Changes in blood pressure thresholds for initiating antihypertensive medication in patients with diabetes: a repeated cross-sectional study focusing on the impact of age and frailty

Objective To assess trends in systolic blood pressure (SBP) thresholds at initiation of antihypertensive treatment in patients with type 2 diabetes and the impact of age and frailty on these trends.Study design and setting A repeated cross-sectional cohort study (2007–2014) using the Groningen Initiative to Analyse Type 2 diabetes Treatment database was conducted. The influence of calendar year, age or frailty and the interaction between year and age or frailty on SBP thresholds were assessed using multilevel regression analyses adjusted for potential confounders.Results We included 4819 patients. The mean SBP at treatment initiation was 157 mm Hg in 2007, rising to 158 mm Hg in 2009 and decreasing to 151 mm Hg in 2014. This quadratic trend was significant (p<0.001). Older patients initiated treatment at higher SBP, but similar decreasing trends after 2009 were observed in all age groups. There were no significant differences in SBP thresholds between patients with different frailty groups. The association between year and SBP threshold was not influenced by age or frailty.Conclusion After an initial rise, the observed SBP thresholds decreased over time and were not influenced by age or frailty. This is in contrast with changed guideline recommendations towards more personalised treatment during the study period and illustrates that changing prescribing practice may take considerable time. Patient-specific algorithms and tools focusing on when and when not to initiate treatment could be helpful to support personalised diabetes care

Predicting Short-term and Long-term HbA1c Response after Insulin Initiation in Patients with Type 2 Diabetes Mellitus using Machine Learning

AIM: To assess the potential of supervised machine learning techniques to identify clinical variables for predicting short-term and long-term glycated hemoglobin (HbA1c) response after insulin treatment initiation in patients with type 2 diabetes mellitus (T2DM). MATERIALS AND METHODS: We included patients with T2DM from the Groningen Initiative to ANalyze Type 2 diabetes Treatment (GIANTT) database who started insulin treatment between 2007-2013 with a minimum follow-up of 2 years. Short-term and long-term response were defined at 6 (± 2) and 24 (± 2) months after insulin initiation, respectively. Patients were defined as good responders if they had a decrease in HbA1c ≥ 5mmol/mol or reached the recommended level of HbA1c ≤ 53 mmol/mol. Twenty-four baseline clinical variables were used for the analysis and elastic net regularization technique was used for variables selection. The performance of three traditional machine learning algorithms was compared to predict short-term and long-term responses and the area under the receiver operator characteristic curve (AUC) was used to assess the performance of the prediction model. RESULTS: The elastic net regularization based generalized linear model, including baseline HbA1c and eGFR, correctly classified short-term and long-term HbA1c response after treatment initiation with an AUC (95% CI) = 0.80 (0.78 - 0.83) and 0.81 (0.79 - 0.84), respectively, and outperformed other machine learning algorithms. Using baseline HbA1c alone, an AUC = 0.71 (0.65 - 0.73) and 0.72 (0.66 - 0.75) was obtained for predicting short-term and long-term response, respectively. CONCLUSIONS: Machine-learning algorithm performed well in the prediction of an individual's short-term and long-term HbA1c response using baseline clinical variables. This article is protected by copyright. All rights reserved

A Classification Approach for Cancer Survivors from Those Cancer-Free, Based on Health Behaviors:Analysis of the Lifelines Cohort

Simple Summary Health behaviors affect health status in cancer survivors. We aimed to identify such key health behaviors using nonlinear algorithms and compare their classification performance with logistic regression, for distinguishing cancer survivors from those cancer-free in a population-based cohort. We used health behaviors and socioeconomic factors for analysis. Participants from the Lifelines population-based cohort were binary classified as cancer survivors or cancer-free using nonlinear algorithms or logistic regression. Data were collected for 107,624 cancer-free participants and 2760 cancer survivors. Using all variables, algorithms obtained an area under the receiver operator curve (AUC) of 0.75 +/- 0.01. Using only health behaviors, the algorithms differentiated cancer survivors from cancer-free participants with AUCs of 0.62 +/- 0.01 and 0.60 +/- 0.01, respectively. In the case-control analyses, both algorithms produced AUCs of 0.52 +/- 0.01. The main distinctive classifier was age. No key health behaviors were identified by linear and nonlinear algorithms to differentiate cancer survivors from cancer-free participants. Health behaviors affect health status in cancer survivors. We hypothesized that nonlinear algorithms would identify distinct key health behaviors compared to a linear algorithm and better classify cancer survivors. We aimed to use three nonlinear algorithms to identify such key health behaviors and compare their performances with that of a logistic regression for distinguishing cancer survivors from those without cancer in a population-based cohort study. We used six health behaviors and three socioeconomic factors for analysis. Participants from the Lifelines population-based cohort were binary classified into a cancer-survivors group and a cancer-free group using either nonlinear algorithms or logistic regression, and their performances were compared by the area under the curve (AUC). In addition, we performed case-control analyses (matched by age, sex, and education level) to evaluate classification performance only by health behaviors. Data were collected for 107,624 cancer free participants and 2760 cancer survivors. Using all variables resulted an AUC of 0.75 +/- 0.01, using only six health behaviors, the logistic regression and nonlinear algorithms differentiated cancer survivors from cancer-free participants with AUCs of 0.62 +/- 0.01 and 0.60 +/- 0.01, respectively. The main distinctive classifier was age. Though not relevant to classification, the main distinctive health behaviors were body mass index and alcohol consumption. In the case-control analyses, algorithms produced AUCs of 0.52 +/- 0.01. No key health behaviors were identified by linear and nonlinear algorithms to differentiate cancer survivors from cancer-free participants in this population-based cohort

