Predictors of Insulin Initiation in Patients with Type 2 Diabetes: An Analysis of the Look AHEAD Randomized Trial

An omics-based machine learning approach to predict diabetes progression: a RHAPSODY study

AIMS/HYPOTHESIS

People with type 2 diabetes are heterogeneous in their disease trajectory, with some progressing more quickly to insulin initiation than others. Although classical biomarkers such as age, HbA1c and diabetes duration are associated with glycaemic progression, it is unclear how well such variables predict insulin initiation or requirement and whether newly identified markers have added predictive value.

METHODS

In two prospective cohort studies as part of IMI-RHAPSODY, we investigated whether clinical variables and three types of molecular markers (metabolites, lipids, proteins) can predict time to insulin requirement using different machine learning approaches (lasso, ridge, GRridge, random forest). Clinical variables included age, sex, HbA1c, HDL-cholesterol and C-peptide. Models were run with unpenalised clinical variables (i.e. always included in the model without weights) or penalised clinical variables, or without clinical variables. Model development was performed in one cohort and the model was applied in a second cohort. Model performance was evaluated using Harrel's C statistic.

RESULTS

Of the 585 individuals from the Hoorn Diabetes Care System (DCS) cohort, 69 required insulin during follow-up (1.0-11.4 years); of the 571 individuals in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) cohort, 175 required insulin during follow-up (0.3-11.8 years). Overall, the clinical variables and proteins were selected in the different models most often, followed by the metabolites. The most frequently selected clinical variables were HbA1c (18 of the 36 models, 50%), age (15 models, 41.2%) and C-peptide (15 models, 41.2%). Base models (age, sex, BMI, HbA1c) including only clinical variables performed moderately in both the DCS discovery cohort (C statistic 0.71 [95% CI 0.64, 0.79]) and the GoDARTS replication cohort (C 0.71 [95% CI 0.69, 0.75]). A more extensive model including HDL-cholesterol and C-peptide performed better in both cohorts (DCS, C 0.74 [95% CI 0.67, 0.81]; GoDARTS, C 0.73 [95% CI 0.69, 0.77]). Two proteins, lactadherin and proto-oncogene tyrosine-protein kinase receptor, were most consistently selected and slightly improved model performance.

CONCLUSIONS/INTERPRETATION

Using machine learning approaches, we show that insulin requirement risk can be modestly well predicted by predominantly clinical variables. Inclusion of molecular markers improves the prognostic performance beyond that of clinical variables by up to 5%. Such prognostic models could be useful for identifying people with diabetes at high risk of progressing quickly to treatment intensification.

DATA AVAILABILITY

Summary statistics of lipidomic, proteomic and metabolomic data are available from a Shiny dashboard at https://rhapdata-app.vital-it.ch .

Diabetes, gaps in care coordination, and preventable adverse events

OBJECTIVES

To compare the frequency of self-reported gaps in care coordination and self-reported preventable adverse events among adults with vs without diabetes.

STUDY DESIGN

Cross-sectional analysis of REasons for Geographic And Racial Differences in Stroke (REGARDS) study participants 65 years and older who completed a survey on health care experiences in 2017-2018 (N = 5634).

METHODS

We analyzed the association of diabetes with self-reported gaps in care coordination and with preventable adverse events. Gaps in care coordination were assessed using 8 validated questions. Four self-reported adverse events were studied (drug-drug interactions, repeat medical tests, emergency department visits, and hospitalizations). Respondents were asked if they thought these events could have been prevented with better communication among providers.

RESULTS

Overall, 1724 (30.6%) participants had diabetes. Among participants with and without diabetes, 39.3% and 40.7%, respectively, reported any gap in care coordination. The adjusted prevalence ratio (aPR) for any gap in care coordination for participants with vs without diabetes was 0.97 (95% CI, 0.89-1.06). Any preventable adverse event was reported by 12.9% and 8.7% of participants with and without diabetes, respectively. The aPR for any preventable adverse event for participants with vs without diabetes was 1.22 (95% CI, 1.00-1.49). Among participants with and without diabetes, the aPRs for any preventable adverse event associated with any gap in care coordination were 1.53 (95% CI, 1.15-2.04) and 1.50 (95% CI, 1.21-1.88), respectively (P comparing aPRs = .922).

CONCLUSIONS

Interventions to improve quality of care for patients with diabetes could incorporate patient-reported gaps in care coordination to aid in preventing adverse events.

