Urine Albumin-Creatinine Ratio Variability in People With Type 2 Diabetes: Clinical and Research Implications.

RATIONALE & OBJECTIVE: Evidence has demonstrated that albuminuria is a key diagnostic and prognostic marker of diabetic chronic kidney disease, but the impact of its day-to-day variability has not been adequately considered. This study quantified within-individual variability of albuminuria in people with type 2 diabetes to inform clinical albuminuria monitoring. STUDY DESIGN: Descriptive cross-sectional analysis. SETTING & PARTICIPANTS: People with type 2 diabetes (n=826, 67.1 [IQR, 60.3-72.4] years, 64.9% male) participating in the Progression of Diabetic Complications (PREDICT) cohort study. EXPOSURE: Four spot urine collections for measurement of urinary albumin-creatinine ratio (UACR) within 4 weeks. OUTCOME: Variability of UACR. ANALYTICAL APPROACH: We characterized within-individual variability (coefficient of variation [CV], 95% limits of random variation, intraclass correlation coefficient), developed a calculator displaying probabilities that any observed difference between a pair of UACR values truly exceeded a 30% difference, and estimated the ranges of diagnostic uncertainty to inform a need for additional UACR collections to exclude or confirm albuminuria. Multiple linear regression examined factors influencing UACR variability. RESULTS: We observed high within-individual variability (CV 48.8%; 95% limits of random variation showed a repeated UACR to be as high/low as 3.78/0.26 times the first). If a single-collection UACR increased from 2 to 5mg/mmol, the probability that UACR actually increased by at least 30% was only 50%, rising to 97% when 2 collections were obtained at each time point. The ranges of diagnostic uncertainty were 2.0-4.0mg/mmol after an initial UACR test, narrowing to 2.4-3.2 and 2.7-2.9mg/mmol for the mean of 2 and 3 collections, respectively. Some factors correlated with higher (female sex; moderately increased albuminuria) or lower (reduced estimated glomerular filtration rate and sodium-glucose cotransporter 2 inhibitor/angiotensin-converting enzyme inhibitor/angiotensin receptor blocker treatment) within-individual UACR variability. LIMITATIONS: Reliance on the mean of 4 UACR collections as the reference standard for albuminuria. CONCLUSIONS: UACR demonstrates a high degree of within-individual variability among individuals with type 2 diabetes. Multiple urine collections for UACR may improve capacity to monitor changes over time in clinical and research settings but may not be necessary for the diagnosis of albuminuria. PLAIN-LANGUAGE SUMMARY: Albuminuria (albumin in urine) is a diagnostic and prognostic marker of diabetic chronic kidney disease. However, albuminuria can vary within an individual from day to day. We compared 4 random spot urinary albumin-creatinine ratio (UACR) samples from 826 participants. We found that a second UACR collection may be as small as a fourth or as large as almost 4 times the first sample's UACR level. This high degree of variability presents a challenge to our ability to interpret changes in albuminuria. Multiple collections have been suggested as a solution. We have constructed tools that may aid clinicians in deciding how many urine collections are required to monitor and diagnose albuminuria. Multiple urine collections may be required for individual monitoring but not necessarily for diagnosis.

Special Issue: "New Trends in Diabetes, Hypertension, and Cardiovascular Diseases".

Cardiovascular disease (CVD) encompasses a range of disorders affecting the heart and blood vessels, including coronary heart disease and cerebrovascular disease [...].

Imputation of plasma lipid species to facilitate integration of lipidomic datasets.

Recent advancements in plasma lipidomic profiling methodology have significantly increased specificity and accuracy of lipid measurements. This evolution, driven by improved chromatographic and mass spectrometric resolution of newer platforms, has made it challenging to align datasets created at different times, or on different platforms. Here we present a framework for harmonising such plasma lipidomic datasets with different levels of granularity in their lipid measurements. Our method utilises elastic-net prediction models, constructed from high-resolution lipidomics reference datasets, to predict unmeasured lipid species in lower-resolution studies. The approach involves (1) constructing composite lipid measures in the reference dataset that map to less resolved lipids in the target dataset, (2) addressing discrepancies between aligned lipid species, (3) generating prediction models, (4) assessing their transferability into the targe dataset, and (5) evaluating their prediction accuracy. To demonstrate our approach, we used the AusDiab population-based cohort (747 lipid species) as the reference to impute unmeasured lipid species into the LIPID study (342 lipid species). Furthermore, we compared measured and imputed lipids in terms of parameter estimation and predictive performance, and validated imputations in an independent study. Our method for harmonising plasma lipidomic datasets will facilitate model validation and data integration efforts.

