Recurrent Bacteriuria as a Prognosis Marker in the Adjuvant Treatment of Non-Muscle Invasive Bladder Cancer.

PURPOSE: Bacteriuria may affect the response to adjuvant therapy in non-muscle invasive bladder cancer (NMIBC). The main aim of this study was to examine the effect of recurrent bacteriuria (RB) on the prognosis of NMIBC in women receiving intravesical therapy. MATERIALS AND METHODS: We designed a prospective observational study from 2012 to 2019. We included women with bladder cancer treated with transurethral resection of the bladder (TURB) and adjuvant intravesical treatment. Significant bacteriuria was defined as a presence in urine cultures at or above 100,000 colony-forming units per millilitre. The recurrent bacteriuria group included patients with significant bacteriuria in at least two determinations in 6 months or in 3 or more determinations in a year. The institutional board approved the study. RESULTS: One hundred thirty-six patients diagnosed with NMIBC participate in the study, of whom 100 met the inclusion criteria. During follow-up, 48 were categorized in the RB group and 52 formed the non-bacteriuria group (NB). RB GROUP HAD A BETTER OUTCOME: Eight patients (16.67%) experiencing a recurrence of the same grade, with no progression to a higher-grade tumor or muscle-invasive tumor. In the NB group, 18 (34.6%) patients presented a recurrence (P = .001) and 22 (42.3%) progressed to a higher-grade tumor or muscular invasion (P = .001). The presence of RB was identified as a predictor of good response in multivariate regression with a relative risk of 0.13 (P = .018) CONCLUSIONS: Female patients with RB had a better response to adjuvant treatment for NMIBC. The RB group showed lower rates of tumor recurrences and progression.

Machine Learning Models for Prediction of Diabetic Microvascular Complications.

IMPORTANCE AND AIMS: Diabetic microvascular complications significantly impact morbidity and mortality. This review focuses on machine learning/artificial intelligence (ML/AI) in predicting diabetic retinopathy (DR), diabetic kidney disease (DKD), and diabetic neuropathy (DN). METHODS: A comprehensive PubMed search from 1990 to 2023 identified studies on ML/AI models for diabetic microvascular complications. The review analyzed study design, cohorts, predictors, ML techniques, prediction horizon, and performance metrics. RESULTS: Among the 74 identified studies, 256 featured internally validated ML models and 124 had externally validated models, with about half being retrospective. Since 2010, there has been a rise in the use of ML for predicting microvascular complications, mainly driven by DKD research across 27 countries. A more modest increase in ML research on DR and DN was observed, with publications from fewer countries. For all microvascular complications, predictive models achieved a mean (standard deviation) c-statistic of 0.79 (0.09) on internal validation and 0.72 (0.12) on external validation. Diabetic kidney disease models had the highest discrimination, with c-statistics of 0.81 (0.09) on internal validation and 0.74 (0.13) on external validation, respectively. Few studies externally validated prediction of DN. The prediction horizon, outcome definitions, number and type of predictors, and ML technique significantly influenced model performance. CONCLUSIONS AND RELEVANCE: There is growing global interest in using ML for predicting diabetic microvascular complications. Research on DKD is the most advanced in terms of publication volume and overall prediction performance. Both DR and DN require more research. External validation and adherence to recommended guidelines are crucial.

Precision prognostics for cardiovascular disease in Type 2 diabetes: a systematic review and meta-analysis.

