Multimorbidity, mortality, and HbA1c in type 2 diabetes: A cohort study with UK and Taiwanese cohorts

Age-specific Multimorbidity Patterns and Burden on All-Cause Mortality and Public Direct Medical Expenditure: A Retrospective Cohort Study

OBJECTIVE

To evaluate age-specific multimorbidity patterns and morbidity burden on mortality and healthcare expenditure across age groups.

PATIENTS AND METHODS

Retrospective observational study between January 1, 2009 to December 31, 2017 using electronic health records in Hong Kong: Individuals were stratified by age (< 50, 50-64, 65-79, ≥ 80), and sub-classified by number of morbidities (0, 1, 2, 3, ≥ 4) out of 21 common chronic conditions. Clustering analyses were conducted to identify specific patterns of multimorbidity. Association between the number as well as combinations of morbidities and all-cause mortality and public expenditure was examined.

RESULTS

4,562,832 individuals with a median follow-up of 7 years were included. Mental disorders were the top morbidities among young individuals, while cardiovascular diseases were prevalent in the elderly. An increased number of morbidities was associated with a greater relative risk for mortality and medical expenditure, and this relationship was stronger among younger patients. Compared to individuals in the same age group without morbidity, the hazard ratios (HR; 95% CI) of all-cause mortality in patients aged < 50 and ≥ 80 with two comorbidities 3.81 (3.60-4.03) and 1.38 (1.36-1.40), respectively, which increased to 14.22 (9.87-20.47) and 2.20 (2.13-2.26), respectively, as the number of morbidities increased to ≥ 4. The stroke-hypertension cluster was shown to be associated with the highest HR of mortality 2.48 (2.43-2.53) among all identified clusters arising from the clustering analysis.

CONCLUSION

Given the stronger association between multimorbidity and all-cause mortality and greater opportunity costs in younger populations, prevention and management of early-onset multimorbidity are warranted. (248 words).

How valid is a prescription-based multimorbidity index (Rx-risk) in predicting mortality in the Outcomes and Multimorbidity In Type 2 diabetes (OMIT) study? A nation-wide registry-based cohort study from Norway

OBJECTIVE

The prescription-based Rx-risk index has previously been developed to measure multimorbidity. We aimed to adapt and evaluate the validity of the Rx-risk index in prediction of mortality among persons with type 2 diabetes.

DESIGN

Registry-based study.

SETTING

Adults with type 2 diabetes in Norway identified within the 'Outcomes and Multimorbidity In Type 2 diabetes' cohort, with linkage to prescriptions from the Norwegian Prescription Database and mortality from the Population Registry.

PARTICIPANTS

We defined a calibration sample of 42 290 adults diagnosed with type 2 diabetes 1950-2013, and a temporal validation sample of 7085 adults diagnosed 2014-2016 to evaluate the index validity over time PRIMARY OUTCOME MEASURE: All-cause mortality METHODS: For the calibration sample, dispensed drug prescriptions in 2013 were used to define 44 morbidity categories. Weights were estimated using regression coefficients from a Cox regression model with 5 year mortality as the outcome and all morbidity categories, age and sex included as covariates. The Rx-risk index was computed as a weighted sum of morbidities. The validity of the index was evaluated using C-statistic and calibration plots.

RESULTS

In the calibration sample, mean (SD) age at start of follow-up and duration of diabetes was 63.8 (12.4) and 10.1 (7.0) years, respectively. The overall C-statistic was 0.82 and varied from 0.74 to 0.85 when stratifying on age groups, sex, level of education and country of origin. In the validation sample, mean (SD) age and duration of diabetes was 59.7 (13.0) and 2.0 (0.8) years, respectively. Despite younger age, shorter duration of diabetes and later time period, the C-index was high both in the total sample (0.84) and separately for men (0.83) and women (0.84).

CONCLUSIONS

The Rx-risk index showed good discrimination and calibration in predicting mortality and thus presents a valid tool to assess multimorbidity among persons with type 2 diabetes.

Effects of COVID-19 on diabetes care among dutch diabetes outpatients

AIMS

The COVID-19 pandemic impacted diabetes care by reducing diabetes outpatient visits and diabetes-related screening due to allocation of healthcare resources. Yet the impact of COVID-19 on diabetes outpatients has not been extensively evaluated. This study aimed to assess the effect of the COVID-19 pandemic on diagnostics and intermediate outcomes of outpatient diabetes care pre- and during COVID.

METHODS

This observational cohort study included 8,442 diabetes patients in the Dutch Pediatric and Adult Registry of Diabetes (DPARD) visiting diabetes outpatient clinics in 2019 and 2021. A mixed-effects regression analysis was used to examine differences in target achievement of HbA1c, BMI, blood pressure, LDL-cholesterol, eGFR, and the difference in mean HbA1c between 2019 and 2020 among n = 1,426 outpatients who visited in both years. Analyses were adjusted for age, sex, and BMI.

RESULTS

A 22.7% (21.6-23.8%, p < 0.001) decline in outpatient volume was observed during the pandemic (2020). BMI, lipid spectrum, kidney function, and HbA1c were assessed less frequently in 2020 than in 2019. In 2020, compared to 2019, the median HbA1c level increased by 2.2% (1.0 mmol/mol, p = 0.035) and the percentages of patients with known HbA1C meeting targets below 10, 8, 7% (86, 64, and 53 mmol/mol) decreased by 0.5%, 1.7% and 1.4%, respectively. Target blood pressure ≤ 130/80 mmHg was achieved more often in 2020 (15.0% versus 18.3%, p = 0.018), while HbA1c ≤ 86 mmol/mol was achieved less (89.3% versus 87.1%, p = 0.001), among diabetes outpatients seen in both 2019 and 2020. In patients visiting both years, HbA1c was 2.3% (1.9 mmol/l, 95% CI 1.2-2.5, p < 0.001) lower during the pandemic than in the prepandemic (2019).

CONCLUSIONS

The COVID pandemic was associated with a marked reduction in patient volume in diabetes outpatient care among five hospitals. Among patients who received outpatient care both before and during the pandemic period, HbA1c control and blood pressure control enhanced during the pandemic. Re-evaluation of current diabetes outpatient care organization is warranted to ensure optimal diabetes care in future times.

Random Blood Glucose, but Not HbA1c, Was Associated with Mortality in COVID-19 Patients with Type 2 Diabetes Mellitus—A Retrospective Study

Previous studies have yielded inconsistent results on whether glycated hemoglobin (HbA1c) and random blood glucose (RBG) are associated with mortality of coronavirus disease 2019 (COVID-19) patients with type 2 diabetes mellitus (T2DM). This study aimed to assess the association of HbA1c and RBG with mortality among COVID-19 patients with T2DM. A retrospective study was conducted on 237 patients with COVID-19 and T2DM (survival (n = 169) and non-survival groups (n = 68)). Data on socio-demography, comorbidities, clinical symptoms, laboratory examination, and mortality were collected. Patients in the non-survival group had an older age range as compared with those in the survival group (60 (52.3–65.0) vs. 56.0 (48.5–61.5) years, p = 0.009). There was no statistical gender difference between the two groups. After matching was done, chronic kidney disease, NLR, d-dimer, procalcitonin, and random blood glucose were higher in the non-survival group compared to the survival group (p < 0.05). HbA1c levels were similar in survivors and non-survivors (8.7% vs. 8.9%, p=0.549). The level of RBG was independently associated with mortality of COVID-19 patients with T2DM (p = 0.003, adjusted OR per 1-SD increment 2.55, 95% CI: 1.36–4.76). In conclusion, RBG was associated with the mortality of COVID-19 patients with T2DM, but HbA1c was not.

Value of GLP-1 receptor agonists versus long-acting insulins for type 2 diabetes patients with and without established cardiovascular or chronic kidney diseases: A model-based cost-effectiveness analysis using real-world data

AIMS

To evaluate the cost-effectiveness of glucagon-like peptide-1 receptor agonists (GLP-1RAs) versus long-acting insulins (LAIs) in patients with type 2 diabetes (T2D) using real-world data.