Precision Medicine in Diabetes

Tailoring treatment or management to groups of individuals based on specific clinical, molecular, and genomic features is the concept of precision medicine. Diabetes is highly heterogenous with respect to clinical manifestations, disease progression, development of complications, and drug response. The current practice for drug treatment is largely based on evidence from clinical trials that report average effects. However, around half of patients with type 2 diabetes do not achieve glycaemic targets despite having a high level of adherence and there are substantial differences in the incidence of adverse outcomes. Therefore, there is a need to identify predictive markers that can inform differential drug responses at the point of prescribing. Recent advances in molecular genetics and increased availability of real-world and randomised trial data have started to increase our understanding of disease heterogeneity and its impact on potential treatments for specific groups. Leveraging information from simple clinical features (age, sex, BMI, ethnicity, and co-prescribed medications) and genomic markers has a potential to identify sub-groups who are likely to benefit from a given drug with minimal adverse effects. In this chapter, we will discuss the state of current evidence in the discovery of clinical and genetic markers that have the potential to optimise drug treatment in type 2 diabetes.

Patterns of drug therapy, glycemic control, and predictors of escalation - non-escalation of treatment among diabetes outpatients at a tertiary care center

OBJECTIVES

The study was conducted to assess patterns of prescribed drug therapy and clinical predictors of need for therapy escalation in outpatients with diabetes mellitus (DM).

METHODS

This was a prospective cohort study, conducted at an apex tertiary care teaching hospital in central India for a period of 18 months. The demographic, clinical, and treatment details on the baseline and follow up visits were collected from the patients' prescription charts. Glycemic control, adherence, pill burdens along with pattern of antidiabetic therapy escalation, and deescalations were analyzed.

RESULTS

A total of 1,711 prescriptions of 925 patients of diabetes with a mean age of 53.81 ± 10.42 years and duration of disease of 9.15 ± 6.3 years were analyzed. Approximately half of the patients (n=450) came for ≥1 follow up visits. Hypertension (59.35%) was the most common comorbidity followed by dyslipidemia and hypothyroidism. The mean total daily drugs and pills per prescription were 4.03 ± 1.71 and 4.17 ± 1.38, respectively. Metformin (30.42%) followed by sulphonylureas (SUs) (21.39%) constituted majority of the AHA's and dual and triple drug therapy regimens were most commonly prescribed. There were improvements in HbA_1c, fasting/postprandial/random blood sugar (FBS/PPBS/RBS) as well as adherence to medication, diet, and exercise in the follow up visits. Among patients with follow ups, therapy escalations were found in 31.11% patients, among whom dose was increased in 12.44% and drug was added in 17.28%. Apart from Hb1Ac, FBS, and PPBS levels (p<0.001), characteristics such as age, BMI, duration of diagnosed diabetes, presence of hypertension and dyslipidemia, and daily pill burdens were found to be significantly higher in the therapy escalation group (p<0.05). Inadequate medication adherence increased the relative risk (RR) of therapy escalation by almost two times.

CONCLUSIONS

Disease and therapy patterns are reflective of diabetes care as expected at a tertiary care center. Higher BMI, age, pill burden, duration of diabetes, presence of comorbidities, and poor medication adherence may be the predictors of therapy escalation independent of glycemic control and such patients should be more closely monitored.

Patterns and preferences of antidiabetic drug use in Turkish patients with type 2 diabetes - A nationwide cross-sectional study (TEMD treatment study)

AIMS

The treatment preferences in type 2 diabetes (T2DM) are affected by multiple factors. This survey aims to find out the profiles of the utilization of antidiabetics and their determinants.

METHODS

The nationwide, multicenter TEMD survey consecutively enrolled patients with T2DM (n = 4678). Medications including oral antidiabetics (OAD) and injectable regimens were recorded. Multiple injectable regimens with or without OADs were defined as complex treatments.

RESULTS

A total of 4678 patients with T2DM (mean age: 58.5 ± 10.4 years, 59% female) were enrolled. More than half of patients (n = 2372; 50.7%) were using injectable regimens with or without OADs, and others (n = 2306, 49.3%) were using only OADs. The most common OADs were metformin (93.5%), secretagogues (40.1%), and DPP-4 inhibitors (37.2%). The rates of the use of basal, basal-bolus and premix insulin were 26.5%, 39.5% and 22.4%, respectively. Patients using OADs achieved better glycemia, blood pressure and weight control (p < 0.001 for all) but poorer LDL-C control (p < 0.001). The independent associates of complex treatments were diabetes duration, obesity, eGFR, glycated haemoglobin, macro and microvascular complications, education level, and self-reported hypoglycemia.

CONCLUSION

This study is the first nationwide report to show that almost half of the patients with T2DM are using injectable regimens in Turkey.

The impact of phenotype, ethnicity and genotype on progression of type 2 diabetes mellitus

AIM

To conduct a comprehensive review of studies of glycaemic deterioration in type 2 diabetes and identify the major factors influencing progression.

METHODS

We conducted a systematic literature search with terms linked to type 2 diabetes progression. All the included studies were summarized based upon the factors associated with diabetes progression and how the diabetes progression was defined.