BACKGROUND: Precision medicine has the potential to improve cardiovascular disease (CVD) risk prediction in individuals with Type 2 diabetes (T2D). METHODS: We conducted a systematic review and meta-analysis of longitudinal studies to identify potentially novel prognostic factors that may improve CVD risk prediction in T2D. Out of 9380 studies identified, 416 studies met inclusion criteria. Outcomes were reported for 321 biomarker studies, 48 genetic marker studies, and 47 risk score/model studies. RESULTS: Out of all evaluated biomarkers, only 13 showed improvement in prediction performance. Results of pooled meta-analyses, non-pooled analyses, and assessments of improvement in prediction performance and risk of bias, yielded the highest predictive utility for N-terminal pro b-type natriuretic peptide (NT-proBNP) (high-evidence), troponin-T (TnT) (moderate-evidence), triglyceride-glucose (TyG) index (moderate-evidence), Genetic Risk Score for Coronary Heart Disease (GRS-CHD) (moderate-evidence); moderate predictive utility for coronary computed tomography angiography (low-evidence), single-photon emission computed tomography (low-evidence), pulse wave velocity (moderate-evidence); and low predictive utility for C-reactive protein (moderate-evidence), coronary artery calcium score (low-evidence), galectin-3 (low-evidence), troponin-I (low-evidence), carotid plaque (low-evidence), and growth differentiation factor-15 (low-evidence). Risk scores showed modest discrimination, with lower performance in populations different from the original development cohort. CONCLUSIONS: Despite high interest in this topic, very few studies conducted rigorous analyses to demonstrate incremental predictive utility beyond established CVD risk factors for T2D. The most promising markers identified were NT-proBNP, TnT, TyG and GRS-CHD, with the highest strength of evidence for NT-proBNP. Further research is needed to determine their clinical utility in risk stratification and management of CVD in T2D.

People living with type 2 diabetes (T2D) are more likely to develop problems with their heart or blood circulation, known as cardiovascular disease (CVD), than people who do not have T2D. However, it can be difficult to predict which people with T2D are most likely to develop CVD. This is because current approaches, such as blood tests, do not identify all people with T2D who are at an increased risk of CVD. In this study we reviewed published papers that investigated the differences between people with T2D who experienced CVD compared to those who did not. We found some indicators that could potentially be used to determine which people with T2D are most likely to develop CVD. More studies are needed to determine how useful these are. However, they could potentially be used to enable clinicians to provide targeted advice and treatment to those people with T2D at most risk of developing CVD.

Association of Age With Treatment-Related Adverse Events and Survival in Patients With Metastatic Colorectal Cancer.

Importance: While the incidence of early-onset metastatic colorectal cancer (mCRC) has been increasing, studies on the age-related disparity in this group of patients are limited. Objective: To evaluate the association of age with treatment-related adverse events and survival in patients with mCRC and explore the potential underlying factors. Design, Setting, and Participants: This cohort study included 1959 individuals. Individual data on 1223 patients with mCRC who received first-line fluorouracil and oxaliplatin therapy in 3 clinical trials, and clinical and genomic data of 736 patients with mCRC from Moffitt Cancer Center were used to assess genomic alterations and serve as an external validation cohort. All statistical analyses were conducted from October 1, 2021, through November 12, 2022. Exposures: Metastatic colorectal cancer. Main Outcomes and Measures: Survival outcomes and treatment-related adverse events were compared among patients in 3 age groups: younger than 50 (early onset), 50 to 65, and older than 65 years. Results: In the total population of 1959 individuals, 1145 (58.4%) were men. Among 1223 patients from previous clinical trials, 179 (14.6%) in the younger than 50 years group, 582 (47.6%) in the 50 to 65 years group, and 462 (37.8%) in the older than 65 years group had similar baseline characteristics except for sex and race. The younger than 50 years group had significantly shorter progression-free survival (PFS) (hazard ratio [HR], 1.46; 95% CI, 1.22-1.76; P < .001) and overall survival (OS) (HR, 1.48; 95% CI, 1.19-1.84; P < .001) compared with the 50 to 65 years group after adjustment for sex, race, and performance status. Significantly shorter OS in the younger than 50 years group was confirmed in the Moffitt cohort. The younger than 50 years group had a significantly higher incidence of nausea and vomiting (69.3% vs 57.6% [50-65 years] vs 60.4% [>65 years]; P = .02), severe abdominal pain (8.4% vs 3.4% vs 3.5%; P = .02), severe anemia (6.1% vs 1.0% vs 1.5%; P < .001), and severe rash (2.8% vs 1.2% vs 0.4% P = .047). The younger than 50 years group also had earlier onset of nausea and vomiting (1.0 vs 2.1 vs 2.6 weeks; P = .01), mucositis (3.6 vs 5.1 vs 5.7 weeks; P = .05), and neutropenia (8.0 vs 9.4 vs 8.4 weeks; P = .04), and shorter duration of mucositis (0.6 vs 0.9 vs 1.0 weeks; P = .006). In the younger than 50 years group, severe abdominal pain and severe liver toxic effects were associated with shorter survival. The Moffitt genomic data showed that the younger than 50 years group had a higher prevalence of CTNNB1 mutation (6.6% vs 3.1% vs 2.3%; P = .047), ERBB2 amplification (5.1% vs 0.6% vs 2.3%; P = .005), and CREBBP mutation (3.1% vs 0.9% vs 0.5%; P = .05), but lower prevalence of BRAF mutation (7.7% vs 8.5% vs 16.7%; P = .002). Conclusions and Relevance: In this cohort study of 1959 patients, those with early-onset mCRC showed worse survival outcomes and unique adverse event patterns, which could be partially attributed to distinct genomic profiles. These findings may inform individualized management approaches in patients with early-onset mCRC.

