Enhancing type 2 diabetes treatment through digital plans of care - a randomized controlled trial: evaluation of change in patient reported outcome measures.

BACKGROUND: We investigated how a personalized care-planning software and linked mobile-app may aid people to self-manage their type 2 diabetes (T2D) more effectively. RESEARCH DESIGN AND METHODS: People with T2D and glycated hemoglobin (HbA1c) greater than 58 mmol/mol (7.5%) were randomized to either an intervention group receiving a personalized care plan, or the control group receiving usual care. Quality of life (QoL) was measured for both groups using validated questionnaires and one-on-one interviews with a subset of 12 participants from each group. RESULTS: QoL for the active treatment group increased, by their EQ -5D-5 L score increasing on average by 0.046, whereas it decreased for the control group on average by 0.009. The EQ Visual Analogue Score (VAS) of the intervention group also increased by 8.2%, whereas the control group had a reduction in EQ VAS score of 2.8% (p = 0.008 for difference). CONCLUSION: In this prospective RCT, the findings point to how the provision of personalized care plans can result in an improvement in individuals' self-rated QoL. This may lead to longer term health benefits.

Macroprolactin over time: Is there any point in rechecking it in people with a persistently elevated serum prolactin?

OBJECTIVE AND DESIGN: Macroprolactinemia may influence the interpretation of serum prolactin levels-a recognised phenomenon since 1981. The degree of macroprolactinaemia over time is less well described. We determined how macroprolactin status (based on polyethylene glycol (PEG) precipitation) varied by analysing serial measurements in hyperprolactinaemic individuals over a period of 9 years. PATIENTS AND MEASUREMENTS: Results from 1810 individuals were included. All serum total prolactin results (measured using Roche Cobas 8000 analyser) were extracted from the laboratory information system for the period 1 January 2012 to 1 April 2021, along with relevant patient demographic/test data. Samples with a macroprolactin screening test performed (on samples with prolactin > 700 miu/L) were included in the main analysis. RESULTS: During the study period, 2782 macroprolactin checks were performed (12.5% of all prolactin tests) in 1810 individuals (599 males/2183 females, median-age: 35, interquartile range: 25-47, range: 16-93 years). Multiple macroprolactin checks were carried out on 465 patients (1437 measurements) with 94 patients (141 measurements) screening positive (<60% recovery). Only 19 patients (18 female) had at least one result above and one below the 60% screening cut-off, with 10 of these patients having results close to the 60% cut-off; in 9 patients, results were clearly different between repeat samples. In seven cases, the adjusted monomeric prolactin showed a potentially clinically significant difference. CONCLUSIONS: In this study, only 19/465 patients appeared to change macroprolactin status based on a 60% PEG recovery cut-off. The majority of these 19 patients were on antipsychotic/antidepressant medication(s) or had a prolactinoma; in only 7 did monomeric prolactin change significantly. This suggests that once macroprolactin status has been determined, clinical decision making is rarely affected by repeating it.

Effect of the COVID-19 pandemic on mental health visits in primary care: an interrupted time series analysis from nine INTRePID countries.