METHODS

A Markov model was utilized to estimate healthcare costs (US$) and quality-adjusted life-years (QALYs) of receiving treatments over 10 years from the healthcare sector perspective. Model inputs were derived from the analyses of Taiwan's National Health Insurance Research Database or published literature on Taiwanese T2D populations. Base-case analysis was performed for the overall study cohort and subgroup analyses were stratified by the presence or absence of established cardiovascular diseases (CVDs) or chronic kidney diseases (CKDs).

RESULTS

Overall, using GLP-1RAs versus LAIs cost $6,053 per QALY gained. Results were robust across sensitivity and scenario analyses. Among patients with established CVDs and CKDs, GLP-1RA versus LAI therapy saved $673 (cost-saving) and cost $1,675 per QALY gained, respectively. Among patients without established CVDs and CKDs, GLP-1RA versus LAI therapy cost $9,093 and $7,659 per QALY gained, respectively.

CONCLUSIONS

Using GLP-1RAs versus LAIs for T2D patients represented good economic value in real-world practice. Pronounced economic benefits of GLP-1RA therapy among those with prior CVDs or CKDs support rational treatment decisions and optimal healthcare resource allocation for these patients.

Lower education and immigrant background are associated with lower participation in a diabetes education program - Insights from adult patients in the Outcomes & Multi-morbidity In Type 2 diabetes cohort (OMIT)

OBJECTIVES

Diabetes educational programmes should be offered to patients with type 2 diabetes mellitus (T2DM). We assessed the proportion of diabetes educational program participation among adults with T2DM, and its associations with place of residence in Norway, education, and immigrant background.

METHODS

We identified 28,128 diagnosed with T2DM (2008-2019) in the Outcomes & Multi-morbidity In Type 2 diabetes cohort. To examine associations between sociodemographic factors and participation in diabetes start courses (yes/no), we computed adjusted risk ratios (95% CI) using log-binomial regression.

RESULTS

Overall, 18% participated on the diabetes start course, but partaking differed by Norwegian counties (range:12-34%). Individuals with an immigrant background were 29% less likely to participate (RR 0.71, CI 0.65-0.79). Similarly, those with a lower educational level were 23% less likely to participate (RR 0.77, CI 0.72-0.83) than those with the highest education. The association between education and start course participation was not significant in the subgroup of immigrant individuals (RR 0.88 CI 0.70-1.12).

CONCLUSIONS

We found that diabetes start course participation was overall low, especially in individuals with low education and immigrant background.

PRACTICE IMPLICATIONS

More efforts are needed to promote diabetes start courses in patients with T2DM.

Age-specific associations between the number of co-morbidities, all-cause mortality and public direct medical costs in patients with type 2 diabetes: A retrospective cohort study

AIM

To evaluate the association between the number of co-morbidities, all-cause mortality and public health system expenditure in patients with type 2 diabetes (T2D) across different age groups.

MATERIALS AND METHODS

A retrospective observational study of T2D patients using electronic health records in Hong Kong was conducted. Patients were stratified by age (< 50, 50-64, 65-79, ≥ 80 years) and the number of co-morbidities (0, 1, 2, 3, ≥ 4), defined using the Charlson Comorbidity Index and prevalent chronic diseases identified in local surveys. The association between the number of co-morbidities, all-cause mortality and direct medical costs was examined using Cox proportional hazard regression and the gamma generalized linear model with log link function.

RESULTS

A total of 262 212 T2D patients with a median follow-up of 10 years were included. Hypertension and dyslipidaemia were the most common co-morbidities in all age groups. After age stratification, cardiovascular diseases dominated the top pair of co-morbidities in the older age groups (65-79 and ≥ 80 years), while inflammatory and liver disease were predominant among younger individuals. Compared with co-morbidity-free T2D patients, the hazard ratios (95% CI) of death for patients aged younger than 50 and 80 years or older with two co-morbidities were 1.31 (1.08-1.59) and 1.25 (1.15-1.36), respectively, and increased to 3.08 (2.25-4.21) and 1.98 (1.82-2.16), respectively, as the number of co-morbidities increased to four or more. Similar trends were observed for medical costs.

CONCLUSIONS

Age-specific co-morbidity patterns were observed for patients with T2D. A greater number of co-morbidities was associated with increased mortality and healthcare costs, with stronger relationships observed among younger patients.

Multimorbidity, Frailty and Diabetes in Older People-Identifying Interrelationships and Outcomes

Multimorbidity and frailty are highly prevalent in older people with diabetes. This high prevalence is likely due to a combination of ageing and diabetes-related complications and other diabetes-associated comorbidities. Both multimorbidity and frailty are associated with a wide range of adverse outcomes in older people with diabetes, which are proportionally related to the number of morbidities and to the severity of frailty. Although, the multimorbidity pattern or cluster of morbidities that have the most adverse effect are not yet well defined, it appears that mental health disorders enhance the multimorbidity-related adverse outcomes. Therefore, comprehensive diabetes guidelines that incorporate a holistic approach that includes screening and management of mental health disorders such as depression is required. The adverse outcomes predicted by multimorbidity and frailty appear to be similar and include an increased risk of health care utilisation, disability and mortality. The differential effect of one condition on outcomes, independent of the other, still needs future exploration. In addition, prospective clinical trials are required to investigate whether interventions to reduce multimorbidity and frailty both separately and in combination would improve clinical outcomes.

NIR-II bioimaging of small molecule fluorophores: From basic research to clinical applications

Due to the low autofluorescence and deep-photo penetration, the second near-infrared region fluorescence imaging technology (NIR-II, 1000-2000 nm) has been widely utilized in basic scientific research and preclinical practice throughout the past decade. The most attractive candidates for clinical translation are organic NIR-II fluorophores with a small-molecule framework, owing to their low toxicity, high synthetic repeatability, and simplicity of chemical modification. In order to enhance the translation of small molecule applications in NIR-II bioimaging, NIR-II fluorescence imaging technology has evolved from its usage in cells to the diagnosis of diseases in large animals and even humans. Although several examples of NIR-II fluorescence imaging have been used in preclinical studies, there are still many challenges that need to be addressed before they can finally be used in clinical settings. In this paper, we reviewed the evolution of the chemical structures and photophysical properties of small-molecule fluorophores, with an emphasis on their biomedical applications ranging from small animals to humans. We also explored the potential of small-molecule fluorophores.

Cost-effectiveness of sodium-glucose cotransporter-2 inhibitors versus dipeptidyl peptidase-4 inhibitors among patients with type 2 diabetes with and without established cardiovascular diseases: A model-based simulation analysis using 10-year real-world data and targeted literature review

AIM

We conducted a model-based economic analysis of sodium-glucose cotransporter-2 inhibitors (SGLT2is) versus dipeptidyl peptidase-4 inhibitors (DPP4is) in patients with type 2 diabetes (T2D), with and without established cardiovascular diseases (CVDs), using 10-year real-world data.

MATERIALS AND METHODS

A Markov model was utilized to estimate healthcare costs and quality-adjusted life-years (QALYs) over a 10-year simulation time horizon from a healthcare sector perspective, with both costs and QALYs discounted at 3% annually. Model inputs were derived from analyses of Taiwan's National Health Insurance Research Database or published studies of Taiwanese populations. The primary outcome measure was the incremental cost-effectiveness ratios (ICERs). Incorporated with our study findings, a targeted literature review was conducted to synthesize updated evidence on the cost-effectiveness of SGLT2is versus DPP4is.

RESULTS

Over 10 years, use of SGLT2is versus DPP4is yielded ICERs of $3244 and $4186 per QALY gained for patients with T2D, with and without established CVDs, respectively. Results were robust across a series of sensitivity and scenario analyses, showing ICERs between $-1074 (cost-saving) and $8467 per QALY gained for patients with T2D with established CVDs and between $369 and $37 122 per QALY gained for patients with T2D without established CVDs.