Genetic studies of paired metabolomes reveal enzymatic and transport processes at the interface of plasma and urine.

The kidneys operate at the interface of plasma and urine by clearing molecular waste products while retaining valuable solutes. Genetic studies of paired plasma and urine metabolomes may identify underlying processes. We conducted genome-wide studies of 1,916 plasma and urine metabolites and detected 1,299 significant associations. Associations with 40% of implicated metabolites would have been missed by studying plasma alone. We detected urine-specific findings that provide information about metabolite reabsorption in the kidney, such as aquaporin (AQP)-7-mediated glycerol transport, and different metabolomic footprints of kidney-expressed proteins in plasma and urine that are consistent with their localization and function, including the transporters NaDC3 (SLC13A3) and ASBT (SLC10A2). Shared genetic determinants of 7,073 metabolite-disease combinations represent a resource to better understand metabolic diseases and revealed connections of dipeptidase 1 with circulating digestive enzymes and with hypertension. Extending genetic studies of the metabolome beyond plasma yields unique insights into processes at the interface of body compartments.

Precision Prognostics for Cardiovascular Disease in Type 2 Diabetes: A Systematic Review and Meta-analysis.

Background Precision medicine has the potential to improve cardiovascular disease (CVD) risk prediction in individuals with type 2 diabetes (T2D). Methods We conducted a systematic review and meta-analysis of longitudinal studies to identify potentially novel prognostic factors that may improve CVD risk prediction in T2D. Out of 9380 studies identified, 416 studies met inclusion criteria. Outcomes were reported for 321 biomarker studies, 48 genetic marker studies, and 47 risk score/model studies. Results Out of all evaluated biomarkers, only 13 showed improvement in prediction performance. Results of pooled meta-analyses, non-pooled analyses, and assessments of improvement in prediction performance and risk of bias, yielded the highest predictive utility for N-terminal pro b-type natriuretic peptide (NT-proBNP) (high-evidence), troponin-T (TnT) (moderate-evidence), triglyceride-glucose (TyG) index (moderate-evidence), Genetic Risk Score for Coronary Heart Disease (GRS-CHD) (moderate-evidence); moderate predictive utility for coronary computed tomography angiography (low-evidence), single-photon emission computed tomography (low-evidence), pulse wave velocity (moderate-evidence); and low predictive utility for C-reactive protein (moderate-evidence), coronary artery calcium score (low-evidence), galectin-3 (low-evidence), troponin-I (low-evidence), carotid plaque (low-evidence), and growth differentiation factor-15 (low-evidence). Risk scores showed modest discrimination, with lower performance in populations different from the original development cohort. Conclusions Despite high interest in this topic, very few studies conducted rigorous analyses to demonstrate incremental predictive utility beyond established CVD risk factors for T2D. The most promising markers identified were NT-proBNP, TnT, TyG and GRS-CHD, with the highest strength of evidence for NT-proBNP. Further research is needed to determine their clinical utility in risk stratification and management of CVD in T2D.