Background: The COVID-19 pandemic impacted mental health disorders, affecting both individuals with pre-existing conditions and those with no prior history. However, there is limited evidence regarding the pandemic's impact on mental health visits to primary care physicians. The International Consortium of Primary Care Big Data Researchers (INTRePID) explored primary care visit trends related to mental health conditions in Argentina, Australia, Canada, China, Norway, Peru, Singapore, Sweden, and the USA. Methods: We conducted an interrupted time series analysis in nine countries to examine changes in rates of monthly mental health visits to primary care settings from January 1st, 2018, to December 31st, 2021. Sub-group analysis considered service type (in-person/virtual) and six categories of mental health conditions (anxiety/depression, bipolar/schizophrenia/other psychotic disorders, sleep disorders, dementia, ADHD/eating disorders, and substance use disorder). Findings: Mental health visit rates increased after the onset of the pandemic in most countries. In Argentina, Canada, China, Norway, Peru, and Singapore, this increase was immediate ranged from an incidence rate ratio of 1·118 [95% CI 1.053-1.187] to 2.240 [95% CI 2.057-2.439] when comparing the first month of pandemic with the pre-pandemic trend. Increases in the following months varied across countries. Anxiety/depression was the leading reason for mental health visits in most countries. Virtual visits were reported in Australia, Canada, Norway, Peru, Sweden, and the USA, accounting for up to 40% of the total mental health visits. Interpretation: Findings suggest an overall increase in mental health visits, driven largely by anxiety/depression. During the COVID-19 pandemic, many of the studied countries adopted virtual care in particular for mental health visits. Primary care plays a crucial role in addressing mental ill-health in times of crisis. Funding: Canadian Institutes of Health Research grant #173094 and the Rathlyn Foundation Primary Care EMR Research and Discovery Fund.

Counting the lifetime cost of obesity: Analysis based on national England data.

AIM: Obesity has a significant impact on all-cause mortality rate and overall health care resource use (HCRU). These outcomes are also strongly linked to age, sex and local deprivation of the population. We aimed to establish the lifetime costs of obesity by demographic group/geographic area using published mortality rates and HCRU use for integrated care boards (ICB) in England in the context of costs of therapeutic intervention. METHODS: Population and expected mortality rates by age, sex and deprivation were obtained from national data. Obesity class prevalence was taken from the health of the nation study. The published impact of obesity by age, group, sex and deprivation on mortality and HCRU were applied to estimate life years lost and lifetime HCRU [by sex, age band and body mass index (BMI) class for each ICB]. The year 2019 was chosen as the study basis data to avoid influences of COVID-19 pandemic on obesity rates with application of 2022/23 HCRU values. Outcomes including prevalence, deaths, life years lost, HCRU and lifetime HCRU were compared by age and sex groups across four BMI classes normal/underweight (BMI <25 kg/m2 ), overweight (25-29.9 kg/m2 ), obese class I and II (30-39.9 kg/m2 ), and obese class III (≥40), with benchmarking being set against all population being BMI <25 kg/m2 overall and by each of the 42 ICBs. We also associated future life with deaths to provide an estimate of 'future life years lost' occurring each year. RESULTS: Total population aged >16 years was 45.4 million (51% female). PREVALENCE: 13.7 million (28% of the total adult population) had a BMI ≥30 mg/m2 and BMI ≥40 kg/m2 were 1.50 million (12%) of these 1.0 million (68%) were female and of these 0.6 million 40% were women aged 16-49 years. In addition, 35% of those with a BMI ≥40 kg/m2 were in the top deprivation quintile (i.e. overall 20%). Mortality was based on expected deaths of 518K/year, and modelling suggested that if a BMI <25 kg/m2 was achieved in all individuals, the death rate would fall by 63K to 455K/year for the English population (12% reduction). For those with a BMI ≥40 kg/m2 the predicted reduction was 12K deaths (54% lower); while in those aged 16-49 years with a BMI ≥40 kg/m2 72% of deaths were linked to obesity. For future life years lost, we estimated 2.5 years were lost in people with BMI 30-39.9 kg/m2 6.7 years when BMI ≥40 kg/m2 . However, for those aged 16-49 years with a BMI ≥40 kg/m2 , 8.3 years were lost. HCRU, for weight reduction, the annual HCRU decrease from BMI ≥40 kg/m2 to BMI 30-39.9 kg/m2 was £342 per person and from BMI 30-39.9 to 25-29.9 kg/m2 the reduction was £316/person. However, lifetime costs were similar because of reduced life expectancy for obese individuals. In quality adjusted life years (QALY), overall, 791 689 future life years were lost (13.1% of all) in people with BMI ≥25 kg/m2 and were related to excess weight. When the NICE £30 000 per QALY value was applied to the estimated total 791 689 future life years lost then the potential QALY value reduction lost was equivalent to £24 billion/year or £522/person in the obese population. For morbidly obese men and women the potential QALY value lost was £2864/person/year. Regarding geography, across the 42 ICBs, we observed significant variation in the prevalence of BMI ≥40 (1.8%-4.3%), excess mortality (11.6%-15.4%) and HCRU linked to higher BMI (7.2%-8.8%). The areas with the greatest impact on HCRU were in the north-west, north-east and Midlands of England, while the south shows less impact. CONCLUSION: The expected increases in annual HCRU because of obesity, when considered over a lifetime, are being mitigated by the increased mortality of obese individuals. Our data suggest that simple short-term HCRU reduction brought about through BMI reduction will be insufficient to fund additional specialist weight reduction interventions. The HRCUs associated with BMI are not in most cases related to short-term health conditions. They are a cumulative result over a number of years, so for age 16-49 years reducing BMI from ≥40 to 30-39.9 kg/m2 might show an annual decrease in HCRU/person by £325/year for women and £80/year for men but this might not have immediately occurred within that year. For those aged >70 years reducing BMI from ≥40 to 30-39.9 kg/m2 might show an annual decrease in HCRU/person by £777/year for women and £796/year for men but also may not be manifest within that year. However, for the morbidly obese men and women, the potential QALY value lost was £2864 per person per year with the potential for these funds to be applied to intensive weight management programmes, including pharmacotherapy.