Factors associated with SARS-COV-2 positive test in Lifelines

BACKGROUND: Severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) can affect anyone, however, it is often mixed with other respiratory diseases. This study aimed to identify the factors associated with SARS-COV-2 positive test.METHODS: Participants from the Northern Netherlands representative of the general population were included if filled in the questionnaire about well-being between June 2020-April 2021 and were tested for SARS-COV-2. The outcome was a self-reported test as measured by polymerase chain reaction. The data were collected on age, sex, household, smoking, alcohol use, physical activity, quality of life, fatigue, symptoms and medications use. Participants were matched on sex, age and the timing of their SARS-COV-2 tests maintaining a 1:4 ratio and classified into those with a positive and negative SARS-COV-2 using logistic regression. The performance of the model was compared with other machine-learning algorithms by the area under the receiving operating curve.RESULTS: 2564 (20%) of 12786 participants had a positive SARS-COV-2 test. The factors associated with a higher risk of SARS-COV-2 positive test in multivariate logistic regression were: contact with someone tested positive for SARS-COV-2, ≥1 household members, typical SARS-COV-2 symptoms, male gender and fatigue. The factors associated with a lower risk of SARS-COV-2 positive test were higher quality of life, inhaler use, runny nose, lower back pain, diarrhea, pain when breathing, sore throat, pain in neck, shoulder or arm, numbness or tingling, and stomach pain. The performance of the logistic models was comparable with that of random forest, support vector machine and gradient boosting machine.CONCLUSIONS: Having a contact with someone tested positive for SARS-COV-2 and living in a household with someone else are the most important factors related to a positive SARS-COV-2 test. The loss of smell or taste is the most prominent symptom associated with a positive test. Symptoms like runny nose, pain when breathing, sore throat are more likely to be indicative of other conditions.</p

A systematic review and narrative synthesis of data-driven studies in schizophrenia symptoms and cognitive deficits

To tackle the phenotypic heterogeneity of schizophrenia, data-driven methods are often applied to identify subtypes of its symptoms and cognitive deficits. However, a systematic review on this topic is lacking. The objective of this review was to summarize the evidence obtained from longitudinal and cross-sectional data-driven studies in positive and negative symptoms and cognitive deficits in patients with schizophrenia spectrum disorders, their unaffected siblings and healthy controls or individuals from general population. Additionally, we aimed to highlight methodological gaps across studies and point out future directions to optimize the translatability of evidence from data-driven studies. A systematic review was performed through searching PsycINFO, PubMed, PsycTESTS, PsycARTICLES, SCOPUS, EMBASE and Web of Science electronic databases. Both longitudinal and cross-sectional studies published from 2008 to 2019, which reported at least two statistically derived clusters or trajectories were included. Two reviewers independently screened and extracted the data. In this review, 53 studies (19 longitudinal and 34 cross-sectional) that conducted among 17,822 patients, 8729 unaffected siblings and 5520 controls or general population were included. Most longitudinal studies found four trajectories that characterized by stability, progressive deterioration, relapsing and progressive amelioration of symptoms and cognitive function. Cross-sectional studies commonly identified three clusters with low, intermediate (mixed) and high psychotic symptoms and cognitive profiles. Moreover, identified subgroups were predicted by numerous genetic, sociodemographic and clinical factors. Our findings indicate that schizophrenia symptoms and cognitive deficits are heterogeneous, although methodological limitations across studies are observed. Identified clusters and trajectories along with their predictors may be used to base the implementation of personalized treatment and develop a risk prediction model for high-risk individuals with prodromal symptoms

Coronary Artery Calcium and Cognitive Function in Dutch Adults:Cross-Sectional Results of the Population-Based ImaLife Study