SGLT-2 Inhibitor Use and Cause-Specific Hospitalization Rates: An Outcome-Wide Study to Identify Novel Associations of SGLT-2 Inhibitors.

Sodium-glucose co-transporter 2 inhibitors (SGLT2is) have demonstrated multifaceted pharmacological effects. In addition to type 2 diabetes, they are now indicated for heart failure and chronic kidney disease. This study aimed to identify novel associations between SGLT2i use and health outcomes using real-world data. Using linked data from a nationwide diabetes registry in Australia, we compared hospitalization rates in people living with type 2 diabetes commencing treatment with SGLT2i and dipeptidyl peptidase-4 inhibitor (DPP4i) between December 1, 2013, and June 30, 2019. Cause-specific hospitalizations were categorized across three hierarchies of diagnoses (first, first three, and first four digits of International Classification of Diseases, Tenth Version, Australian Modification codes). Incidence rate ratio (IRR) and 95% confidence interval (95% CI) for each cause-specific hospitalization were estimated using negative binomial regression. In the first hierarchy, hospitalization rates were lower across most diagnosis groups among SGLT2i initiators (n = 99,569) compared with DPP4i initiators (n = 186,353). In the second and third hierarchies, there were lower hospitalization rates relating to infections, anemias, and obstructive airway diseases among SGLT2i initiators compared with DPP4i initiators. These included sepsis (IRR: 0.60, 95% CI: 0.51-0.72) anemia (IRR: 0.55, 95% CI: 0.46-0.66), and chronic obstructive pulmonary diseases (IRR: 0.52, 95% CI: 0.40-0.68), as well as for previously known associations (e.g., heart failure (IRR: 0.63, 95% CI: 0.56-0.70)). SGLT2is have previously uncharacterized associations on a range of important clinical outcomes; validation of these associations requires further study, some of which may suggest novel benefits or new indications for SGLT2is.

Liver disease mortality and hospitalisations among people with type 2 diabetes mellitus: A population-based study.

BACKGROUND AND AIMS: The burden of liver disease among people with diabetes at a population level is unknown. We explored the burden and trends of liver disease mortality and hospitalisations among Australians with diabetes. METHODS: We linked Australians with type 2 diabetes on the National Diabetes Services Scheme to the National Death Index for 2002-2019 to determine trends in the proportion of deaths due to liver disease, overall and by subcategory. We also determined the leading reasons and risk factors for liver disease hospitalisations in those with diabetes over this period. Finally, we compared the burden of liver disease hospitalisations among those with diabetes to the general population using excess hospitalisations per 100 000 person-years. RESULTS: Among Australians with type 2 diabetes (n = 1 122 431) liver diseases accounted for between 1.5% and 1.9% of deaths between 2002 and 2019, roughly one-third of the proportion of deaths caused by kidney disease. The proportion of deaths due to inflammatory liver diseases among those with diabetes increased from .08% in 2002 to .27% in 2019. Alcohol-related liver disease accounted for the greatest share (22.7%) of liver disease hospitalisation in those with diabetes, but the number of hospitalisations for this condition declined over time. Compared to the general population, men (RR 3.63, 95% CI 3.44-3.84) and women (RR 4.49, 4.21-4.78) with diabetes were at higher risk of hospitalisation for fibrosis and cirrhosis; however, this did not translate to a substantial excess risk per 100 000 population. CONCLUSIONS: Better screening methods for liver disease among people with diabetes should be developed and implemented into practice.

Causes of death among Australians with type 1 or type 2 diabetes, 2002-2019.