CONCLUSIONS

Use of SGLT2is versus DPP4is was highly cost-effective for patients with T2D regardless of their CVD history in real-world clinical practice. Our results extend current evidence by showing SGLT2is as an economically rational alternative over DPP4is for T2D treatment in routine care. Future research is warranted to explore the heterogeneous economic benefits of SGLT2is given diverse patient characteristics in clinical settings.

Self-rated health and multimorbidity in patients with type 2 diabetes

The relationship between multimorbidity and self-rated health is well established. This study examined self-rated health in relation to multimorbidity, glycaemia and body weight specifically in adults with type 2 diabetes. Bootstrapped hierarchical logistic regression and structural equation modelling (SEM) were used to analyse survey data from 280 adults with type 2 diabetes. The odds of 'fair/bad/very bad' self-rated health increased 10-fold in patients with three (OR = 10.11 (3.36-30.40)) and four conditions (OR = 10.58 (2.9-38.25)), irrespective of glycaemic control (p < 0.001). The relationship between multimorbidity and perceived health was more pronounced in male patients. SEM generated a model with good fit, χ2 (CMIN) = 5.10, df = 3, p = 0.164, χ2 (CMIN)/df = 1.70, RMSEA = 0.05, CFI = 0.97, TLI = 0.95 and NFI = 0.94; self-rated health mediated relations between multimorbidity and BMI. Overall, this study highlights the potential of self-rated health to mediate relationships between multimorbidity and BMI, but not glycaemic control, in adults with type 2 diabetes.

Shared exposure liability of type 2 diabetes and other chronic conditions in the UK Biobank

AIMS

To investigate whether the cumulative exposure risks of incident T2D are shared with other common chronic diseases.

RESEARCH DESIGN AND METHODS

We first establish and report the cross-sectional prevalence, cross-sectional co-prevalence, and incidence of seven T2D-associated chronic diseases [hypertension, atrial fibrillation, coronary artery disease, obesity, chronic obstructive pulmonary disease (COPD), and chronic kidney and liver diseases] in the UK Biobank. We use published weights of genetic variants and exposure variables to derive the T2D polygenic (PGS) and polyexposure (PXS) risk scores and test their associations to incident diseases.

RESULTS

PXS was associated with higher levels of clinical risk factors including BMI, systolic blood pressure, blood glucose, triglycerides, and HbA1c in individuals without overt or diagnosed T2D. In addition to predicting incident T2D, PXS and PGS were significantly and positively associated with the incidence of all 7 other chronic diseases. There were 4% and 8% of individuals in the bottom deciles of PXS and PGS, respectively, who were prediabetic at baseline but had low risks of T2D and other chronic diseases. Compared to the remaining population, individuals in the top deciles of PGS and PXS had particularly high risks of developing chronic diseases. For instance, the hazard ratio of COPD and obesity for individuals in the top T2D PXS deciles was 2.82 (95% CI 2.39-3.35, P = 4.00 × 10-33) and 2.54 (95% CI 2.24-2.87, P = 9.86 × 10-50), respectively, compared to the remaining population. We also found that PXS and PGS were both significantly (P < 0.0001) and positively associated with the total number of incident diseases.

CONCLUSIONS

T2D shares polyexposure risks with other common chronic diseases. Individuals with an elevated genetic and non-genetic risk of T2D also have high risks of cardiovascular, liver, lung, and kidney diseases.

Associations between multimorbidity and adverse health outcomes in UK Biobank and the SAIL Databank: A comparison of longitudinal cohort studies

BACKGROUND

Cohorts such as UK Biobank are increasingly used to study multimorbidity; however, there are concerns that lack of representativeness may lead to biased results. This study aims to compare associations between multimorbidity and adverse health outcomes in UK Biobank and a nationally representative sample.

METHODS AND FINDINGS

These are observational analyses of cohorts identified from linked routine healthcare data from UK Biobank participants (n = 211,597 from England, Scotland, and Wales with linked primary care data, age 40 to 70, mean age 56.5 years, 54.6% women, baseline assessment 2006 to 2010) and from the Secure Anonymised Information Linkage (SAIL) databank (n = 852,055 from Wales, age 40 to 70, mean age 54.2, 50.0% women, baseline January 2011). Multimorbidity (n = 40 long-term conditions [LTCs]) was identified from primary care Read codes and quantified using a simple count and a weighted score. Individual LTCs and LTC combinations were also assessed. Associations with all-cause mortality, unscheduled hospitalisation, and major adverse cardiovascular events (MACEs) were assessed using Weibull or negative binomial models adjusted for age, sex, and socioeconomic status, over 7.5 years follow-up for both datasets. Multimorbidity was less common in UK Biobank than SAIL (26.9% and 33.0% with ≥2 LTCs in UK Biobank and SAIL, respectively). This difference was attenuated, but persisted, after standardising by age, sex, and socioeconomic status. The association between increasing multimorbidity count and mortality, hospitalisation, and MACE was similar between both datasets at LTC counts of ≤3; however, above this level, UK Biobank underestimated the risk associated with multimorbidity (e.g., mortality hazard ratio for 2 LTCs 1.62 (95% confidence interval 1.57 to 1.68) in SAIL and 1.51 (1.43 to 1.59) in UK Biobank, hazard ratio for 5 LTCs was 3.46 (3.31 to 3.61) in SAIL and 2.88 (2.63 to 3.15) in UK Biobank). Absolute risk of mortality, hospitalisation, and MACE, at all levels of multimorbidity, was lower in UK Biobank than SAIL (adjusting for age, sex, and socioeconomic status). Both cohorts produced similar hazard ratios for some LTCs (e.g., hypertension and coronary heart disease), but UK Biobank underestimated the risk for others (e.g., alcohol-related disorders or mental health conditions). Hazard ratios for some LTC combinations were similar between the cohorts (e.g., cardiovascular conditions); however, UK Biobank underestimated the risk for combinations including other conditions (e.g., mental health conditions). The main limitations are that SAIL databank represents only part of the UK (Wales only) and that in both cohorts we lacked data on severity of the LTCs included.

CONCLUSIONS

In this study, we observed that UK Biobank accurately estimates relative risk of mortality, unscheduled hospitalisation, and MACE associated with LTC counts ≤3. However, for counts ≥4, and for some LTC combinations, estimates of magnitude of association from UK Biobank are likely to be conservative. Researchers should be mindful of these limitations of UK Biobank when conducting and interpreting analyses of multimorbidity. Nonetheless, the richness of data available in UK Biobank does offers opportunities to better understand multimorbidity, particularly where complementary data sources less susceptible to selection bias can be used to inform and qualify analyses of UK Biobank.

Multimorbidity and catastrophic health expenditure among patients with diabetes in China: a nationwide population-based study

INTRODUCTION

Multimorbidity is common among patients with diabetes and can lead to catastrophic health expenditure (CHE) for their families. This study aims to investigate the prevalence of multimorbidity and CHE among people with diabetes in China, and the association between multimorbidity and CHE and whether this is influenced by socioeconomic status and health insurance type.

METHODS

A national survey was conducted in China in 2013 that included 8471 people aged ≥18 years who were living with diabetes. The concentration curve and concentration index were used to measure socioeconomic-related inequalities. Factors influencing CHE and the impact of multimorbidity on CHE according to socioeconomic status and health insurance type were examined by logistic regression.

RESULTS

There were 5524 (65.2%) diabetes patients with multimorbidity. The prevalence of CHE was 56.6%, with a concentration index of -0.030 (95% CI -0.035 to -0.026). For each additional chronic disease, the probability of CHE increased by 39% (OR=1.39, 95% CI 1.31 to 1.47). Factors that were positively associated (p<0.05) with CHE included older age; male sex; lower educational level; being retired, unemployed or jobless; being a non-smoker and non-drinker; having had no physical examination; lower socioeconomic status; being in an impoverished family; and residing in the central or western regions. Among participants with Urban Employee Basic Medical Insurance, Urban Resident Basic Medical Insurance, and New Rural Cooperative Medical Scheme, the probability of CHE increased by 32% (OR=1.32, 95% CI 1.23 to 1.43), 43% (OR=1.43, 95% CI 1.24 to 1.65) and 47% (OR=1.47, 95% CI 1.33 to 1.63), respectively, with each additional chronic disease. The association between multimorbidity and CHE was observed across all health insurance types irrespective of socioeconomic status.

