer sleep and high prevalence of OSA. Poor sleep quality, shorter sleep duration, and OSA were associated with suboptimal glycemic control in T1D patients.

Sex and Gender Differences in Risk, Pathophysiology and Complications of Type 2 Diabetes Mellitus

The steep rise of type 2 diabetes mellitus (T2DM) and associated complications go along with mounting evidence of clinically important sex and gender differences. T2DM is more frequently diagnosed at lower age and body mass index in men; however, the most prominent risk factor, which is obesity, is more common in women. Generally, large sex-ratio differences across countries are observed. Diversities in biology, culture, lifestyle, environment, and socioeconomic status impact differences between males and females in predisposition, development, and clinical presentation. Genetic effects and epigenetic mechanisms, nutritional factors and sedentary lifestyle affect risk and complications differently in both sexes. Furthermore, sex hormones have a great impact on energy metabolism, body composition, vascular function, and inflammatory responses. Thus, endocrine imbalances relate to unfavorable cardiometabolic traits, observable in women with androgen excess or men with hypogonadism. Both biological and psychosocial factors are responsible for sex and gender differences in diabetes risk and outcome. Overall, psychosocial stress appears to have greater impact on women rather than on men. In addition, women have greater increases of cardiovascular risk, myocardial infarction, and stroke mortality than men, compared with nondiabetic subjects. However, when dialysis therapy is initiated, mortality is comparable in both males and females. Diabetes appears to attenuate the protective effect of the female sex in the development of cardiac diseases and nephropathy. Endocrine and behavioral factors are involved in gender inequalities and affect the outcome. More research regarding sex-dimorphic pathophysiological mechanisms of T2DM and its complications could contribute to more personalized diabetes care in the future and would thus promote more awareness in terms of sex- and gender-specific risk factors.

Big data analytics to improve cardiovascular care: promise and challenges

The potential for big data analytics to improve cardiovascular quality of care and patient outcomes is tremendous. However, the application of big data in health care is at a nascent stage, and the evidence to date demonstrating that big data analytics will improve care and outcomes is scant. This Review provides an overview of the data sources and methods that comprise big data analytics, and describes eight areas of application of big data analytics to improve cardiovascular care, including predictive modelling for risk and resource use, population management, drug and medical device safety surveillance, disease and treatment heterogeneity, precision medicine and clinical decision support, quality of care and performance measurement, and public health and research applications. We also delineate the important challenges for big data applications in cardiovascular care, including the need for evidence of effectiveness and safety, the methodological issues such as data quality and validation, and the critical importance of clinical integration and proof of clinical utility. If big data analytics are shown to improve quality of care and patient outcomes, and can be successfully implemented in cardiovascular practice, big data will fulfil its potential as an important component of a learning health-care system.

Metformin, besides exhibiting strong in vivo anti-inflammatory properties, increases mptp-induced damage to the nigrostriatal dopaminergic system

Metformin is a widely used oral antidiabetic drug with known anti-inflammatory properties due to its action on AMPK protein. This drug has shown a protective effect on various tissues, including cortical neurons. The aim of this study was to determine the effect of metformin on the dopaminergic neurons of the substantia nigra of mice using the animal model of Parkinson's disease based on the injection of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine, an inhibitor of the mitochondrial complex I. In vivo and in vitro experiments were used to study the activation of microglia and the damage of the dopaminergic neurons. Our results show that metformin reduced microglial activation measured both at cellular and molecular levels. Rather than protecting, metformin exacerbated dopaminergic damage in response to MPTP. Our data suggest that, contrary to other brain structures, metformin treatment could be deleterious for the dopaminergic system. Hence, metformin treatment may be considered as a risk factor for the development of Parkinson's disease.

Diabetes and Schizophrenia

People with schizophrenia have 2- to 5-fold higher risk of type 2 diabetes than the general population. The traditional risk factors for type 2 diabetes, especially obesity, poor diet, and sedentary lifestyle, are common in people with schizophrenia already early in the course of illness. People with schizophrenia also often have low socioeconomic status and income, which affects their possibilities to make healthy lifestyle choices. Antipsychotic medications increase the risk of type 2 diabetes both directly by affecting insulin sensitivity and indirectly by causing weight gain. Lifestyle modification interventions for prevention of diabetes should be an integral part of treatment of patients with schizophrenia. In the treatment of type 2 diabetes in patients with schizophrenia, communication and collaboration between medical care and psychiatric treatment providers are essential.