AIMS: This population-based study sought to explore in detail the conditions driving the diversification in causes of death among people with diabetes. METHODS: We linked Australians with type 1 or type 2 diabetes of all ages on the National Diabetes Services Scheme to the National Death Index for 2002-2019. We investigated the proportional contributions of different causes of death to total deaths over time across eight categories of causes of death, stratified by sex and diabetes type. The underlying causes of death were classified according to the International Classification of Diseases, Tenth Revision codes. RESULTS: Between 2002 and 2019, there was a shift in the causes of death among Australians with diabetes away from cardiovascular disease. The proportion of deaths attributed to cardiovascular disease declined in both sexes (ptrend <0.001), most substantially among women with type 2 diabetes from 48.2% in 2002 to 30.7% in 2019. Among men with type 2 diabetes, cancer replaced cardiovascular disease as the leading cause of death. The proportion of deaths due to dementia increased overall, from 2% in 2002 to over 7% in 2019, and across all age groups, notably from 1% to 4% in those aged 70-79. The proportion of deaths due to falls and Parkinson's disease also increased. CONCLUSIONS: There has been a shift of causes of death among those with diabetes away from cardiovascular disease. The proportion of deaths due to conditions such as dementia and falls is increasing among those with diabetes, which will require consideration when planning future resource allocation.

Reasons for Hospitalization Among Australians With Type 1 or Type 2 Diabetes and COVID-19.

OBJECTIVE: Our aim in this study was to determine the reasons for hospitalization in Australian people with diabetes who contract COVID-19. METHODS: All COVID-19 cases reported to the Victorian Department of Health and linked hospitalization data were assessed. We determined reasons for acute (0 to 30 days) and postacute (31 to 365 days) hospitalization among those with type 1 or type 2 diabetes and COVID-19, compared to those with COVID-19 and no diabetes, and to admissions before the COVID-19 pandemic. RESULTS: A total of 13,302 Australians with type 1 or type 2 diabetes were hospitalized in the state of Victoria in the 12 months after COVID-19 diagnosis. Respiratory diseases accounted for 40% of acute admissions among those with diabetes. Viral pneumonia was the leading cause of acute hospitalization among those with diabetes and constituted a larger proportion of admissions in those with compared to those without diabetes (adjusted prevalence ratio 1.87, 95% confidence interval 1.76 to 1.99). The distribution of postacute hospitalizations among those with diabetes aligned with that of people with diabetes before the COVID-19 pandemic. CONCLUSIONS: Respiratory diseases are the leading cause of acute hospitalization in those with type 1 or type 2 diabetes and COVID-19. The reasons for postacute hospitalization resemble those in people with diabetes and no COVID-19. We reinforce the importance of community management of people with diabetes in the ongoing pandemic.

Reasons for hospitalisation in youth with type 1 diabetes, 2010-2019.

AIMS: To determine the incidence of hospitalisation for all diagnoses among Australian youth with type 1 diabetes. METHODS: We linked Australians aged under 20 years with type 1 diabetes on the National Diabetes Services Scheme (n = 45,685) to hospital admission data from 2010 to 2019. We determined relative risks (RR) of hospitalisation among those with type 1 diabetes in the states of Victoria and Queensland (n = 21,898) compared to the general population for 2010-2017 using Poisson regression. RESULTS: Australian youth with type 1 diabetes had increased risk for almost all reasons for hospitalisation compared to the general population, especially infections such as anogenital herpesviral infections (RR 54.83, 95% CI 33.21-90.53), and mental health disorders including personality disorders (RR 9.70, 95% CI 8.02-11.72). Among those with type 1 diabetes, over 60% of hospitalisations were directly related to diabetes, almost half of which were for ketoacidosis. Approximately 15% of ketoacidosis admissions occurred within 3 months of diabetes diagnosis. One quarter of those with admissions for ketoacidosis were readmitted for ketoacidosis within 12 months. Residence in areas of high socio-economic disadvantage was an independent risk factor for admission and readmission for ketoacidosis. CONCLUSIONS: Youth with type 1 diabetes are susceptible to a wide range of complications. Clinicians should consider screening and prevention for conditions such as infections and mental health disorders. Targeted support and education around glycaemic management should be considered in those at high risk for ketoacidosis admission including those living in areas of high socio-economic disadvantage.

Hospital admissions among people with diabetes: A systematic review.