Genome-wide mRNA profiling in urinary extracellular vesicles reveals stress gene signature for diabetic kidney disease.

Urinary extracellular vesicles (uEV) are a largely unexplored source of kidney-derived mRNAs with potential to serve as a liquid kidney biopsy. We assessed ∼200 uEV mRNA samples from clinical studies by genome-wide sequencing to discover mechanisms and candidate biomarkers of diabetic kidney disease (DKD) in Type 1 diabetes (T1D) with replication in Type 1 and 2 diabetes. Sequencing reproducibly showed >10,000 mRNAs with similarity to kidney transcriptome. T1D DKD groups showed 13 upregulated genes prevalently expressed in proximal tubules, correlated with hyperglycemia and involved in cellular/oxidative stress homeostasis. We used six of them (GPX3, NOX4, MSRB, MSRA, HRSP12, and CRYAB) to construct a transcriptional "stress score" that reflected long-term decline of kidney function and could even identify normoalbuminuric individuals showing early decline. We thus provide workflow and web resource for studying uEV transcriptomes in clinical urine samples and stress-linked DKD markers as potential early non-invasive biomarkers or drug targets.

Different roles of protein biomarkers predicting eGFR trajectories in people with chronic kidney disease and diabetes mellitus: a nationwide retrospective cohort study.

BACKGROUND: Chronic kidney disease (CKD) is a common comorbidity in people with diabetes mellitus, and a key risk factor for further life-threatening conditions such as cardiovascular disease. The early prediction of progression of CKD therefore is an important clinical goal, but remains difficult due to the multifaceted nature of the condition. We validated a set of established protein biomarkers for the prediction of trajectories of estimated glomerular filtration rate (eGFR) in people with moderately advanced chronic kidney disease and diabetes mellitus. Our aim was to discern which biomarkers associate with baseline eGFR or are important for the prediction of the future eGFR trajectory. METHODS: We used Bayesian linear mixed models with weakly informative and shrinkage priors for clinical predictors (n = 12) and protein biomarkers (n = 19) to model eGFR trajectories in a retrospective cohort study of people with diabetes mellitus (n = 838) from the nationwide German Chronic Kidney Disease study. We used baseline eGFR to update the models' predictions, thereby assessing the importance of the predictors and improving predictive accuracy computed using repeated cross-validation. RESULTS: The model combining clinical and protein predictors had higher predictive performance than a clinical only model, with an [Formula: see text] of 0.44 (95% credible interval 0.37-0.50) before, and 0.59 (95% credible interval 0.51-0.65) after updating by baseline eGFR, respectively. Only few predictors were sufficient to obtain comparable performance to the main model, with markers such as Tumor Necrosis Factor Receptor 1 and Receptor for Advanced Glycation Endproducts being associated with baseline eGFR, while Kidney Injury Molecule 1 and urine albumin-creatinine-ratio were predictive for future eGFR decline. CONCLUSIONS: Protein biomarkers only modestly improve predictive accuracy compared to clinical predictors alone. The different protein markers serve different roles for the prediction of longitudinal eGFR trajectories potentially reflecting their role in the disease pathway.

Endogenous incretin levels and risk of first incident cancer: a prospective cohort study.