CONCLUSIONS

Multimorbidity affects about two-thirds of Chinese patients with diabetes. Current health insurance schemes offer limited protection against CHE to patients' families.

Spatiotemporal Analysis of the Prevalence and Pattern of Multimorbidity in Older Chinese Adults

Background

Multimorbidity presents an enormous problem to societal and healthcare utilization under the context of aging population in low- and middle-income countries (LMICs). Currently, systematic studies on the profile of multimorbidity and its characteristics among Chinese elderly are lacking. We described the temporal and spatial trends in the prevalence of multimorbidity and explored chronological changes of comorbidity patterns in a large elderly population survey.

Methods

Data were extracted from the Chinese Longitudinal Healthy Longevity Study (CLHLS) conducted between 1998 and 2018 in a random selection of half of the counties and city districts. All the elderly aged 65 and older were included in the survey of eight waves. We used 13 investigated chronic diseases to measure the prevalence of multimorbidity by means of geography, subpopulation, and chronological changes. The patterns of multimorbidity were assessed by computing the value of relative risk (RR indicates the likelihood of certain diseases to be associated with multimorbidity) and the observed-to-expected ratio (O/E indicates the likelihood of the coexistence of a multimorbidity combination).

Results

From 1998 to 2018, the prevalence of multimorbidity went from 15.60 to 30.76%, increasing in the fluctuation across the survey of eight waves (p_{for trend} = 0.020). Increasing trends were observed similarly in a different gender group (p_male = 0.009; p_female = 0.004) and age groups among female participants (p_~80 = 0.009; p₈₁₋₉₀ = 0.004; p₉₁₋₁₀₀ = 0.035; p_101~ = 0.018). The gap in the prevalence of multimorbidity between the north and the south was getting narrow across the survey of eight waves. Hypertension was the highest prevalent chronic condition while diabetes was most likely to coexist with other chronic conditions in the CLHLS survey. The most frequently occurring clusters were hypertension and heart disease, hypertension and cataract, and hypertension and chronic lung disease. And, the cancer, TB, and Parkinson's disease cluster took the domination of O/E rankings over time, which had a higher probability of coexistence in all the multimorbidity combinations.

Conclusions

The prevalence of multimorbidity has been increasing nationwide, and more attention should be paid to a rapid growth in the southern part of China. It demands the effective diagnosis and treatment adopted to the highly prevalent comorbidities, and strategies and measures were adjusted to strongly relevant clusters.

Prevalence of Diabetes Among Community-Living Older Persons in the Philippines: The FITforFrail Study

Objective

To estimate the prevalence of diabetes among Filipino older persons living in the community.

Methodology

A cross-sectional analysis was done on a random sample of persons 60 years and older from the Focused Interventions for Frail Older Adults Research and Development Program (2018-2019). A diagnosis of diabetes was established by self-reported physician's diagnosis or if the person was on any antihyperglycemic drugs.

Results

The prevalence of self-reported diabetes was 20.5%, with no difference in age, sex, education, or body mass index between older persons with and without diabetes. The presence of 2 or more comorbidities was significantly more common among older persons with diabetes (p<0.001). Visual impairment (p<0.01), hypertension (p<0.001) and hyperlipidemia (p<0.001) were more frequent among those with diabetes.

Conclusion

Diabetes is prevalent among community-living older Filipinos. Therefore, effective public health measures for diabetes prevention and management are needed for the ever-growing older population, who are at the highest risk for morbidity and mortality.

The relationship between the level of exercise and hemoglobin A1c in patients with type 2 diabetes mellitus: a systematic review and meta-analysis

PURPOSE

The aim of the study was to evaluate the relationship between changes in hemoglobin A₁c (HbA_1c) and exercise levels in type 2 diabetes mellitus (T2DM) patients when performing various types of exercise.

METHODS

The inclusion criteria were randomized controlled trials involving adults with T2DM, intervention involving exercise alone, the overall duration of intervention ≥12 weeks, and reporting HbA_1c. Weighted mean difference (WMD) was defined as the mean difference between the intervention group and the control group weighted by the inverse of the squared standard error for each study, and all WMDs were pooled as overall effects. A meta-regression analysis was performed to evaluate the relationship between the exercise level and the WMD in HbA_1c.

RESULTS

Forty-eight studies (2395 subjects) were analyzed. The pooled WMD in HbA_1c decreased significantly (-0.5% [95% confidence intervals: -0.6 to -0.4]) but contained significant heterogeneity (Q = 103.8, P < 0.01; I² = 36.6%). A meta-regression analysis showed that the intensity (metabolic equivalents [METs]), time (min/session), or frequency (sessions/week) of the exercise was not associated with the HbA_1c. However, the overall duration of exercise (weeks) was significantly associated with the WMD in HbA_1c (meta-regression coefficient: 0.01 [95% confidence intervals: 0.002-0.016]; R² = 70.0%), and that result did not contain significant heterogeneity (P > 0.05; I² = 14.7%).

CONCLUSIONS

The exercise intervention decreases HbA_1c in T2DM patients. In addition, exercise for an extended duration was associated with an increase in HbA_1c, so the effects of exercise may be evident early on, but results suggested that exercise for a prolonged period alone may increase HbA_1c.

Diabetes complication burden and patterns and risk of mortality in people with schizophrenia and diabetes: A population-based cohort study with 16-year follow-up

Schizophrenia is associated with increased prevalence of diabetes. However, risk of diabetes complications as well as the impact of complication burden and patterns on subsequent mortality risk in schizophrenia patients with co-existing diabetes is understudied. This population-based, propensity-score matched (1:10) cohort study identified 6991 patients with incident diabetes and pre-existing schizophrenia and 68,682 patients with incident diabetes only (comparison group) between 2001 and 2016 in Hong Kong, using territory-wide medical-record database of public healthcare services. Complications were measured by Diabetes Complications Severity Index (DCSI), which stratified complication burden into 6 levels (DCSI score=0, 1, 2, 3, 4, or ≥5). Associations of diabetes complications, in terms of DCSI scores (complication burden), specific types and two-way combinations of complications (complication patterns), with all-cause mortality rate in schizophrenia were evaluated using Cox proportional-hazards models. Schizophrenia group had comparable macrovascular (adjusted OR 0.99 [95% CI 0.92-1.06]) and lower microvascular (0.79 [0.73-0.86]) complication rates relative to comparison group. Mortality risk ratio for schizophrenia was elevated at all complication burden levels, which conferred incremental impact on excess mortality in both groups. Cardiovascular diseases (1.60 [1.45-1.77]) and cerebrovascular-metabolic diseases (2.74 [1.25-5.99]) were associated with the highest differential mortality in schizophrenia among various specific complications and complication combinations, respectively. Our results indicate that schizophrenia patients with co-existing diabetes are at increased risk of excess mortality relative to those with diabetes alone, regardless of complication burden levels. Implementation of multilevel, targeted interventions is needed to improve diabetes-related outcomes and reduce mortality gap in this vulnerable population.

Hospitalisation events in people with chronic kidney disease as a component of multimorbidity: parallel cohort studies in research and routine care settings

BACKGROUND

Chronic kidney disease (CKD) typically co-exists with multimorbidity (presence of 2 or more long-term conditions: LTCs). The associations between CKD, multimorbidity and hospitalisation rates are not known. The aim of this study was to examine hospitalisation rates in people with multimorbidity with and without CKD. Amongst people with CKD, the aim was to identify risk factors for hospitalisation.

METHODS

Two cohorts were studied in parallel: UK Biobank (a prospective research study: 2006-2020) and Secure Anonymised Information Linkage Databank (SAIL: a routine care database, Wales, UK: 2011-2018). Adults were included if their kidney function was measured at baseline. Nine categories of participants were used: zero LTCs; one, two, three and four or more LTCs excluding CKD; and one, two, three and four or more LTCs including CKD. Emergency hospitalisation events were obtained from linked hospital records.