Weight change following diagnosis with psychosis: a retrospective cohort study in Greater Manchester, UK.

INTRODUCTION: Weight gain in the months/years after diagnosis/treatment of severe enduring mental illness (SMI) is a major predictor of future diabetes, dysmetabolic profile and increased risk of cardiometabolic diseases. There is limited data on the longer-term profile of weight change in people with a history of SMI and how this may differ between individuals. We here report a retrospective study on weight change over the 5 years following an SMI diagnosis in Greater Manchester UK, an ethnically and culturally diverse community, with particular focus on comparing non-affective psychosis (NAP) vs affective psychosis (AP) diagnoses. METHODS: We undertook an anonymised search in the Greater Manchester Care Record (GMCR). We reviewed the health records of anyone who had been diagnosed for the first time with first episode psychosis, schizophrenia, schizoaffective disorder, delusional disorder (non-affective psychosis = NAP) or affective psychosis (AP). We analysed body mass index (BMI) change in the 5-year period following the first prescription of antipsychotic medication. All individuals had taken an antipsychotic agent for at least 3 months. The 5-year follow-up point was anywhere between 2003 and 2023. RESULTS: We identified 9125 people with the diagnoses above. NAP (n = 5618; 37.3% female) mean age 49.9 years; AP (n = 4131; 60.5% female) mean age 48.7 years. 27.0% of NAP were of non-White ethnicity vs 17.8% of AP individuals. A higher proportion of people diagnosed with NAP were in the highest quintile of social disadvantage 52.4% vs 39.5% for AP. There were no significant differences in baseline BMI profile. In a subsample with HbA1c data (n = 2103), mean HbA1c was higher in NAP at baseline (40.4 mmol/mol in NAP vs 36.7 mmol/mol for AP). At 5-year follow-up, there was similarity in both the overall % of individuals in the obese ≥ 30 kg/m2 category (39.8% NAP vs 39.7% AP), and % progressing from a normal healthy BMI transitioned to obese/overweight BMI (53.6% of NAP vs 55.6% with AP). 43.7% of those NAP with normal BMI remained at a healthy BMI vs 42.7% with AP. At 5-year follow-up for NAP, 83.1% of those with BMI ≥ 30 kg/m2 stayed in this category vs 81.5% of AP. CONCLUSION: The results of this real-world longitudinal cohort study suggest that the changes in BMI with treatment of non-affective psychosis vs bipolar disorder are not significantly different, while 43% maintain a healthy weight in the first 5 years following antipsychotic prescription.