Concerns have been raised regarding a potentially increased risk of cancer associated with treatment with glucagon-like peptide-1 (GLP-1) receptor agonists. Here, we explored whether fasting and oral glucose tolerance test post-challenge glucose-dependent insulinotropic peptide (GIP) and GLP-1 levels were associated with incident first cancer. Within the cardiovascular re-examination arm of the population-based Malmö Diet Cancer study (n = 3734), 685 participants with a previous cancer diagnosis were excluded, resulting in 3049 participants (mean age 72.2 ± 5.6 years, 59.5% women), of whom 485 were diagnosed with incident first cancer (median follow-up time 9.9 years). Multivariable Cox-regression and competing risk regression (death as competing risk) were used to explore associations between incretin levels and incident first cancer. Higher levels of fasting GLP-1 (462 incident first cancer cases/2417 controls) showed lower risk of incident first cancer in competing risk regression (sub-hazard ratio 0.90; 95% confidence interval 0.82-0.99; p = 0.022). No association was seen for fasting GIP, post-challenge GIP, or post-challenge GLP-1 and incident first cancer. In this prospective study, none of the fasting and post-challenge levels of GIP and GLP-1 were associated with higher risk of incident first cancer; by contrast, higher levels of fasting GLP-1 were associated with lower risk of incident first cancer.

Validation of the Spanish Acute Cystitis Symptoms Score (ACSS) in native Spanish-speaking women of Europe and Latin America.

INTRODUCTION AND OBJECTIVES: The Acute Cystitis Symptom Score (ACSS) is a patient self-reporting questionnaire for clinical diagnostics and patient-reported outcome (PRO), which may assess the symptoms and the effect on the quality of life in women with acute cystitis (AC). The current study aimed to create a validated Spanish version of the ACSS questionnaire. MATERIAL AND METHODS: The process of linguistic validation of the Spanish version of the ACSS consisted of the independent forward and backward translations, revision and reconciliation, and cognitive assessment. Clinical evaluation of the study version of the ACSS was carried out in clinics in Spain and Latin America. Statistical tests included the calculation of Cronbach's α, split-half reliability, specificity, sensitivity, diagnostic odds ratio, positive and negative likelihood ratio, and area under the receiver-operating characteristic curve (AUC). RESULTS: The study was performed on 132 patients [age (mean;SD) 45.0;17.8 years] with AC and 55 controls (44.5;12.2 years). Cronbach's α of the ACSS was 0.86, and the split-half reliability was 0.82. The summary scores of the ACSS domains were significantly higher in patients than in controls, 16.0 and 2.0 (p < 0.001), respectively. The predefined cut-off point of ≥6 for a summary score of the "Typical" domain resulted in a specificity of 83.6% and a sensitivity of 99.2% for the Spanish version of the ACSS. AUC was 0.91 [0.85; 0.97]. CONCLUSIONS: The validated Spanish ACSS questionnaire evaluates the symptoms and clinical outcomes of patients with AC. It can be used as a patient's self-diagnosis of AC, as a PRO measure tool, and help to rule out other pathologies in patients with voiding syndrome.

A regression discontinuity analysis of the social distancing recommendations for older adults in Sweden during COVID-19.

BACKGROUND: This article investigates the impact of a non-mandatory and age-specific social distancing recommendation on isolation behaviours and disease outcomes in Sweden during the first wave of the coronavirus disease 2019 (COVID-19) pandemic (March to July 2020). The policy stated that people aged 70 years or older should avoid crowded places and contact with people outside the household. METHODS: We used a regression discontinuity design-in combination with self-reported isolation data from COVID Symptom Study Sweden (n = 96 053; age range: 39-79 years) and national register data (age range: 39-100+ years) on severe COVID-19 disease (hospitalization or death, n = 21 804) and confirmed cases (n = 48 984)-to estimate the effects of the policy. RESULTS: Our primary analyses showed a sharp drop in the weekly number of visits to crowded places (-13%) and severe COVID-19 cases (-16%) at the 70-year threshold. These results imply that the age-specific recommendations prevented approximately 1800-2700 severe COVID-19 cases, depending on model specification. CONCLUSIONS: It seems that the non-mandatory, age-specific recommendations helped control COVID-19 disease during the first wave of the pandemic in Sweden, as opposed to not implementing a social distancing policy aimed at older adults. Our study provides empirical data on how populations may react to non-mandatory, age-specific social distancing policies in the face of a novel virus.