RESULTS

Our search yielded 2785 articles; based on title, abstract and full-text review, we included 61 studies in the review. We identified seven criteria for diabetes progression: 'Initiation of insulin', 'Initiation of oral antidiabetic drug', 'treatment intensification', 'antidiabetic therapy failure', 'glycaemic deterioration', 'decline in beta-cell function' and 'change in insulin dose'. The determinants of diabetes progression were grouped into phenotypic, ethnicity and genotypic factors. Younger age, poorer glycaemia and higher body mass index at diabetes diagnosis were the main phenotypic factors associated with rapid progression. The effect of genotypic factors on progression was assessed using polygenic risk scores (PRS); a PRS constructed from the genetic variants linked to insulin resistance was associated with rapid glycaemic deterioration. The evidence of impact of ethnicity on progression was inconclusive due to the small number of multi-ethnic studies.

CONCLUSION

We have identified the major determinants of diabetes progression-younger age, higher BMI, higher HbA1c and genetic insulin resistance. The impact of ethnicity is uncertain; there is a clear need for more large-scale studies of diabetes progression in different ethnic groups.

Time to Insulin Initiation in Type 2 Diabetes Patients in 2010/2011 and 2016/2017 in Germany

BACKGROUND

The aim of the current study was to determine whether the time to insulin therapy initiation in patients with type 2 diabetes in primary care in Germany has changed in recent years.

METHODS

Longitudinal data from general practices in Germany (Disease Analyzer) were analyzed. These data included information of 7128 patients (age: 68.5 [SD: 11.5] years; 54.4% male) receiving incident insulin therapy in 2010/2011 and 8216 patients (age: 69.1 [SD: 11.9] years; 54.9% male) receiving incident insulin therapy in 2016/2017. Changes in time to insulin initiation in the practices and the last HbA1c value before the start of insulin therapy were analyzed, stratified by index date. To analyze the impact of covariables on the time to insulin initiation, a multivariate regression analysis was performed, adjusted for age, sex, diabetologist care, and HbA1c as independent variables.

RESULTS

Median time from T2D diagnosis to insulin therapy in the Disease Analyzer database increased from 1717 days in 2010/2011 to 1917 days in 2016/2017 (P < .001). The proportion of patients with a HbA1c value ≥9% before insulin initiation was high in both groups (2010/2011: 33.0%, 2016/2017: 34.2%, P = .347). The time to insulin initiation in DPP-4i patients was 112 days longer, and in SGLT2 patients 346 days longer than in patients treated with sulfonylurea.

CONCLUSIONS

The present analysis confirms an increasing delay of the insulin therapy initiation as a consequence of the more frequent use of newer oral antidiabetics. However, the rather moderate increase of time to insulin might display insufficient long-term glycemic control using these agents. Still, more than one-third of patients receive insulin only when HbA1c levels exceed 9%.

Differences in National Diabetes Treatment Patterns and Trends between Older and Younger Adults

BACKGROUND/OBJECTIVES

The treatment of type 2 diabetes in older adults requires special considerations including avoidance of hypoglycemia, yet variation in diabetes treatment with aging is not well understood. In this study, we compared nationally representative diabetes treatment patterns and trends between older adults (≥65 y) and younger adults (30-64 y).

DESIGN

Repeated cross-sectional physician surveys from 2006 to 2015.

SETTING

The National Ambulatory Medical Care Survey, an annual probability sample of visits to office-based US physicians.

PARTICIPANTS

Adults with type 2 diabetes using one or more diabetes medications.

MEASUREMENTS

Proportions of visits in which patients treated with each diabetes medication class were compared between older and younger adults in 2-year intervals.

RESULTS

From 2006 to 2015, the average number of yearly visits for older and younger adults was 25.4 million and 24.2 million, respectively. In 2014-2015, visits for older compared with younger adults involved less use of metformin (56.0% vs 70.0%; p < .001) and glucagon-like peptide 1 receptor agonists (2.9% vs 6.2%; p = .004), and more use of long-acting insulin (30.2% vs 22.4%; p = .017); other classes were used similarly. During the study period, long-acting insulin use increased markedly in older adults, particularly between 2010 and 2015 where it rose from 12.5% to 30.2% of visits (P-trend <.001). In younger adult visits, long-acting insulin use increased modestly (17.2% to 22.4%) and at a slower rate compared with older adult visits (p < .001).

CONCLUSION

The ambulatory treatment of type 2 diabetes differs between older and younger adults, with the treatment of older adults characterized by low use of newer diabetes medications and a greater and rapidly increasing use of long-acting insulin. These findings call for further research clarifying the comparative effectiveness and safety of newer diabetes medications and long-acting insulin to optimize diabetes care for older patients. J Am Geriatr Soc 67:1066-1073, 2019.