Is the Current Cut Point for Glycated Haemoglobin (HbA1c) Correct for Diagnosing Diabetes Mellitus in Premenopausal Women? Evidence to Inform Discussion.

INTRODUCTION: Women are on average diagnosed with diabetes mellitus at later age than men but have higher mortality. As the diagnosis of diabetes mellitus is primarily based on HbA1c, the use of a non-specific reference range and cut point for diabetes mellitus that does not account for gender differences in diabetes could potentially lead to underdiagnosis of diabetes mellitus in women and missed opportunities for intervention. We investigated whether a contributing factor to the later diagnosis in women may be a difference in distribution of HbA1c in premenopausal women versus men of the same age by comparing HbA1c values in men and women across multiple sites in the UK. METHODS: We analysed the HbA1c levels of 146,907 individuals who underwent single testing only and had HbA1c ≤ 50 mmol/mol between 2012 and 2019 in one laboratory (cohort 1). This was replicated in six laboratories with 938,678 individuals tested between 2019 and 2021 (cohort 2). RESULTS: In cohort 1, women < 50 years old had an HbA1c distribution markedly lower than that in men by a mean of 1.6 mmol/mol (p < 0.0001), while the difference in the distribution of HbA1c for individuals aged ≥ 50 years was less pronounced (mean difference 0.9 mmol/mol, p < 0.0001). For individuals under the age of 50, HbA1c in women lagged by up to 10 years compared to men. Similar findings were found in cohort 2. We estimated an additional 17% (n = 34,953) of undiagnosed women aged < 50 years in England and Wales could be reclassified to have diabetes mellitus, which may contribute to up to 64% of the difference in mortality rates between men/women with diabetes mellitus aged 16-50 years. CONCLUSION: The HbA1c cut point for diagnosis of diabetes mellitus may need to be re-evaluated in women under the age of 50 years. Early identification of diabetes mellitus in women has the potential to improve women's health outcomes in the longer term.

Effectiveness of locally produced ready-to-use supplementary foods on the prevention of stunting in children aged 6-23 months: a community-based trial from Pakistan.

Undernutrition is a major public health problem in developing countries. Around 40·2 % of children are stunted in Pakistan. This longitudinal study aimed to assess the effectiveness of locally produced ready-to-use supplementary foods in the prevention of stunting by detecting change in of children in intervention v. control arm against the 2006 WHO growth reference. A community-based non-randomised cluster-controlled trial was conducted from January 2018 to December 2020 in the district of Kurram, Khyber Pakhtunkhwa, Pakistan. A total of 80 clusters (each cluster comprising ≈ 250-300 households) were defined in the catchment population of twelve health facilities. Children aged 6-18 months were recruited n 1680. The intervention included a daily ration of 50 g - locally produced ready-to-use-supplementary food (Wawa-Mum). The main outcome of this study was a change in length for age z-score (LAZ) v. WHO growth standards. Comparison between the interventions was by t test and ANOVA. Cox proportional hazard models were used to assess the association between stunting occurrence and the utilisation of locally produced supplement. Out of the total 1680, fifty-one out of the total 1680, 51·1 out of the total 1680 and 51·1 % (n 859) were male. Mean age 13·9 months (sd + 859) were male. Mean age 13·9 months (sd + -4·4). At baseline, 36·9 % (n 618) were stunted. In the intervention group, mean LAZ score significantly increased from -1·13(2·2 sd) at baseline to -0·93(1·8 sd) at 6-month follow-up (P value 0·01) compared with the control group. The incidence rate of stunting in the intervention arm was 1·3 v. 3·4 per person year in the control arm. The control group had a significantly increased likelihood of stunting (Hazard Ratio (HR) 1·7, 95 % CI 1·46, 2·05, P value < 0·001) v. the intervention group. Locally produced ready-to-use supplementary food is an effective intervention for reducing stunting in children below 2 years of age. This can be provided as part of a malnutrition prevention package to overcome the alarming rates of stunting in Pakistan.

