Three-year trajectories of metabolic risk factors predict subsequent long-term mortality in patients with type 2 diabetes

Precision subclassification of type 2 diabetes: a systematic review

BACKGROUND

Heterogeneity in type 2 diabetes presentation and progression suggests that precision medicine interventions could improve clinical outcomes. We undertook a systematic review to determine whether strategies to subclassify type 2 diabetes were associated with high quality evidence, reproducible results and improved outcomes for patients.

METHODS

We searched PubMed and Embase for publications that used 'simple subclassification' approaches using simple categorisation of clinical characteristics, or 'complex subclassification' approaches which used machine learning or 'omics approaches in people with established type 2 diabetes. We excluded other diabetes subtypes and those predicting incident type 2 diabetes. We assessed quality, reproducibility and clinical relevance of extracted full-text articles and qualitatively synthesised a summary of subclassification approaches.

RESULTS

Here we show data from 51 studies that demonstrate many simple stratification approaches, but none have been replicated and many are not associated with meaningful clinical outcomes. Complex stratification was reviewed in 62 studies and produced reproducible subtypes of type 2 diabetes that are associated with outcomes. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into clinically meaningful subtypes.

CONCLUSION

Critical next steps toward clinical implementation are to test whether subtypes exist in more diverse ancestries and whether tailoring interventions to subtypes will improve outcomes.

6-Year trajectory of fasting plasma glucose (FPG) and mortality risk among individuals with normal FPG at baseline: a prospective cohort study

BACKGROUND

Higher fasting plasma glucose (FPG) levels were associated with an increased risk of all-cause mortality; however, the associations between long-term FPG trajectory groups and mortality were unclear, especially among individuals with a normal FPG level at the beginning. The aims of this study were to examine the associations of FPG trajectories with the risk of mortality and identify modifiable lifestyle factors related to these trajectories.

METHODS

We enrolled 50,919 individuals aged ≥ 20 years old, who were free of diabetes at baseline, in the prospective MJ cohort. All participants completed at least four FPG measurements within 6 years after enrollment and were followed until December 2011. FPG trajectories were identified by group-based trajectory modeling. We used Cox proportional hazards models to examine the associations of FPG trajectories with mortality, adjusting for age, sex, marital status, education level, occupation, smoking, drinking, physical activity, body mass index, baseline FPG, hypertension, dyslipidemia, cardiovascular disease or stroke, and cancer. Associations between baseline lifestyle factors and FPG trajectories were evaluated using multinomial logistic regression.

RESULTS

We identified three FPG trajectories as stable (n = 32,481), low-increasing (n = 17,164), and high-increasing (n = 1274). Compared to the stable group, both the low-increasing and high-increasing groups had higher risks of all-cause mortality (hazard ratio (HR) = 1.18 (95% CI 0.99-1.40) and 1.52 (95% CI 1.09-2.13), respectively), especially among those with hypertension. Compared to participants with 0 to 1 healthy lifestyle factor, those with 6 healthy lifestyle factors were more likely to be in the stable group (ORlow-increasing = 0.61, 95% CI 0.51-0.73; ORhigh-increasing = 0.20, 95% CI 0.13-0.32).

CONCLUSIONS

Individuals with longitudinally increasing FPG had a higher risk of mortality even if they had a normal FPG at baseline. Adopting healthy lifestyles may prevent individuals from transitioning into increasing trajectories.

Time trajectories of key cardiometabolic parameters and of cardiovascular risk in subjects with diabetes in a real world setting

BACKGROUND AND AIMS

Diabetes is associated with increased cardiovascular risk. Glycated haemoglobin (HbA_1c), lipid parameters and blood pressure are known risk factors for adverse outcome. The aim of the study was to explore the time trajectories of these key parameters and of the associated cardiovascular risk.

METHODS

We linked the diabetes electronic health records to the laboratory information system so as to investigate the trajectories of key metabolic parameters from 3 years prior to the diagnosis of diabetes to 10 years after diagnosis. We calculated the cardiovascular risk at the different time points during this period using the United Kingdom Prospective Study (UKPDS) risk engine.

RESULTS

The study included 21,288 patients. The median age at diagnosis was 56 years and 55.3% were male. There was a sharp decrease in HbA_1c after diagnosis of diabetes, but there was a progressive rise thereafter. All lipid parameters after diagnosis also improved in the year of diagnosis, and these improvements persisted even up to 10 years post-diagnosis. There was no discernible trend in mean systolic or diastolic blood pressures following diagnosis of diabetes. There was a slight decrease in the UKPDS-estimated cardiovascular risk after diagnosis of diabetes followed by a progressive increase. Estimated glomerular filtration rate declined at an average rate of 1.33 ml/min/1.73 m²/year.