RESULTS

Amongst 469,339 UK Biobank participants, those without CKD had a median of 1 LTC and those with CKD had a median of 3 LTCs. Amongst 1,620,490 SAIL participants, those without CKD had a median of 1 LTC and those with CKD had a median of 5 LTCs. Compared to those with zero LTCs, participants with four or more LTCs (excluding CKD) had high event rates (rate ratios UK Biobank 4.95 (95% confidence interval 4.82-5.08)/SAIL 3.77 (3.71-3.82)) with higher rates if CKD was one of the LTCs (rate ratios UK Biobank 7.83 (7.42-8.25)/SAIL 9.92 (9.75-10.09)). Amongst people with CKD, risk factors for hospitalisation were advanced CKD, age over 60, multiple cardiometabolic LTCs, combined physical and mental LTCs and complex patterns of multimorbidity (LTCs in three or more body systems).

CONCLUSIONS

People with multimorbidity have high rates of hospitalisation. Importantly, the rates are two to three times higher when CKD is one of the multimorbid conditions. Further research is needed into the mechanism underpinning this to inform strategies to prevent hospitalisation in this very high-risk group.

Multimorbidity and the risk of major adverse kidney events: findings from the UK Biobank cohort

Background

Multimorbidity [the presence of two or more long-term conditions (LTCs)] is associated with a heightened risk of mortality, but little is known about its relationship with the risk of kidney events.

Methods

Associations between multimorbidity and major adverse kidney events [MAKE: the need for long-term kidney replacement therapy, doubling of serum creatinine, fall of estimated glomerular filtration rate (eGFR) to <15 mL/min/1.73 m² or 30% decline in eGFR] were studied in 68 505 participants from the UK Biobank cohort. Participants were enrolled in the study between 2006 and 2010. Associations between LTC counts and MAKE were tested using survival analyses accounting for the competing risk of death.

Results

Over a median follow-up period of 12.0 years, 2963 participants had MAKE. There were associations between LTC count categories and the risk of MAKE [one LTC adjusted subhazard ratio (sHR) = 1.29, 95% confidence interval (CI) 1.15–1.45; two LTCs sHR = 1.74 (95% CI 1.55–1.96); and three or more LTCs sHR = 2.41 (95% CI 2.14–2.71)]. This finding was more pronounced when only cardiometabolic LTCs were considered [one LTC sHR = 1.58 (95% CI 1.45–1.73); two LTCs sHR = 3.17 (95% CI 2.80–3.59); and three or more LTCs sHR = 5.24 (95% CI 4.34–6.33)]. Combinations of LTCs associated with MAKE were identified. Diabetes, hypertension and coronary heart disease featured most commonly in high-risk combinations.

Conclusions

Multimorbidity, and in particular cardiometabolic multimorbidity, is a risk factor for MAKE. Future research should study groups of patients who are at high risk of progressive kidney disease based on the number and type of LTCs.

The relationship between high-density lipoprotein cholesterol (HDL-C) and glycosylated hemoglobin in diabetic patients aged 20 or above: a cross-sectional study

AIM

The incidence rate of diabetes is increasing year by year, seriously threatening human health. As a predictor of glycemic control, glycated hemoglobin is reported to be related to various complications and prognoses of diabetes. Besides, HDL-C dyslipidemia is a component of metabolic syndrome and may be related to various cardiovascular and cerebrovascular diseases. The principal objective of this project was to investigate the relationship between HDL-C and glycosylated hemoglobin in adult diabetic patients.

METHODS

A total of 3171 adult diabetic patients aged 20 years and above were included in the present study from the National Health and Nutrition Examination Survey (NHANES). HDL-C and glycosylated hemoglobin were regarded as independent and dependent variables, respectively. EmpowerStats software and R (version 3.4.3) were used to examine the association between HDL-C and glycosylated hemoglobin.

RESULTS

HDL-C was inversely associated with glycohemoglobin after adjusting for other covariates (β = - 0.004, 95% CI:- 0.008 to - 0.000, p = 0.044). Race/ethnicity and age were considered the most prominent interactive factors that affect the relationship between HDL and glycosylated hemoglobin by the interaction analysis. A U-shaped association was detected between HDL-C and glycosylated hemoglobin for people of other race/ethnicity or aged 60 and above, which had an inflection point of HDL-C at 60 mg/dL. In contrast, we observed an inverted U-shaped distribution between HDL-C and glycosylated hemoglobin in people under 40 with point of inflection located at 60 mg/dL as well.

CONCLUSIONS

HDL-C in diabetic patients is inversely associated with glycosylated hemoglobin and may be relevant to glycemic control. However, a U-shaped relationship was also observed in a certain kind of people, which implied that, though HDL-C is considered as metabolism and anti-atherogenic property, for diabetics, it is not the higher, the better.

Self-management interventions for Type 2 Diabetes: systematic review protocol focusing on patient workload and capacity support

Introduction:

People living with type 2 diabetes undertake a range of tasks to manage their condition, collectively referred to as self-management. Interventions designed to support self-management vary in their content, and efficacy. This systematic review will analyse self-management interventions for type 2 diabetes drawing on theoretical models of patient workload and capacity.

Methods and analysis:

Five electronic databases (Medline, Embase, CENTRAL, CINAHL and PsycINFO) will be searched from inception to 27th April 2021, supplemented by citation searching and hand-searching of reference lists. Two reviewers will independently review titles, abstracts and full texts. Inclusion criteria include Population: Adults with type 2 diabetes mellitus; Intervention: Randomised controlled trials of self-management support interventions; Comparison: Usual care; Outcomes: HbA1c (primary outcome) health-related quality of life (QOL), medication adherence, self-efficacy, treatment burden, healthcare utilization (e.g. number of appointment, hospital admissions), complications of type 2 diabetes (e.g. nephropathy, retinopathy, neuropathy, macrovascular disease) and mortality; Setting: Community. Study quality will be assessed using the Effective Practice and Organisation of Care (EPOC) risk of bias tool. Interventions will be classified according to the EPOC taxonomy and the PRISMS self-management taxonomy and grouped into similar interventions for analysis. Clinical and methodological heterogeneity will be assessed within subgroups, and random effects meta-analyses performed if appropriate. Otherwise, a narrative synthesis will be performed. Interventions will be graded on their likely impact on patient workload and support for patient capacity. The impact of these theoretical constructs on study outcomes will be explored using meta-regression.

Conclusion

This review will provide a broad overview of self-management interventions, analysed within the cumulative complexity model theoretical framework. Analyses will explore how the workload associated with self-management, and support for patient capacity, impact on outcomes of self-management interventions.

Registration number:

PROSPERO CRD42021236980.

Multimorbidity and achievement of treatment goals among patients with type 2 diabetes: a primary care, real-world study

Background

Type 2 diabetes (T2D), with its prevalence and disability-causing nature, is a challenge for primary health care. Most patients with T2D are multimorbid, i.e. have one or more long-term diseases in addition to T2D. Multimorbidity may play a role in the achievement of T2D treatment targets, but is still not fully understood. The aims of the present cross-sectional, register-based study were to evaluate the prevalence and the most common patterns of multimorbidity among patients with T2D; and to study the potential associations between multimorbidity and treatment goal achievement, including measurements of glycosylated haemoglobin A1c (HbA1c), low-density lipoprotein (LDL) and systolic blood pressure (sBP).

Methods

The study population consisted of 4545 primary care patients who received a T2D diagnosis between January 2011 and July 2019 in Rovaniemi Health Centre, Finland. Data on seven long-term concordant (T2D-related) diseases, eight long-term discordant (non-T2D-related) diseases, potential confounders (age, sex, body mass index, prescribed medication), and the outcomes studied were collected from patients’ records. Logistic regression models with odds ratios (ORs) and 95 % confidence intervals (CIs) were assessed to determine the associations between multimorbidity and the achievement of treatment targets.