OBJECTIVE: To describe the reasons for hospital admission among people with diabetes. METHODS: We searched Emcare, Embase, Medline and Google Scholar databases for population-based studies describing the causes of hospitalisation among people with diabetes. We included articles published in English from 1980 to 2022. For each study, we determined the most frequent reasons for admission. Studies were assessed for quality using the Newcastle Ottawa quality assessment tool. RESULTS: 6920 research articles were retrieved from the search of all sources. After screening the titles and abstracts of these, we reviewed the full text of 135 papers and finally included data from 42 studies. Admissions among the total diabetes were reported in 25 papers: 5 articles reported type 1 diabetes alone, 10 articles reported type 2 diabetes alone and the remaining 2 articles reported type 1 and type 2 diabetes separately. Among the 25 total and type 2 diabetes studies that reported the distribution of hospitalisations in broad categories, cardiovascular diseases (CVD) were the leading cause of admission in 19/25 (76%) of studies. Among the 19 studies that reported CVD admissions by subcategories, ischaemic or coronary heart disease was the leading subtype of CVD in 58% of studies. The other common causes of admissions were infections, renal disorders, endocrine, nutritional, metabolic and immunity disorders. In people with type 1 diabetes, acute diabetes complications were the leading cause of admission. CONCLUSION: CVD are the leading cause of hospital admission for people with diabetes, with ischaemic or coronary heart disease as the predominant subtype.

COX2 inhibitor use and type 2 diabetes treatment intensification: A registry-based cohort study.

AIM: This study examined the association between cyclooxygenase-2 inhibitor (COX2i) use and diabetes progression in people with type 2 diabetes. METHODS: We conducted a nation-wide cohort study using an Australian diabetes registry linked to medication dispensing data. We assessed time to diabetes treatment intensification among new users of COX2i compared to mild opioids. Inverse probability of treatment-weighted Cox regression models were used to adjust for age, sex, time since diabetes diagnosis, comorbidities, and socio-economic disadvantage. We conducted several sensitivity analyses, including per-protocol analyses and comparing use of any NSAID to mild opioids. RESULTS: There were 8,071 new users of COX2i and 7,623 of mild opioids with 4,168 diabetes treatment intensifications over a median follow-up of 1.6 years. Use of COX2i was associated with decreased risk of treatment intensification when compared to mild opioids (HR 0.91, 95 %CI 0.85-0.96). The results were not significant in the per-protocol analyses. Use of any NSAID was associated with a lower risk of treatment intensification compared to mild opioids (HR 0.90, 95 %CI 0.85-0.96). CONCLUSIONS: Treatment with COX2i may be associated with a modest decreased risk of diabetes treatment intensification compared to mild opioids. Future clinical studies are required to confirm whether COX2 inhibition has clinically significant benefits for glycaemic control.

Effect of Hydralazine on Angiotensin II-Induced Abdominal Aortic Aneurysm in Apolipoprotein E-Deficient Mice.

The rupture of an abdominal aortic aneurysm (AAA) causes about 200,000 deaths worldwide each year. However, there are currently no effective drug therapies to prevent AAA formation or, when present, to decrease progression and rupture, highlighting an urgent need for more research in this field. Increased vascular inflammation and enhanced apoptosis of vascular smooth muscle cells (VSMCs) are implicated in AAA formation. Here, we investigated whether hydralazine, which has anti-inflammatory and anti-apoptotic properties, inhibited AAA formation and pathological hallmarks. In cultured VSMCs, hydralazine (100 µM) inhibited the increase in inflammatory gene expression and apoptosis induced by acrolein and hydrogen peroxide, two oxidants that may play a role in AAA pathogenesis. The anti-apoptotic effect of hydralazine was associated with a decrease in caspase 8 gene expression. In a mouse model of AAA induced by subcutaneous angiotensin II infusion (1 µg/kg body weight/min) for 28 days in apolipoprotein E-deficient mice, hydralazine treatment (24 mg/kg/day) significantly decreased AAA incidence from 80% to 20% and suprarenal aortic diameter by 32% from 2.26 mm to 1.53 mm. Hydralazine treatment also significantly increased the survival rate from 60% to 100%. In conclusion, hydralazine inhibited AAA formation and rupture in a mouse model, which was associated with its anti-inflammatory and anti-apoptotic properties.

Left ventricular hypertrophy: an ECG-based study of prevalence and risk factors in a multiethnic population.