Impact of Bariatric Surgery on Subtilisin/Kexin Type 9 (PCSK9) Gene Expression and Inflammation in the Adipose Tissue of Obese Diabetic Rats.

Bariatric surgery improves dyslipidaemia and reduces body weight, but it remains unclear how bariatric surgery modulates gene expression in fat cells to influence the proprotein convertase subtilisin/kexin type 9 (PCSK-9) and low-density lipoprotein receptor (LDLR) gene expression. The expression of the PCSK9/LDLR/tumor necrosis factor-alpha (TNFα) gene in adipose tissue was measured in two groups of Zucker Diabetic Sprague Dawley (ZDSD) rats after Roux-en-Y gastric bypass (RYGB) surgery or 'SHAM' operation. There was lower PCSK9 (p = 0.02) and higher LDLR gene expression (p = 0.02) in adipose tissue in rats after RYGB. Weight change did not correlate with PCSK9 gene expression (r = -0.5, p = 0.08) or TNFα gene expression (r = -0.4, p = 0.1). TNFα gene expression was positively correlated with PCSK9 gene expression (r = 0.7, p = 0.001) but not correlated with LDLR expression (r = -0.3, p = 0.3). Circulating triglyceride levels were lower in RYGB compared to the SHAM group (1.1 (0.8-1.4) vs. 1.5 (1.0-4.2), p = 0.038) mmol/L with no difference in cholesterol levels. LDLR gene expression was increased post-bariatric surgery with the potential to reduce the number of circulating LDL particles. PCSK9 gene expression and TNFα gene expression were positively correlated after RYGB in ZDSD rats, suggesting that the modulation of pro-inflammatory pathways in adipose tissue after RYGB may partly relate to PCSK9 and LDLR gene expression.

Adult Male Hypogonadism: A Laboratory Medicine Perspective on Its Diagnosis and Management.

Testosterone (T), the principal androgen secreted by the testes, plays an essential role in male health. Male hypogonadism is diagnosed based on a combination of associated clinical signs and symptoms and laboratory confirmation of low circulating T levels. In this review, we have highlighted factors, both biological and analytical, that introduce variation into the measurement of serum T concentrations in men; these need to be considered when requesting T levels and interpreting results. There is an ongoing need for analytical standardisation of T assays and harmonisation of pre- and post-analytical laboratory practices, particularly in relation to the laboratory reference intervals provided to clinicians. Further, there is a need to share with service users the most up-to-date and evidence-based action thresholds for serum T as recommended in the literature. Estimation of free testosterone may be helpful. Causes of secondary hypogonadism should be considered. A comprehensive approach is required in the management of male hypogonadism, including lifestyle modification as well as medication where appropriate. The goal of treatment is the resolution of symptoms as well as the optimisation of metabolic, cardiovascular, and bone health. The advice of an endocrinologist should be sought when there is doubt about the cause and appropriate management of the hypogonadism.

Changes in prescribing of psychotropic vs some physical health medication in primary care through the COVID-19 pandemic in England: a national-level survey.