Results

Altogether, 93 % of the patients had one or more diseases in addition to T2D, i.e. were considered multimorbid. Furthermore, 21 % had only concordant disease(s) (Concordant subgroup), 8 % had only discordant disease(s) (Discordant subgroup) and 64 % had both (Concordant and discordant subgroup). As either single diseases or in combination with others, hypertension, musculoskeletal (MS) disease and hyperlipidaemia were the most prevalent multimorbidity patterns. Being multimorbid in general (OR 1.32, CI 1.01–1.70) and belonging to the Concordant (OR 1.45, CI 1.08–1.95) and Concordant and discordant (OR 1.31, CI 1.00–1.72) subgroups was associated with achievement of the HbA1c treatment target. Belonging to the Concordant and discordant subgroup was related to meeting the LDL treatment target (OR 1.31, CI 1.00–1.72).

Conclusions

Multimorbidity, including cardiovascular risk and the musculoskeletal disease burden, was extremely prevalent among the T2D patients who consulted primary health care. Primary care clinicians should survey the possible co-existence of long-term diseases among T2D patients to help maintain adequate treatment of T2D.

An analysis of frailty and multimorbidity in 20,566 UK Biobank participants with type 2 diabetes

Background

Frailty and multimorbidity are common in type 2 diabetes (T2D), including people <65 years. Guidelines recommend adjustment of treatment targets in people with frailty or multimorbidity. It is unclear how recommendations to adjust treatment targets in people with frailty or multimorbidity should be applied to different ages. We assess implications of frailty/multimorbidity in middle/older-aged people with T2D.

Methods

We analysed UK Biobank participants (n = 20,566) with T2D aged 40–72 years comparing two frailty measures (Fried frailty phenotype and Rockwood frailty index) and two multimorbidity measures (Charlson Comorbidity index and count of long-term conditions (LTCs)). Outcomes were mortality, Major Adverse Cardiovascular Event (MACE), hospitalization with hypoglycaemia or fall/fracture.

Results

Here we show  that choice of measure influences the population identified: 42% of participants are frail or multimorbid by at least one measure; 2.2% by all four measures. Each measure is associated with mortality, MACE, hypoglycaemia, and fall or fracture. The absolute 5-year mortality risk is higher in older versus younger participants with a given level of frailty (e.g. 1.9%, and 9.9% in men aged 45 and 65, respectively, using frailty phenotype) or multimorbidity (e.g. 1.3%, and 7.8% in men with 4 LTCs aged 45 and 65, respectively). Using frailty phenotype, the relationship between higher HbA1c and mortality is stronger in frail compared with pre-frail or robust participants.

Conclusions

Assessment of frailty/multimorbidity should be embedded within routine management of middle-aged and older people with T2D. Method of identification as well as features such as age impact baseline risk and should influence clinical decisions (e.g. glycaemic control).

Therapeutic inertia in the management of dyslipidaemia and hypertension in incident type 2 diabetes and the resulting risk factor burden: Real-world evidence from primary care

OBJECTIVE

To investigate trends in the prevalence of hypertension and dyslipidaemia in incident type 2 diabetes (T2DM), time to antihypertensive (AHT) and lipid-lowering therapy (LLT), and the association with systolic blood pressure (SBP) and lipid control.

RESEARCH DESIGN AND METHODS

Using The Health Improvement Network UK primary care database, 254 925 people with incident T2DM and existing dyslipidaemia or hypertension were identified. Among those without atherosclerotic cardiovascular disease (ASCVD) history and not on AHT or LLT at diagnosis, the adjusted median months to initiating an AHT or an LLT, and the probabilities of high SBP or lipid levels over 2 years in people initiating therapy within or after 1 year were evaluated according to high and low ASCVD risk status.

RESULTS

At diabetes diagnosis, 66% and 66% had dyslipidaemia and hypertension, respectively. During 2005 to 2016, dyslipidaemia prevalence increased by 10% in people aged <60 years, while hypertension prevalence remained stable in all age groups. Among those with high ASCVD risk status in the age groups 18 to 39, 40 to 49, and 50 to 59 years, the median number of months to initiation of therapy were 20.4 (95% confidence interval [CI] 20.3-20.5), 10.9 (95% CI 10.8-11.0), and 9.5 (95% CI 9.4-9.6) in the dyslipidaemia subcohort, and 28.1 (95% CI 28.0-28.2), 19.2 (95% CI 19.1-19.3), and 19.9 (95% CI 19.8-20.0) in the hypertension subcohort. Among people with high and low ASCVD risk status, respectively, compared to early LLT initiators, those who initiated LLT after 1 year had a 65.3% to 85.3% and a 65.0% to 85.3% significantly higher probability of failing lipid control at 2 years of follow-up, while late AHT initiators had a 46.5% to 57.9% and a 40.0% to 58.7% significantly higher probability of failing SBP control.

CONCLUSIONS

Significant delay in initiating cardioprotective therapies was observed, and time to first prescription was similar in the primary prevention setting, irrespective of ASCVD risk status across all T2DM diagnosis age groups, resulting in poor risk factor control at 2 years of follow-up.

Temporal trends in co-morbidities and cardiometabolic risk factors at the time of type 2 diabetes diagnosis in the UK

AIM

To evaluate temporal patterns in co-morbidities, cardiometabolic risk factors and a high atherosclerotic cardiovascular disease (ASCVD) risk population at type 2 diabetes (T2D) diagnosis by age groups and sex.

MATERIALS AND METHODS

From the UK primary care database, 248,619 people with a new diagnosis of T2D during 2005-2016 were identified. Among people without ASCVD, high ASCVD risk was defined as two or more of current smoker, grade 2+ obesity, hypertension, dyslipidaemia or microvascular disease. Cardiometabolic multimorbidity (CMM) was defined as two or more of cardiovascular disease, microvascular disease, hypertension, dyslipidaemia, grade 2+ obesity or cancer. Temporal patterns in the distribution of cardiometabolic risk factors were evaluated.

RESULTS

While the prevalence of ASCVD was stable over time (approximately 18%), 50% were identified to have a high ASCVD risk (26% and 38% in the 18-39 and 40-49 years age groups, respectively), with an increasing trend across all age groups. Overall, 51% had CMM at diagnosis, increasing during 2005-2016 for the 18-39 years age group by 14%-17%, for the 40-49 years age group by 27%-33%, for the 50-59 years age group by 41%-50%, for the 60-69 years age group by 56%-65%, and for the 70-79 years age group by 65%-80%. People with young-onset T2D had significantly higher HbA1c, body mass index and lipids at diagnosis (all p < .01). The proportions with an HbA1c of 7.5% or higher in the 18-39 and 40-49 years age groups were 58% and 54%, respectively, significantly and consistently higher over the last decade compared with those aged 50 years or older, with males having higher proportions of 15-26 and 10-18 percentage points, respectively, compared with females.

CONCLUSIONS

CMM and high ASCVD risk have been increasing consistently across all age groups and in both sex, in particular CMM in those aged younger than 50 years. Our findings indicate that the European Society of Cardiology-European Association for the Study of Diabetes recommendations need to change to consider people with young-onset T2D as a high-risk group, as recommended in the Primary Care Diabetes Europe position statement.

Functional categories based on cognition and activities of daily living predict all-cause mortality in older adults with diabetes mellitus: The Japanese Elderly Diabetes Intervention Trial

AIM

Although the glycemic target in older diabetes patients is based on cognition, activities of daily living and multimorbidity in the Japanese guideline, evidence of the relationships is limited. Thus, we aimed to assess the relationship between functional category and mortality in older people with diabetes.

METHODS

We evaluated the data of 843 older diabetes patients in a 6-year prospective study, and the association between functional categories and all-cause mortality. The patients were divided into three functional categories based on cognition, instrumental activities of daily living and basic activities of daily living using the Mini-Mental State Examination, Tokyo Metropolitan Institute of Gerontology Index of Competence and Barthel Index at baseline, respectively (model 1). Those with multimorbidity (≥4 of 8 morbidities) were classified into category III (model 2). The functional category assessed using eight items from the Tokyo Metropolitan Institute of Gerontology Index of Competence and Barthel Index was also constructed (model 3). Hazard ratios and 95% confidence intervals were calculated in the Cox regression analysis using age, sex, body mass index, glycated hemoglobin level, total cholesterol level, estimated glomerular filtration rate and frequency of severe hypoglycemia as covariates.