Background The aim of this study was to investigate whether increased severity of coronary artery calcium (CAC), an imaging biomarker of subclinical coronary atherosclerosis, is associated with worse cognitive function independent of cardiovascular risk factors in a large population-based Dutch cohort with broad age range. Methods and Results A cross-sectional analysis was performed in 4988 ImaLife participants (aged 45-91 years, 58.3% women) without history of cardiovascular disease. CAC scores were obtained using nonenhanced cardiac computed tomography scanning. The CogState Brief Battery was used to assess 4 cognitive domains: processing speed, attention, working memory, and visual learning based on detection task, identification task, 1-back task, and 1-card-learning task, respectively. Differences in mean scores of each cognitive domain were compared among 4 CAC categories (0, 1-99, 100-399, >= 400) using analysis of covariates to adjust for classical cardiovascular risk factors. Age-stratified analysis (45-54, 55-64, and >= 65 years) was performed to assess whether the association of CAC severity with cognitive function differed by age. Overall, higher CAC was associated with worse performance on 1-back task after adjusting for classical cardiovascular risk factors, but CAC was not associated with the other cognitive tasks. Age-stratified analyses revealed that the association of CAC severity with working memory persisted in participants aged 45 to 54 years, while in the elderly this association lost significance. Conclusions In this Dutch population of >= 45 years, increased CAC severity was associated with worse performance of working memory, independent of classical cardiovascular risk factors. The inverse relationship of CAC score categories with working memory was strongest in participants aged 45 to 54 years

Type 2 Diabetes Mellitus and Clinicopathological Tumor Characteristics in Women Diagnosed with Breast Cancer:A Systematic Review and Meta-Analysis

SIMPLE SUMMARY: Female breast cancer continues to be the leading cause of cancer deaths worldwide, and type 2 diabetes mellitus (T2DM) is one of the contributors to the poor prognosis of breast cancer. This raises the issue that T2DM might be associated with aggressive clinicopathological characteristics, which indicate pivotal prognostic values. This study aimed to clarify the differences in breast cancer characteristics at diagnosis between patients with and without pre-existing T2DM. Our meta-analyses showed an increased risk of being diagnosed with a late-stage tumor, large tumor size, and invasive lymph nodes in patients with T2DM. No significant results were observed for grade, estrogen/progesterone receptor, and human epidermal growth factor receptor. These findings indicate an association between T2DM and advanced breast cancer at diagnosis, and suggest that the more active role of breast cancer screening should be further explored for women with T2DM. ABSTRACT: Poor prognosis caused by type 2 diabetes mellitus (T2DM) in women with breast cancer is conferred, while the association between T2DM and breast tumor aggressiveness is still a matter of debate. This study aimed to clarify the differences in breast cancer characteristics, including stage, size, lymph node status, grade, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor (Her2), between patients with and without pre-existing T2DM. PubMed, Embase, and Web of Science were searched for studies from 1 January 2010 to 2 July 2021. Adjusted odds ratios (ORs) with 95% confidence intervals (CIs) were pooled by using a random effects model. T2DM was significantly associated with tumor stages III/IV versus cancers in situ and stages I/II (pooled ORs (pOR), 95% CI: 1.19; 1.04–1.36, p = 0.012), tumor size >20 versus ≤20 mm (pOR, 95% CI: 1.18; 1.04–1.35, p = 0.013), and lymph node invasion versus no involvement (pOR, 95% CI: 1.26; 1.05–1.51, p = 0.013). These findings suggest that women with T2DM are at a higher risk of late-stage tumors, large tumor sizes, and invasive lymph nodes at breast cancer diagnosis

The validity of self-reported cancer in a population-based cohort compared to that in formally registered sources

Background: Self-reported cancer has been validated with heterogeneous results across populations. The aim was to assess the validity of self-reported cancer in the Lifelines population-based cohort and to search for explanations for not reporting cancer. Methods: Data from adult participants (n = 152,780) from Lifelines was linked to the Dutch-Nationwide pathology databank (PALGA), which has nearly 100% coverage of cancer diagnoses in the Netherlands and is considered as the gold standard for ascertainment of cancer diagnosis in this study. Sensitivity and positive predictive value (PPV) for self-reported cancers -reported as hand-written free text- were described. Logistic regressions analyses were performed to evaluate whether socio-demographic factors were associated with the presence of self-reported cancer when there was a diagnosis in PALGA. Results: 6611 (4.50%) participants had at least one self-reported diagnosis of cancer, where 9960 (6.97%) participants had at least one cancer diagnosis in PALGA. The sensitivity of self-reported cancer was 64.68% [95%CI:63.71–65.66], and 70.18% [95%CI:68.83–71.56] after excluding skin and cervical cancers. Skin and cervical cancers represented 61.24% of non-self-reported cancers. The overall PPV was 97.45% [95%CI:97.45–97.81], and 97.33% [95%CI:96.72–97.82] after the exclusion of skin and cervical cancers. Participants who did not self-report their cancer were more likely to be male, had longer time since diagnosis and lower educational level. Conclusion: Overall, the reports of cancer in Lifelines have a high positive predictive value and moderate sensitivity. One third of the cancers were not reported, mainly skin and cervical cancers. Male participants, those with a lower educational level and those with longer time since diagnosis were less likely to self-report a diagnosed cancer