INTRODUCTION: The COVID-19 pandemic globally impacted healthcare provision. Prescribing changes in common medications can be used as a marker for new diagnoses. We describe how the prescribing of specific psychotropics was impacted by the pandemic. METHODS: Primary Care Prescribing data for different classes of drugs from March 2017 to February 2022 were considered. To capture the impact during periods of restricted access to health services for new diagnoses/existing conditions, repeat prescriptions/episodic prescribing were included with account taken of historical trends. The pre-pandemic prescriptions issued each month from March 2018 to February 2020 were linearly extrapolated forward to give an expected annual growth (EAG). The monthly average expected prescriptions for the pandemic period (March 2020-February 2022) were compared. RESULTS: Physical health medications had lower monthly prescriptions during the pandemic, most markedly for antibiotics - 12.5% (EAG - 1.3%). Bronchodilator prescribing showed a marked increase in the early pandemic months from March 2020 of 5% (EAG 0.1%). Mental health medication prescribing increased above trend for hypnotics/anxiolytics by 0.2% (EAG - 2.3%), while antidepressants fell by - 0.2% (EAG 5.0%), with no net change for antipsychotics (EAG 2.8%), but a temporary increase in antipsychotic prescribing in the early pandemic period. For all the main antidepressants prescribed in England (Sertraline, Mirtazapine, Venlafaxine, Fluoxetine and Citalopram), prescribing actually decreased in the main pandemic period vs historical trend. CONCLUSIONS: The increase in anxiolytic/hypnotic prescribing above trend links to pandemic effects on anxiety/worry. If anything, there was a slight fall in prescribing of the main antidepressants prescribed, which given prevailing circumstances at the time, suggests that access to services may have restricted access to timely assessment.

A Longitudinal Clinical Trajectory Analysis Examining the Accumulation of Co-morbidity in People with Type 2 Diabetes (T2D) Compared with Non-T2D Individuals.

BACKGROUND: Type 2 diabetes mellitus (T2D) is commonly associated with an increasing complexity of multimorbidity. While some progress has been made in identifying genetic and non-genetic risk factors for T2D, understanding the longitudinal clinical history of individuals before/after T2D diagnosis may provide additional insights. METHODS: In this study, we utilised longitudinal data from the DARE (Diabetes Alliance for Research in England) study to examine the trajectory of clinical conditions in individuals with and without T2D. Data from 1932 individuals (T2D n = 1196 vs. matched non-T2D controls n = 736) were extracted and subjected to trajectory analysis over a period of up to 50 years (25 years pre-diagnosis/25 years post-diagnosis). We also analysed the cumulative proportion of people with diagnosed coronary artery disease (CAD) in their general practice (GP) record with an analysis of lower respiratory tract infection (RTI) as a comparator group. RESULTS: The mean age of diagnosis of T2D was 52.6 (95% confidence interval 52.0-53.4) years. In the years leading up to T2D diagnosis, individuals who eventually received a T2D diagnosis consistently exhibited a considerable increase in several clinical phenotypes. Additionally, immediately prior to T2D diagnosis, a significantly greater prevalence of hypertension (35%)/RTI (34%)/heart conditions (17%)/eye, nose, throat infection (19%) and asthma (12%) were observed. The corresponding trajectory of each of these conditions was much less dramatic in the matched controls. Post-T2D diagnosis, proportions of T2D individuals exhibiting hypertension/chronic kidney disease/retinopathy/infections climbed rapidly before plateauing. At the last follow-up by quintile of disadvantage, the proportion (%) of people with diagnosed CAD was 6.4% for quintile 1 (least disadvantaged) and 11% for quintile 5 (F = 3.4, p = 0.01 for the difference between quintiles). CONCLUSION: These findings provide novel insights into the onset/natural progression of T2D, suggesting an early phase of inflammation-related disease activity before any clinical diagnosis of T2D is made. Measures that reduce social inequality have the potential in the longer term to reduce the social gradient in health outcomes reported here.

Number of CAG repeats and mortality in middle aged and older men.