RESULTS

During the 6-year follow up, 64 incident mortalities occurred. The hazard ratios for mortality in categories II and III (as the reference of category I) were 1.83 (95% confidence interval 1.06-3.14, P = 0.030) and 3.05 (95% confidence interval 1.12-8.26, P = 0.029) after adjustment for covariates, respectively (model 1). Models 2 and 3 showed similar associations between functional category and mortality.

CONCLUSIONS

The functional categories predicted all-cause mortality in older adults with diabetes. Geriatr Gerontol Int 2021; 21: 512-518.

Estimating and characterizing the burden of multimorbidity in the community: A comprehensive multistep analysis of two large nationwide representative surveys in France

BACKGROUND

Given the increasing burden of chronic conditions, multimorbidity is now a priority for healthcare and public health systems worldwide. Appropriate methodological approaches for assessing the phenomenon have not yet been established, resulting in inconsistent and incomplete descriptions. We aimed to estimate and characterize the burden of multimorbidity in the adult population in France in terms of number and type of conditions, type of underlying mechanisms, and analysis of the joint effects for identifying combinations with the most deleterious interaction effects on health status.

METHODS AND FINDINGS

We used a multistep approach to analyze cross-sectional and longitudinal data from 2 large nationwide representative surveys: 2010/2014 waves of the Health, Health Care, and Insurance Survey (ESPS 2010-2014) and Disability Healthcare Household Survey 2008 (HSM 2008), that collected similar data on 61 chronic or recurrent conditions. Adults aged ≥25 years in either ESPS 2010 (14,875) or HSM 2008 (23,348) were considered (participation rates were 65% and 62%, respectively). Longitudinal analyses included 7,438 participants of ESPS 2010 with follow-up for mortality (97%) of whom 3,798 were reinterviewed in 2014 (52%). Mortality, activity limitation, self-reported health, difficulties in activities/instrumental activities of daily living, and Medical Outcomes Study Short-Form 12-Item Health Survey were the health status measures. Multiple regression models were used to estimate the impact of chronic or recurrent conditions and multimorbid associations (dyads, triads, and tetrads) on health status. Etiological pathways explaining associations were investigated, and joint effects and interactions between conditions on health status measures were evaluated using both additive and multiplicative scales. Forty-eight chronic or recurrent conditions had an independent impact on mortality, activity limitations, or perceived heath. Multimorbidity prevalence varied between 30% (1-year time frame) and 39% (lifetime frame), and more markedly according to sex (higher in women), age (with greatest increases in middle-aged), and socioeconomic status (higher in less educated and low-income individuals and manual workers). We identified various multimorbid combinations, mostly involving vasculometabolic and musculoskeletal conditions and mental disorders, which could be explained by direct causation, shared or associated risk factors, or less frequently, confounding or chance. Combinations with the highest health impacts included diseases with complications but also associations of conditions affecting systems involved in locomotion and sensorial functions (impact on activity limitations), and associations including mental disorders (impact on perceived health). The interaction effects of the associated conditions varied on a continuum from subadditive and additive (associations involving cardiometabolic conditions, low back pain, osteoporosis, injury sequelae, depression, and anxiety) to multiplicative and supermultiplicative (associations involving obesity, chronic obstructive pulmonary disease, migraine, and certain osteoarticular pathologies). Study limitations included self-reported information on chronic conditions and the insufficient power of some analyses.

CONCLUSIONS

Multimorbidity assessments should move beyond simply counting conditions and take into account the variable impacts on health status, etiological pathways, and joint effects of associated conditions. In particular, the multimorbid combinations with substantial health impacts or shared risk factors deserve closer attention. Our findings also suggest that multimorbidity assessment and management may be beneficial already in midlife and probably earlier in disadvantaged groups.

Decompression of Multimorbidity Along the Disease Trajectories of Diabetes Mellitus Patients

Multimorbidity, the presence of two or more diseases in a patient, is maybe the greatest health challenge for the aging populations of many high-income countries. One of the main drivers of multimorbidity is diabetes mellitus (DM) due to its large number of risk factors and complications. Yet, we currently have very limited understanding of how to quantify multimorbidity beyond a simple counting of diseases and thereby inform prevention and intervention strategies tailored to the needs of elderly DM patients. Here, we conceptualize multimorbidity as typical temporal progression patterns of multiple diseases, so-called trajectories, and develop a framework to perform a matched and sex-specific comparison between DM and non-diabetic patients. We find that these disease trajectories can be organized into a multi-level hierarchy in which DM patients progress from relatively healthy states with low mortality to high-mortality states characterized by cardiovascular diseases, chronic lower respiratory diseases, renal failure, and different combinations thereof. The same disease trajectories can be observed in non-diabetic patients, however, we find that DM patients typically progress at much higher rates along their trajectories. Comparing male and female DM patients, we find a general tendency that females progress faster toward high multimorbidity states than males, in particular along trajectories that involve obesity. Males, on the other hand, appear to progress faster in trajectories that combine heart diseases with cerebrovascular diseases. Our results show that prevention and efficient management of DM are key to achieve a compression of morbidity into higher patient ages. Multidisciplinary efforts involving clinicians as well as experts in machine learning and data visualization are needed to better understand the identified disease trajectories and thereby contribute to solving the current multimorbidity crisis in healthcare.

Cardiovascular events and mortality in people with type 2 diabetes and multimorbidity: A real-world study of patients followed for up to 19 years

AIM

To quantify the risk of cardiovascular disease (CVD) events, all-cause mortality and cardiovascular mortality in patients diagnosed with type 2 diabetes (T2D) and multimorbidity.

METHODS

This retrospective study used English primary and secondary care data to identify 120 409 adults newly diagnosed with T2D during 2000-2018 with follow-up until death or 31 December 2018. Patients were classified according to the level and type of multimorbidity at T2D diagnosis, and adjusted hazard ratios (aHRs) were calculated for each outcome.

RESULTS

In total, 66 977 (55.6%) patients had T2D only, 37 894 (31.5%) had one co-morbidity, 11 357 (9.4%) had two co-morbidities, 3186 (2.6%) patients had three co-morbidities and 995 (0.8%) patients had four or more co-morbidities. Co-morbidities were associated with increased aHRs for all outcomes. Compared with patients with T2D only, at 19 years after diagnosis of T2D the aHR for four or more co-morbidities was 2.57 (95% CI 2.45-2.69) for a CVD event, 1.73 (1.68-1.78) for all-cause mortality and 2.68 (2.52-2.85) for cardiovascular mortality. Also, 100 183 (83.2%) patients had no CVD co-morbidities, 16 874 (14.0%) patients had one CVD co-morbidity and 3352 (2.8%) patients had two or more co-morbidities. Compared with patients with no CVD co-morbidities, at 19 years after diagnosis of T2D the aHR for two or more CVD co-morbidities was 2.42 (2.35-2.49) for a CVD event, 1.44 (1.42-1.47) for all-cause mortality and 2.44 (2.35-2.54) for cardiovascular mortality.

CONCLUSION

In people with T2D, level of multimorbidity and, in particular, CVD multimorbidity increased the risk of subsequent CVD events, mortality and cardiovascular mortality.

Can Trans-Syndromal Prototypes ("Types") Improve Depiction of Complex Psychiatric Cases?: An Alternative Way to Consider Concordant Comorbid Psychiatric Disorders and Their Contexts as Coherent Units for Research, Assessment, and Treatment Planning

BACKGROUND AND AIMS

In many psychiatric settings, patients with complex cases are the rule rather than the exception. These cases are characterized by multidiagnostic conditions, often complicated with distressed social contexts, for which few if any evidence-based practice guidelines exist. The aim of this hypothesis-generating article is to consider whether and how these cases might comprise trans-syndromal prototypes ("types"), coherent units that could serve as the basis for further study, assessment, and treatment planning.