App-based COVID-19 syndromic surveillance and prediction of hospital admissions in COVID Symptom Study Sweden.

The app-based COVID Symptom Study was launched in Sweden in April 2020 to contribute to real-time COVID-19 surveillance. We enrolled 143,531 study participants (≥18 years) who contributed 10.6 million daily symptom reports between April 29, 2020 and February 10, 2021. Here, we include data from 19,161 self-reported PCR tests to create a symptom-based model to estimate the individual probability of symptomatic COVID-19, with an AUC of 0.78 (95% CI 0.74-0.83) in an external dataset. These individual probabilities are employed to estimate daily regional COVID-19 prevalence, which are in turn used together with current hospital data to predict next week COVID-19 hospital admissions. We show that this hospital prediction model demonstrates a lower median absolute percentage error (MdAPE: 25.9%) across the five most populated regions in Sweden during the first pandemic wave than a model based on case notifications (MdAPE: 30.3%). During the second wave, the error rates are similar. When we apply the same model to an English dataset, not including local COVID-19 test data, we observe MdAPEs of 22.3% and 19.0% during the first and second pandemic waves, respectively, highlighting the transferability of the prediction model.

Effect of prior antibiotic or chemotherapy treatment on immunotherapy response in non-small cell lung cancer.

BACKGROUND: Treatment outcomes of advanced non-small cell lung cancer (NSCLC) have substantially improved with immune checkpoint inhibitors (ICI), although only approximately 19% of patients respond to immunotherapy alone, increasing to 58% with the addition of chemotherapy. The gut microbiome has been recognized as a modulator of ICI response via its priming effect on the host immune response. Antibiotics as well as chemotherapy reduce gut microbial diversity, hence altering composition and function of the gut microbiome. Since the gut microbiome may modify ICI efficacy, we conducted a retrospective study evaluating the effects of prior antibiotic or chemotherapy use on NSCLC patient response to ICI. METHODS: We retrospectively evaluated 256 NSCLC patients treated between 2011-2017 at Moffitt Cancer Center with ICI ± chemotherapy, examining the associations between prior antibiotic or chemotherapy use, overall response rate and survival. Relative risk regression using a log-link with combinatorial expectation maximization algorithm was performed to analyze differences in response between patients treated with antibiotics or chemotherapy versus patients who didn't receive antibiotics or chemotherapy. Cox proportional hazards models were constructed to evaluate associations between risk factors and overall survival. RESULTS: Only 46 (18% of 256) patients used antibiotics prior to and/or during ICI treatment, and 146 (57%) had prior chemotherapy. Antibiotic users were 8% more likely to have worse overall response rate (RR:1.08; CI:0.93-1.26; p = 0.321), as well as a 35% worse overall survival (HR:1.35; CI:0.91-2.02; p = 0.145), although results were not statistically significant. However, prior use of chemotherapy was significantly associated with poor ICI response (RR:1.24; CI:1.05-1.47; p = 0.013) and worse overall survival (HR:1.47; CI:1.07-2.03; p = 0.018). CONCLUSIONS: Patients receiving antibiotics prior to and/or during ICI therapy might experience worse treatment outcomes and survival than unexposed patients, although these associations were not statistically significant and hence warrant further prospective study. Prior chemotherapy significantly reduced ICI response and overall survival. Antibiotic or chemotherapy exposure may negatively impact ICI response, perhaps through disruption of the eubiotic gut microbiome.

Validation of the classification for type 2 diabetes into five subgroups: a report from the ORIGIN trial.