DESIGN: The androgen receptor (AR) mediates peripheral effects of testosterone. Previous data suggests an association between the number of CAG repeats in exon-1 of the AR gene and AR transcriptional activity. The aim of this analysis was to determine the association between the number of AR CAG repeats and all-cause mortality in men and the influence of testosterone level on the association. PATIENTS AND MEASUREMENTS: Follow-up data to 27 January 2018 were available for men aged 40-79 years recruited across six countries of the European Male Aging Study between 2003 and 2005. Cox proportional hazards modelling was used to determine the association between CAG repeat number/mortality. Results were expressed as hazard ratios (HR)/95% confidence intervals (CI). RESULTS: One thousand nine hundred and seventy-seven men were followed up. Mean baseline age was 60 ± 11.1 years. Mean duration of follow-up was 12.2 years. At follow up 25.1% of men had died. CAG repeat length ranged from 6 to 39, with the highest proportion of CAG repeat number at 21 repeats (16.4%). In a multivariable model, compared to men with 22-23 AR CAG repeats: for men with <22 and >23 AR CAG HR, 95% CI for mortality were, <22 CAG repeats 1.17 (0.93-1.49) and >23 CAG repeats 1.14 (0.88-1.47). In a post-hoc analysis, the association was significant for men in the lowest tertile of baseline testosterone (<14.2 nmol/L) with >23 CAG repeats: in the adjusted model for <22 and >23 CAG repeats, respectively, 1.49 (0.97-2.27) and 1.68 (1.06-2.67) versus 22-23 repeats. CONCLUSIONS: Our European-wide cohort data overall found no association of androgen receptor CAG repeat number and mortality in men. However, post hoc analysis suggested that an association might be present in men with lower baseline testosterone concentrations, which merits further investigation.

Diabetes foot complications and standardized mortality rate in type 2 diabetes.

AIM: To quantify the impact of foot complications on mortality outcomes in people with type 2 diabetes (T2D), and how routinely measured factors might modulate that risk. MATERIALS AND METHODS: Data for individuals with T2D for 2010-2020, from the Salford Integrated Care Record (Salford, UK), were extracted for laboratory and clinical data, and deaths. Annual expected deaths were taken from Office of National Statistics mortality data. An index of multiple deprivation (IMD) adjusted the standardized mortality ratio (SMR_IMD). Life years lost per death (LYLD) was estimated from the difference between expected and actual deaths. RESULTS: A total of 11 806 T2D patients were included, with 5583 new diagnoses and 3921 deaths during 2010-2020. The number of expected deaths was 2135; after IMD adjustment, there were 2595 expected deaths. Therefore, excess deaths numbered 1326 (SMR_IMD 1.51). No foot complications were evident in n = 9857. This group had an SMR_IMD of 1.13 and 2.74 LYLD. In total, 2979 patients had any foot complication recorded. In this group, the SMD_IMR was 2.29; of these, 2555 (75%) had only one foot complication. Patients with a foot complication showed little difference in percentage HbA1c more than 58 mmol/mol. In multivariate analysis, for those with a foot complication and an albumin-to-creatinine ratio of more than 3 mg/mmol, the odds ratio (OR) for death was 1.93, and for an estimated glomerular filtration rate of less than 60 mL/min/1.73m2 , the OR for death was 1.92. CONCLUSIONS: Patients with T2D but without a foot complication have an SMR_IMD that is only slightly higher than that of the general population. Those diagnosed with a foot complication have a mortality risk that is double that of those without T2D.

Sars-Cov-2 Infection in People with Type 1 Diabetes and Hospital Admission: An Analysis of Risk Factors for England.

