Circulating Folate Concentrations and Risk of Peripheral Neuropathy and Mortality: A Retrospective Cohort Study in the U.K

BACKGROUND: Folate deficiency may increase the risk of peripheral neuropathy but there is a paucity of data from large prospective studies examining this association. METHODS: Longitudinal analysis of electronic health records in The Health Improvement Network (THIN), a U.K. primary care database including 594,338 patients aged 18-70 years with a folate measurement and without a history of peripheral neuropathy. RESULTS: After a mean follow-up of 3.71 (standard deviation (SD) = 3.14) years, 1949 patients were diagnosed with peripheral neuropathy and 20,679 patients died. In those <40 years, compared to patients with folate ≥13.6 nmol/L, those with folate <6.8 (deficient) and 6.8-13.5 nmol/L (insufficient) had a hazard ratio (HR) for peripheral neuropathy of 1.83 (95% confidence intervals (CI) = 1.16-2.91) and 1.48 (95% CI = 1.04-2.08), respectively. There was no significant association between folate and peripheral neuropathy among those aged 41-70 years. Compared to patients with folate ≥ 13.6 nmol/L, folate <6.8 nmol/L was associated with a greater risk of death among all ages. CONCLUSION: Folate deficiency and insufficiency was associated with a greater risk of peripheral neuropathy among younger patients. This investigation should be replicated in other large datasets and it may be important to monitor peripheral neuropathy incidence after the introduction of mandatory folic acid fortification of flour in the U.K

The clinical profile and associated mortality in people with and without diabetes with Coronavirus disease 2019 on admission to acute hospital services

Introduction: To assess if in adults with COVID-19, whether those with diabetes and complications (DM+C) present with a more severe clinical profile and if that relates to increased mortality, compared to those with diabetes with no complications (DM-NC) and those without diabetes. Methods: Service-level data was used from 996 adults with laboratory confirmed COVID-19 who presented to the Queen Elizabeth Hospital Birmingham, UK, from March to June 2020. All individuals were categorized into DM+C, DM-NC, and non-diabetes groups. Physiological and laboratory measurements in the first 5 days after admission were collated and compared among groups. Cox proportional hazards regression models were used to evaluate associations between diabetes status and the risk of mortality. Results: Among the 996 individuals, 104 (10.4%) were DM+C, 295 (29.6%) DM-NC and 597 (59.9%) non-diabetes. There were 309 (31.0%) in-hospital deaths documented, 40 (4.0% of total cohort) were DM+C, 99 (9.9%) DM-NC and 170 (17.0%) non-diabetes. Individuals with DM+C were more likely to present with high anion gap/metabolic acidosis, features of renal impairment, and low albumin/lymphocyte count than those with DM-NC or those without diabetes. There was no significant difference in mortality rates among the groups: compared to individuals without diabetes, the adjusted HRs were 1.39 (95% CI 0.95–2.03, p = 0.093) and 1.18 (95% CI 0.90–1.54, p = 0.226) in DM+C and DM-C, respectively. Conclusions: Those with COVID-19 and DM+C presented with a more severe clinical and biochemical profile, but this did not associate with increased mortality in this study

Symptoms and risk factors for long COVID in non-hospitalized adults

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection is associated with a range of persistent symptoms impacting everyday functioning, known as post-COVID-19 condition or long COVID. We undertook a retrospective matched cohort study using a UK-based primary care database, Clinical Practice Research Datalink Aurum, to determine symptoms that are associated with confirmed SARS-CoV-2 infection beyond 12 weeks in non-hospitalized adults and the risk factors associated with developing persistent symptoms. We selected 486,149 adults with confirmed SARS-CoV-2 infection and 1,944,580 propensity score-matched adults with no recorded evidence of SARS-CoV-2 infection. Outcomes included 115 individual symptoms, as well as long COVID, defined as a composite outcome of 33 symptoms by the World Health Organization clinical case definition. Cox proportional hazards models were used to estimate adjusted hazard ratios (aHRs) for the outcomes. A total of 62 symptoms were significantly associated with SARS-CoV-2 infection after 12 weeks. The largest aHRs were for anosmia (aHR 6.49, 95% CI 5.02–8.39), hair loss (3.99, 3.63–4.39), sneezing (2.77, 1.40–5.50), ejaculation difficulty (2.63, 1.61–4.28) and reduced libido (2.36, 1.61–3.47). Among the cohort of patients infected with SARS-CoV-2, risk factors for long COVID included female sex, belonging to an ethnic minority, socioeconomic deprivation, smoking, obesity and a wide range of comorbidities. The risk of developing long COVID was also found to be increased along a gradient of decreasing age. SARS-CoV-2 infection is associated with a plethora of symptoms that are associated with a range of sociodemographic and clinical risk factors