BACKGROUND: Left ventricular hypertrophy (LVH) is frequently seen in association with arterial hypertension and indicates poor prognosis. This study aimed to determine the prevalence of LVH and associated factors in a multiethnic population from Mauritius. METHODS: Population-based health surveys were performed in 2009 and 2015 and included in total 8961 individuals aged 35-75 years with recorded 12-lead ECG. LVH was defined according to three criteria: Sokolow-Lyon, Cornell voltage and Cornell product. Data were collected about health and lifestyle behaviour. Anthropometry and blood pressure were measured. Fasting levels of blood lipids and glucose were determined, oral glucose tolerance test was performed in people without glucose-lowering medications. RESULTS: The age-standardised prevalence of LVH was 9% (n=875) according to any of the three ECG criteria. Individuals with LVH were older, more likely to have hypertension, diabetes, known cardiovascular disease (CVD) and elevated levels of cholesterol and creatinine. Further, they were more likely to be of African descent (Creole) and have lower educational level. In a multivariable model, Creole (OR (95% CI)) (1.56 (1.33 to 1.83)), low educational level (1.49 (1.28 to 1.75)), hypertension (3.01 (2.55 to 3.56)), known CVD (1.42 (1.11 to 1.83)) and elevated creatinine (1.08 (1.03 to 1.14)) remained associated with LVH. Individuals with non-treated or uncontrolled hypertension had a higher risk for LVH (3.09 (95% CI 2.57 to 3.71) and 4.07 (95% CI 3.29 to 5.05), respectively), than individuals with well controlled hypertension or normotension. CONCLUSION: LVH occurs more frequently in individuals with hypertension, as well as in individuals with African ancestry and/or low education level.

Metabolic phenotyping of BMI to characterize cardiometabolic risk: evidence from large population-based cohorts.

Obesity is a risk factor for type 2 diabetes and cardiovascular disease. However, a substantial proportion of patients with these conditions have a seemingly normal body mass index (BMI). Conversely, not all obese individuals present with metabolic disorders giving rise to the concept of "metabolically healthy obese". We use lipidomic-based models for BMI to calculate a metabolic BMI score (mBMI) as a measure of metabolic dysregulation associated with obesity. Using the difference between mBMI and BMI (mBMIΔ), we identify individuals with a similar BMI but differing in their metabolic health and disease risk profiles. Exercise and diet associate with mBMIΔ suggesting the ability to modify mBMI with lifestyle intervention. Our findings show that, the mBMI score captures information on metabolic dysregulation that is independent of the measured BMI and so provides an opportunity to assess metabolic health to identify "at risk" individuals for targeted intervention and monitoring.

Current insights and emerging trends in early-onset type 2 diabetes.

Type 2 diabetes diagnosed in childhood or early adulthood is termed early-onset type 2 diabetes. Cases of early-onset type 2 diabetes are increasing rapidly globally, alongside rising obesity. Compared with a diagnosis later in life, an earlier-onset diagnosis carries an unexplained excess risk of microvascular complications, adverse cardiovascular outcomes, and earlier death. Women with early-onset type 2 diabetes also have a higher risk of adverse pregnancy outcomes. The high burden of complications renders individuals with early-onset type 2 diabetes at future risk of multimorbidity and interventions to reverse these concerning trends should be a priority. Within the early-onset cohort, disease pathophysiology and interventions have been better studied in paediatric-onset (<19 years) type 2 diabetes compared to adults; however, young adults aged 19-39 years (a larger number proportionally) are not well characterised and are also invisible in the current evidence base supporting management, which is derived from trials in later-onset type 2 diabetes. Young adults with type 2 diabetes face challenges in self-management that older individuals are less likely to experience (being in education or of working age, higher diabetes distress, and possible obesity-related stigma and diabetes-related stigma). There is a major research gap as to the optimal strategies to deploy in managing type 2 diabetes in adolescents and young adults, given that current models of care appear to not work as well in this age group. In the face of manifold risk factors (obesity, female sex, social deprivation, non-White European ethnicity, and genetic risk factors) prevention strategies with tailored lifestyle interventions, where needed, are likely to have greater success, but more evidence is needed. In this Review, we draw on evidence from both adolescents and young adults to provide a contemporary update on the current insights and emerging trends in early-onset type 2 diabetes.

The T1D Index: Implications of Initial Results, Data Limitations, and Future Development.