AIMS/HYPOTHESIS: Data analyses from Swedish individuals with newly diagnosed diabetes have suggested that diabetes could be classified into five subtypes that differ with respect to the progression of dysglycaemia and the incidence of diabetes consequences. We assessed this classification in a multiethnic cohort of participants with established and newly diagnosed diabetes, randomly allocated to insulin glargine vs standard care. METHODS: In total, 7017 participants from the Outcome Reduction with Initial Glargine Intervention (ORIGIN) trial were assigned to the five predefined diabetes subtypes (namely, severe auto-immune diabetes, severe insulin-deficient diabetes, severe insulin-resistant diabetes, mild obesity-related diabetes, mild age-related diabetes) based on the age at diabetes diagnosis, BMI, HbA1c, fasting C-peptide levels and the presence of glutamate decarboxylase antibodies at baseline. Differences between diabetes subtypes in cardiovascular and renal outcomes were investigated using Cox regression models for a median follow-up of 6.2 years. We also compared the effect of glargine vs standard care on hyperglycaemia, defined by having a mean post-randomisation HbA1c ≥6.5%, between subtypes. RESULTS: The five diabetes subtypes were replicated in the ORIGIN trial and exhibited similar baseline characteristics in Europeans and Latin Americans, compared with the initially described clusters in the Swedish cohort. We confirmed differences in renal outcomes, with a higher incidence of events in the severe insulin-resistant diabetes subtype compared with the mild age-related diabetes subtype (i.e., chronic kidney disease stage 3A: HR 1.49 [95% CI 1.31, 1.71]; stage 3B: HR 2.25 [1.82, 2.78]; macroalbuminuria: HR 1.56 [1.22, 1.99]). No differences were observed in the incidence of retinopathy and cardiovascular diseases after adjusting for multiple hypothesis testing. Diabetes subtypes also differed in glycaemic response to glargine, with a particular benefit of receiving glargine (vs standard care) in the severe insulin-deficient diabetes subtype compared with the mild age-related diabetes subtype, with a decreased occurrence of hyperglycaemia by 13% (OR 1.36 [1.30, 1.41] on glargine; OR 1.49 [1.43, 1.57] on standard care; p for interaction subtype × intervention = 0.001). CONCLUSIONS/INTERPRETATION: Cluster analysis enabled the characterisation of five subtypes of diabetes in a multiethnic cohort. Both the incidence of renal outcomes and the response to insulin varied between diabetes subtypes. These findings reinforce the clinical utility of applying precision medicine to predict comorbidities and treatment responses in individuals with diabetes. TRIAL REGISTRATION: ORIGIN trial, ClinicalTrials.gov NCT00069784.

Perspectives on a Way Forward to Implementation of Precision Medicine in Patients With Diabetic Kidney Disease; Results of a Stakeholder Consensus-Building Meeting.

Aim: This study aimed to identify from different stakeholders the benefits and obstacles of implementing precision medicine in diabetic kidney disease (DKD) and to build consensus about a way forward in order to treat, prevent, or even reverse this disease. Methods: As part of an ongoing effort of moving implementation of precision medicine in DKD forward, a two-day consensus-building meeting was organized with different stakeholders involved in drug development and patient care in DKD, including patients, patient representatives, pharmaceutical industry, regulatory agencies representatives, health technology assessors, healthcare professionals, basic scientists, and clinical academic researchers. The meeting consisted of plenary presentations and discussions, and small group break-out sessions. Discussion topics were based on a symposium, focus groups and literature search. Benefits, obstacles and potential solutions toward implementing precision medicine were discussed. Results from the break-out sessions were presented in plenary and formed the basis of a broad consensus discussion to reach final conclusions. Throughout the meeting, participants answered several statement and open-ended questions on their mobile device, using a real-time online survey tool. Answers to the statement questions were analyzed descriptively. Results of the open-ended survey questions, the break-out sessions and the consensus discussion were analyzed qualitatively. Results and conclusion: Seventy-one participants from 26 countries attended the consensus-building meeting in Amsterdam, April 2019. During the opening plenary on the first day, the participants agreed with the statement that precision medicine is the way forward in DKD (n = 57, median 90, IQR [75-100]). Lack of efficient tools for implementation in practice and generating robust data were identified as significant obstacles. The identified benefits, e.g., improvement of the benefit-risk ratio of treatment, offer substantive incentives to find solutions for the identified obstacles. Earlier and increased multi-stakeholder collaboration and specific training may provide solutions to alter clinical and regulatory guidelines that lie at the basis of both obstacles and solutions. At the end of the second day, the opinion of the participants toward precision medicine in DKD was somewhat more nuanced (n = 45, median 83, IQR [70-92]) and they concluded that precision medicine is an important way forward in improving the treatment of patients with DKD.