METHODS

For context, psychiatric and medical visit diagnoses and problem lists noted among principle visit diagnoses and "snapshot" portions of electronic medical records were tabulated for 293 psychiatric outpatients seen consecutively during a 1-week period at a university psychiatric clinic. By considering resulting comorbidity patterns in these records from the perspectives of clinicians caring for these patients, several commonly encountered diagnostic-problem sets emerged as candidate types.

RESULTS

Of 293 patients, only 18% had a single diagnosis, 43% had two, 29% had three, and 7% had four or more noted. Occurring in assorted combinations, specific diagnostic areas noted included depressive disorders (68%, the large majority major depressive disorder recurrent), anxiety disorders (60%, the large majority generalized anxiety disorder, with or without panic disorder and/or social anxiety disorder), posttraumatic stress disorder (22%), attention deficit hyperactivity disorder (ADHD) (17%), alcohol and substance abuse disorders (16%), personality disorders (11%), and bipolar disorders (18%). Several illustrative candidate types emerging from this population are described including major anxious depressive disorder, anxiety disorder secondary to ADHD, complex emotional instability disorder, multi-impulsive eating disorder, substance-dependent impoverished personality disorder, painful mood disorder, and complex personal and cultural trauma disorder. Other potential types are identified as well.

CONCLUSIONS AND IMPLICATIONS

The types described here are but a small selection, because other settings including community mental health centers, private practices, public and private hospitals, and forensic facilities see a variety of other types as well. The study of types might provide important findings about pathogenesis, course, outcome, and treatment to augment information obtained from examination of individual diagnostic components.

Associations between multimorbidity and glycaemia (HbA1c) in people with type 2 diabetes: cross-sectional study in Australian general practice

OBJECTIVES

To explore the prevalence of multimorbidity as well as individual and combinations of long-term conditions (LTCs) in people with type 2 diabetes (T2D) attending Australian general practice, using electronic health record (EHR) data. We also examine the association between multimorbidity condition count (total/concordant(T2D related)/discordant(unrelated)) and glycaemia (glycated haemoglobin, HbA1c).

DESIGN

Cross-sectional study.

SETTING

Australian general practice.

PARTICIPANTS

69 718 people with T2D with a general practice encounter between 2013 and 2015 captured in the MedicineInsight database (EHR Data from 557 general practices and >3.8 million Australian patients).

PRIMARY AND SECONDARY OUTCOME MEASURES

Prevalence of multimorbidity, individual and combinations of LTCs. Multivariable linear regression models used to examine associations between multimorbidity counts and HbA1c (%).

RESULTS

Mean (SD) age 66.42 (12.70) years, 46.1% female and mean (SD) HbA1c 7.1 (1.4)%. More than 90% of participants with T2D were living with multimorbidity. Discordant conditions were more prevalent (83.4%) than concordant conditions (69.9 %). The three most prevalent discordant conditions were: painful conditions (55.4%), dyspepsia (31.6%) and depression (22.8%). The three most prevalent concordant conditions were hypertension (61.4%), coronary heart disease (17.1%) and chronic kidney disease (8.5%). The three most common combinations of conditions were: painful conditions and hypertension (38.8%), painful conditions and dyspepsia (23.1%) and hypertension and dyspepsia (22.7%). We found no associations between any multimorbidity counts (total, concordant and discordant) or combinations and HbA1c.

CONCLUSIONS

Multimorbidity was common in our cohort of people with T2D attending Australian general practice, but was not associated with glycaemia. Although we did not explore mortality in this study, our results suggest that the increased mortality in those with multimorbidity and T2D observed in other studies may not be linked to glycaemia. Interestingly, discordant conditions were more prevalent than concordant conditions with painful conditions being the second most common comorbidity. Better understanding of the implications of different patterns of multimorbidity in people with T2D will allow more effective tailored care.

Multimorbidity, glycaemic variability and time in target range in people with type 2 diabetes: A baseline analysis of the GP-OSMOTIC trial

AIMS

To explore associations between multimorbidity condition counts (total; concordant (diabetes-related); discordant (unrelated to diabetes)) and glycaemia (HbA1c; glycaemic variability (GV); time in range (TIR)) using data from a randomised controlled trial examining effectiveness of continuous glucose monitoring (CGM) in people with type 2 diabetes (T2D).

METHODS

Cross-sectional study: 279 people with T2D using baseline data from the General Practice Optimising Structured MOnitoring To Improve Clinical outcomes (GP-OSMOTIC) trial from 25 general practices in Australia. Number of long-term conditions (LTCs) in addition to T2D used to quantify total/concordant/discordant multimorbidity counts. GV (measured by coefficient of variation (CV)) and TIR derived from CGM data. Multivariable linear regression models used to examine associations between multimorbidity counts, HbA1c (%), GV and TIR.

RESULTS

Mean (SD) age of participants 60.4 (9.9) years; 40.9% female. Multimorbidity was present in 89.2% of participants. Most prevalent comorbid LTCs: hypertension (57.4%), painful conditions (29.8%), coronary heart disease (22.6%) and depression (19.0%). No evidence of associations between multimorbidity counts, HbA1c, GV and TIR.

CONCLUSIONS

While multimorbidity was common in this T2D cohort, it was not associated with HbA1c, CV or TIR. Future studies should explore factors other than glycaemia that contribute to the increased mortality observed in those with multimorbidity and T2D.

Multimorbidity, polypharmacy, and COVID-19 infection within the UK Biobank cohort

BACKGROUND

It is now well recognised that the risk of severe COVID-19 increases with some long-term conditions (LTCs). However, prior research primarily focuses on individual LTCs and there is a lack of data on the influence of multimorbidity (≥2 LTCs) on the risk of COVID-19. Given the high prevalence of multimorbidity, more detailed understanding of the associations with multimorbidity and COVID-19 would improve risk stratification and help protect those most vulnerable to severe COVID-19. Here we examine the relationships between multimorbidity, polypharmacy (a proxy of multimorbidity), and COVID-19; and how these differ by sociodemographic, lifestyle, and physiological prognostic factors.

METHODS AND FINDINGS

We studied data from UK Biobank (428,199 participants; aged 37-73; recruited 2006-2010) on self-reported LTCs, medications, sociodemographic, lifestyle, and physiological measures which were linked to COVID-19 test data. Poisson regression models examined risk of COVID-19 by multimorbidity/polypharmacy and effect modification by COVID-19 prognostic factors (age/sex/ethnicity/socioeconomic status/smoking/physical activity/BMI/systolic blood pressure/renal function). 4,498 (1.05%) participants were tested; 1,324 (0.31%) tested positive for COVID-19. Compared with no LTCs, relative risk (RR) of COVID-19 in those with 1 LTC was no higher (RR 1.12 (CI 0.96-1.30)), whereas those with ≥2 LTCs had 48% higher risk; RR 1.48 (1.28-1.71). Compared with no cardiometabolic LTCs, having 1 and ≥2 cardiometabolic LTCs had a higher risk of COVID-19; RR 1.28 (1.12-1.46) and 1.77 (1.46-2.15), respectively. Polypharmacy was associated with a dose response higher risk of COVID-19. All prognostic factors were associated with a higher risk of COVID-19 infection in multimorbidity; being non-white, most socioeconomically deprived, BMI ≥40 kg/m2, and reduced renal function were associated with the highest risk of COVID-19 infection: RR 2.81 (2.09-3.78); 2.79 (2.00-3.90); 2.66 (1.88-3.76); 2.13 (1.46-3.12), respectively. No multiplicative interaction between multimorbidity and prognostic factors was identified. Important limitations include the low proportion of UK Biobank participants with COVID-19 test data (1.05%) and UK Biobank participants being more affluent, healthier and less ethnically diverse than the general population.

CONCLUSIONS

Increasing multimorbidity, especially cardiometabolic multimorbidity, and polypharmacy are associated with a higher risk of developing COVID-19. Those with multimorbidity and additional factors, such as non-white ethnicity, are at heightened risk of COVID-19.