CONCLUSIONS

Our data suggest that lipid control should be tightened with increasing duration of diabetes since this is more readily achievable than HbA_1c lowering and since other factors such as age and duration of diabetes are unmodifiable.

Systematic review of precision subclassification of type 2 diabetes

Heterogeneity in type 2 diabetes presentation, progression and treatment has the potential for precision medicine interventions that can enhance care and outcomes for affected individuals. We undertook a systematic review to ascertain whether strategies to subclassify type 2 diabetes are associated with improved clinical outcomes, show reproducibility and have high quality evidence. We reviewed publications that deployed 'simple subclassification' using clinical features, biomarkers, imaging or other routinely available parameters or 'complex subclassification' approaches that used machine learning and/or genomic data. We found that simple stratification approaches, for example, stratification based on age, body mass index or lipid profiles, had been widely used, but no strategy had been replicated and many lacked association with meaningful outcomes. Complex stratification using clustering of simple clinical data with and without genetic data did show reproducible subtypes of diabetes that had been associated with outcomes such as cardiovascular disease and/or mortality. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into meaningful groups. More studies are needed to test these subclassifications in more diverse ancestries and prove that they are amenable to interventions.

Baseline and longitudinal trajectories of body-mass index and all-cause mortality among patients with type 2 diabetes

AIM

To investigate the associations of baseline body mass index (BMI) and longitudinal BMI trajectories with all-cause mortality among patients with type 2 diabetes mellitus (T2DM).

METHODS,

We used data from the diabetes surveillance system of Yinzhou Health Information System with T2DM patients registered from 2010 to 2015. Participants aged ≥ 40 years were included and were followed up until September 30, 2021. The latent class growth mixture model was used to identify different changing patterns in BMI for 5 years from registration. Cox proportional hazards models were used to examine the associations of baseline BMI and 5-year BMI trajectories with all-cause mortality.

RESULTS

We observed a nonlinear association between baseline BMI and all-cause mortality (P for nonlinearity < 0.001), with an increased risk of death for low but not high BMI. However, compared with participants with medium-stable BMI for 5 years from baseline, individuals with increasing BMI had higher mortality, with adjusted hazard ratios (95% confidence intervals) 1.21 (1.02;1.43) for early-increasing and 1.47 (1.19;1.80) for late-sharp increasing groups.

CONCLUSION

These findings suggest that while obesity itself may not be associated with an increased risk for mortality, weight gain, and in particular rapid weight gain, is a risk factor for mortality among patients with T2DM.

Prediction of all-cause and cardiovascular mortality using ankle-brachial index and brachial-ankle pulse wave velocity in patients with type 2 diabetes

Ankle-brachial index (ABI) and brachial-ankle pulse wave velocity (baPWV) are used as non-invasive indicators for detecting atherosclerosis and arterial stiffness, two well-known predictors of mortality in patients with type 2 diabetes mellitus (T2DM). ABI and baPWV have independent associations with mortality; however, their joint and interactive effects on mortality have not been assessed in patients with T2DM. This work aims to evaluate the independent, joint, and interactive associations of ABI and baPWV with all-cause and expanded cardiovascular disease (CVD) mortality in patients with T2DM. This observational study included 2160 patients with T2DM enlisted in the Diabetes Care Management Program database of China Medical University Hospital from 2001 to 2016 and then followed their death status until August 2021. Cox proportional hazard models were used to evaluate the independent, joint, and interactive effects of ABI and baPWV on the risk of all-cause and expanded CVD mortality. A total of 474 patient deaths occurred after a mean follow-up of 8.4 years, and 268 of which were attributed to cardiovascular events. Abnormal ABI (≤ 0.9) and highest baPWV quartile were independently associated with increased risks of all-cause [ABI: hazard ratio (HR) 1.67, 95% confidence interval (CI) 1.30–2.11; baPWV: 1.63, 1.16–2.27] and expanded CVD mortality (ABI: 2.21, 1.62–3.02; baPWV: 1.75, 1.09–2.83). The combination of abnormal ABI (≤ 0.9) and highest baPWV quartile was associated with a significantly higher risk of all-cause (4.51, 2.50–8.11) and expanded CVD mortality (9.74, 4.21–22.51) compared with that of the combination of normal ABI and lowest baPWV quartile. Significant interactions were observed between ABI and baPWV in relation to all-cause and expand CVD mortality (both p for interaction < 0.001). Through their independent, joint, and interactive effects, ABI and baPWV are significant parameters that can improve the prediction of all-cause and expanded CVD mortality in patients with T2DM and help identify high-risk patients who may benefit from diabetes care.