Obesity Without Metabolic Abnormality and Incident CKD: A Population-Based British Cohort Study.

RATIONALE & OBJECTIVE Metabolically healthy obesity (obesity without any metabolic abnormality) is considered not to be associated with increased risk of morbidity and mortality. We aimed to examine and quantify the association between metabolically healthy overweight/obesity and the risk of incident chronic kidney disease (CKD) in a British primary care population. DESIGN Retrospective population-based cohort study. SETTING & PARTICIPANTS 4,447,955 of the 5,182,908 adults in The Health Improvement Network (THIN) database (United Kingdom, 1995-2015) with a recorded body mass index (BMI) at the time of registration date who were free of CKD and cardiovascular disease. EXPOSURES 11 body size phenotypes were created, defined by body mass index categories (underweight, normal weight, overweight, and obesity) and 3 metabolic abnormalities (diabetes, hypertension, and dyslipidemia). OUTCOMES Incident CKD defined as a recorded diagnosis of kidney replacement therapy (KRT), a recorded diagnosis of CKD, or by an estimated glomerular filtration rate 3 mg/mmol for ≥90 days. RESULTS Of the 4.5 million individuals, 1,040,921 (23.4%) and 588,909 (13.2%) were metabolically healthy overweight and metabolically healthy obese, respectively. During a mean follow-up of 5.4 (SD 4.3) years, compared to individuals with metabolically healthy normal weight (n=1,656,231), those who had metabolically healthy overweight (adjusted HR = 1.30, 95% CI 1.28 to 1.33) and metabolically healthy obesity (adjusted HR = 1.66, 95% CI 1.62 to 1.70) had a higher risk of incident CKD. The association was stronger in those younger than 65 years of age. The risk of incident CKD in all BMI categories increased with an increasing number of metabolic abnormalities in a graded manner. LIMITATIONS Potential misclassification of metabolic status due to delayed diagnosis and residual confounding due to unmeasured factors. CONCLUSIONS Overweight and obesity without metabolic abnormality are associated with a higher risk of incident CKD compared to those with normal body weight and no metabolic abnormality

In simulated data and health records, latent class analysis was the optimum multimorbidity clustering algorithm.

BACKGROUND AND OBJECTIVES: To investigate the reproducibility and validity of latent class analysis (LCA) and hierarchical cluster analysis (HCA), multiple correspondence analysis followed by k-means (MCA-kmeans) and k-means (kmeans) for multimorbidity clustering. METHODS: We first investigated clustering algorithms in simulated datasets with 26 diseases of varying prevalence in predetermined clusters, comparing the derived clusters to known clusters using the adjusted Rand Index (aRI). We then them investigated the medical records of male patients, aged 65 to 84 years from 50 UK general practices, with 49 long-term health conditions. We compared within cluster morbidity profiles using the Pearson correlation coefficient and assessed cluster stability using in 400 bootstrap samples. RESULTS: In the simulated datasets, the closest agreement (largest aRI) to known clusters was with LCA and then MCA-kmeans algorithms. In the medical records dataset, all four algorithms identified one cluster of 20-25% of the dataset with about 82% of the same patients across all four algorithms. LCA and MCA-kmeans both found a second cluster of 7% of the dataset. Other clusters were found by only one algorithm. LCA and MCA-kmeans clustering gave the most similar partitioning (aRI 0.54). CONCLUSION: LCA achieved higher aRI than other clustering algorithms