Symptoms and syndromes associated with SARS-CoV-2 infection and severity in pregnant women from two community cohorts.

We tested whether pregnant and non-pregnant women differ in COVID-19 symptom profile and severity, and we extended previous investigations on hospitalized pregnant women to those who did not require hospitalization. Two female community-based cohorts (18-44 years) provided longitudinal (smartphone application, N = 1,170,315, n = 79 pregnant tested positive) and cross-sectional (web-based survey, N = 1,344,966, n = 134 pregnant tested positive) data, prospectively collected through self-participatory citizen surveillance in UK, Sweden and USA. Pregnant and non-pregnant were compared for frequencies of events, including SARS-CoV-2 testing, symptoms and hospitalization rates. Multivariable regression was used to investigate symptoms severity and comorbidity effects. Pregnant and non-pregnant women positive for SARS-CoV-2 infection were not different in syndromic severity, except for gastrointestinal symptoms. Pregnant were more likely to have received testing, despite reporting fewer symptoms. Pre-existing lung disease was most closely associated with syndromic severity in pregnant hospitalized. Heart and kidney diseases and diabetes increased risk. The most frequent symptoms among non-hospitalized women were anosmia [63% pregnant, 92% non-pregnant] and headache [72%, 62%]. Cardiopulmonary symptoms, including persistent cough [80%] and chest pain [73%], were more frequent among pregnant who were hospitalized. Consistent with observations in non-pregnant populations, lung disease and diabetes were associated with increased risk of more severe SARS-CoV-2 infection during pregnancy.

Attributes and predictors of long COVID.

Reports of long-lasting coronavirus disease 2019 (COVID-19) symptoms, the so-called 'long COVID', are rising but little is known about prevalence, risk factors or whether it is possible to predict a protracted course early in the disease. We analyzed data from 4,182 incident cases of COVID-19 in which individuals self-reported their symptoms prospectively in the COVID Symptom Study app1. A total of 558 (13.3%) participants reported symptoms lasting ≥28 days, 189 (4.5%) for ≥8 weeks and 95 (2.3%) for ≥12 weeks. Long COVID was characterized by symptoms of fatigue, headache, dyspnea and anosmia and was more likely with increasing age and body mass index and female sex. Experiencing more than five symptoms during the first week of illness was associated with long COVID (odds ratio = 3.53 (2.76-4.50)). A simple model to distinguish between short COVID and long COVID at 7 days (total sample size, n = 2,149) showed an area under the curve of the receiver operating characteristic curve of 76%, with replication in an independent sample of 2,472 individuals who were positive for severe acute respiratory syndrome coronavirus 2. This model could be used to identify individuals at risk of long COVID for trials of prevention or treatment and to plan education and rehabilitation services.

Symptom clusters in COVID-19: A potential clinical prediction tool from the COVID Symptom Study app.

As no one symptom can predict disease severity or the need for dedicated medical support in coronavirus disease 2019 (COVID-19), we asked whether documenting symptom time series over the first few days informs outcome. Unsupervised time series clustering over symptom presentation was performed on data collected from a training dataset of completed cases enlisted early from the COVID Symptom Study Smartphone application, yielding six distinct symptom presentations. Clustering was validated on an independent replication dataset between 1 and 28 May 2020. Using the first 5 days of symptom logging, the ROC-AUC (receiver operating characteristic - area under the curve) of need for respiratory support was 78.8%, substantially outperforming personal characteristics alone (ROC-AUC 69.5%). Such an approach could be used to monitor at-risk patients and predict medical resource requirements days before they are required.