INTRODUCTION: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus (coronavirus disease 2019 [COVID-19]) pandemic revealed the vulnerability of specific population groups in relation to susceptibility to acute deterioration in their health, including hospital admission and mortality. There is less data on outcomes for people with type 1 diabetes (T1D) following SARS-CoV-2 infection than for those with type 2 diabetes (T2D). In this study we set out to determine the relative likelihood of hospital admission following SARS-CoV-2 infection in people with T1D when compared to those without T1D. METHODS: This study was conducted as a retrospective cohort study and utilised an all-England dataset. Electronic health record data relating to people in a national England database (NHS England's Secure Data Environment, accessed via the BHF Data Science Centre's CVD-COVID-UK/COVID-IMPACT consortium) were analysed. The cohort consisted of patients with a confirmed SARS-CoV-2 infection, and the exposure was whether or not an individual had T1D prior to infection (77,392 patients with T1D). The patients without T1D were matched for sex, age and approximate date of the positive COVID-19 test, with three SARS-CoV-2-infected people living without diabetes (n = 223,995). Potential factors influencing the relative likelihood of the outcome of hospital admission within 28 days were ascertained using univariable and multivariable logistic regression. RESULTS: Median age of the people living with T1D was 37 (interquartile range 25-52) years, 47.4% were female and 89.6% were of white ethnicity. Mean body mass index was 27 (standard error [SE] 0.022) kg/m2, and mean glycated haemoglobin (HbA1c) was 67.3 (SE 0.069) mmol/mol (8.3%). A significantly higher proportion of people with T1D (10.7%) versus matched non-diabetes individuals (3.9%) were admitted to hospital. In combined analysis including individuals with T1D and matched controls, multiple regression modelling indicated that the factors independently relating to a higher likelihood of hospital admission were: T1D (odds ratio [OR] 1.71, 95% confidence interval [CI] 1.62-1.80]), age (OR 1.02, 95% CI 1.02-1.03), social deprivation (higher Townsend deprivation score: OR 1.07, 95% CI 1.06-1.08), lower estimated glomerular filtration rate (eGFR) value (OR 0.975, 95% CI 0.974-0.976), non-white ethnicity (OR black 1.19, 95% CI 1.06-1.33/OR Asian 1.21, 95% CI 1.05-1.39) and having asthma (OR 1.27, 95% CI 1.19-1.35]), chronic obstructive pulmonary disease (OR 2.10, 95% CI 1.89-2.32), severe mental illness (OR 1.83, 95% CI 1.57-2.12) or hypertension (OR 1.44, 95% CI 1.37-1.52). CONCLUSION: In this all-England study, we describe that, following confirmed infection with SARS-CoV-2, the risk factors for hospital admission for people living with T1D are similar to people without diabetes following confirmed SARS-CoV-2 infection, although the former were more likely to be admitted to hospital. The younger age of individuals with T1D in relation to risk stratification must be taken into account in any ongoing risk reduction strategies regarding COVID-19/future viral pandemics.

Failure to control conventional cardiovascular risk factors in women with type 2 diabetes might explain worse mortality.

INTRODUCTION: The standardised mortality rate (SMR) for people with diabetes in England is 1.5-1.7, with differences in outcomes between sexes. There has been little work examining the factors that could have an impact on this or on what may determine sex differences in outcome. METHODS: Data were extracted for patients with type 2 diabetes (T2D) in Salford (England) in 2010 for the years up to 2020, including any deaths recorded. Expected deaths were calculated from annual Office of National Statistics mortality rate and life expectancy by age and gender, adjusted for the local Index of Multiple Deprivation (IMD). This provided the SMR deprivation (SMRd), and life expectancy years lost per death (LEYLD). The effects of treatment type, and clinical features on SMRd relative to sex were examined by univariable and multivariable analysis. RESULTS: Data from n = 11,806 (F = 5184; M = 6622) patients were included. Of these, n = 5540 were newly diagnosed and n = 3921 died (F = 1841; M = 2080). In total, n = 78,930 patient years. The expected deaths numbered n = 2596 (adjusted for age, sex, and IMD). Excess deaths were n = 1325 (F = 689; M = 636). Life expectancy years lost (LEYL) 18,989 (F = 9714; M = 9275). SMRd 1.51 (F = 1.60; M = 1.44) and LEYLD 4.84 years (F = 5.28; M = 4.46). The impact of risk factors was not different by sex. However, women had higher prevalence of % diagnosed >65 years of age; % last eGFR <60 mLs/min/1.73 m2 , and lower prevalence of % prescribed ACE-inhibitor/ARB, DPP4-inhibitor and SGLT2-inhibitor. Applying the male prevalence rate to the female population and expected mortality suggested n = 437 (55%) of excess T2D female deaths were attributed to sex difference in the prevalence of these risk and protective factors. CONCLUSIONS: Outcomes in women with T2DM are worse than in men, contributed to by greater prevalence of adverse factors and less prescribing of cardioprotective medication.