PURPOSE OF THE REVIEW: Current global information on incidence, prevalence, and mortality of type 1 diabetes (T1D) is limited, particularly in low- and middle-income countries. To address this gap in evidence, JDRF, Life for a Child, International Society for Pediatric and Adolescent Diabetes, and International Diabetes Federation have developed the T1D Index, which uses a Markov mathematical model, and machine learning and all available data to provide global estimates of the burden on T1D. This review assesses the methodology, limitations, current findings, and future directions of the Index. RECENT FINDINGS: Global prevalence was estimated at 8.4 million in 2021, with 1.5 million <20 years (y). T1D prevalence varied from 1.5 to 534 per 100,000, with T1D accounting for <0.1-17.8% of all diabetes in different countries. A total of 35,000 young people <25 y are estimated to have died at clinical onset of T1D from non-diagnosis. An estimated 435,000 people <25 y were receiving "minimal care." Health-adjusted life years (HALYs) lost for individuals diagnosed with T1D at age 10 y in 2021 ranged from 14 to 55 y. These results show that interventions to reduce deaths from non-diagnosis, and improve access to at least an intermediate care level, are needed to reduce projected life years lost. The results have significant uncertainties due to incomplete data across the required inputs. Obtaining recent incidence, prevalence, and mortality data, as well as addressing data quality issues, misdiagnoses, and the lack of adult data, is essential for maintaining and improving accuracy. The index will be updated regularly as new data become available.

Non-alcoholic fatty liver disease prevalence in Australia has risen over 15 years in conjunction with increased prevalence of obesity and reduction in healthy lifestyle.

BACKGROUND AND AIM: Non-alcoholic fatty liver disease (NAFLD) is the most prevalent liver condition globally. The aim of this study was to evaluate the change in age- and sex-standardized prevalence of NAFLD in regional Victoria over a 15-year period and explore the underlying factors associated with differences over time. METHODS: Repeated comparative cross-sectional studies in four towns in regional Victoria, Australia. Individuals randomly selected from households from residential address lists from local government organizations in 2001-2003 (CrossRoads I [CR1]) and 2016-2018 (CrossRoads II [CR2]) with 1040 (99%) and 704 (94%) participants from CR1 and CR2 having complete data for analysis. Primary outcome was change in prevalence estimates of NAFLD (defined by a fatty liver index ≥ 60 in the absence of excess alcohol and viral hepatitis) between 2003 and 2018. RESULTS: Crude prevalence of NAFLD increased from 32.7% to 38.8% (P < 0.01), while age-standardized/sex-standardized prevalence increased from 32.4% to 35.4% (P < 0.01). Concurrently, prevalence of obesity defined by BMI and elevated waist circumference increased 28% and 25%, respectively. Women had a greater increase in the prevalence of NAFLD than men, in parallel with increasing prevalence of obesity. Proportion of participants consuming takeaway food greater than once weekly increased significantly over time. Up to 60% of NAFLD patients require additional tests for assessment of significant fibrosis. CONCLUSIONS: Crude and age-standardized/sex-standardized prevalence of NAFLD have both increased significantly over the last 15 years, particularly among women, in association with a parallel rise in the prevalence of obesity.

Algorithms to define diabetes type using data from administrative databases: A systematic review of the evidence.

AIMS: To find the best-performing algorithms to distinguish type 1 and type 2 diabetes in administrative data. METHODS: Embase and MEDLINE databases were searched from January 2000 until January 2023. Papers evaluating the performance of algorithms to define type 1 and type 2 diabetes by reporting diagnostic metrics against a range of reference standards were selected. Study quality was evaluated using the Quality Assessment of Diagnostic Accuracy Studies. RESULTS: Of the 24 studies meeting the eligibility criteria, 19 demonstrated a low risk of bias and low concerns about the applicability of the study population across all domains. Algorithms considering multiple diabetes diagnostic codes alone were sensitive and specific approaches to classify diabetes type (both metrics >92.1% for type 1 diabetes; >86.9% for type 2 diabetes). Among the top 10-performing algorithms to detect type 1 and type 2 diabetes, 70% and 100% featured multiple criteria, respectively. Information on insulin use was more sensitive and specific for detecting diabetes type than were criteria based on use of oral hypoglycaemic agents. CONCLUSIONS: Algorithms based on multiple diabetes diagnostic codes and insulin use are the most accurate approaches to distinguish type 1 from type 2 diabetes using administrative data. Approaches with more than one criterion may also increase sensitivity in distinguishing diabetes type.