Association Between Cardiovascular Health Status and Healthcare Utilization in a Large Integrated Healthcare System.

Introduction: The American Heart Association Life's Simple 7 schema can be used to categorize patients' cardiovascular health status as poor, intermediate, or ideal on the basis of smoking, BMI, physical activity, dietary patterns, blood pressure, cholesterol, and fasting blood sugar. This study examined the association between cardiovascular health status and subsequent healthcare utilization. Methods: This was an observational cohort study of adults from an integrated healthcare delivery system-Kaiser Permanente Northern California-that had outpatient care between 2013 and 2014. Patients were categorized by American Heart Association cardiovascular health status: poor, intermediate, or ideal. Individual-level healthcare utilization and costs in 2015 were accumulated for each patient and compared across the 3 cardiovascular health categories and stratified by age groups. Results: A total of 991,698 patients were included in the study. A total of 194,003 (19.6%) were aged 18-39 years; 554,129 (55.9%) were aged 40-64 years; and 243,566 (24.6%) were aged ≥65 years. A total of 259,931 (26.2%) had ideal cardiovascular health; 521,580 (52.6%) had intermediate cardiovascular health; and 210,187 (21.2%) had poor cardiovascular health. Healthcare utilization measured by average relative cost per patient increased monotonically across age categories (p<0.001). In addition, cardiovascular health category was inversely associated with lower cost in each age group (p<0.001). Conclusions: Adults who were younger and had more ideal cardiovascular health had relatively lower healthcare costs across age groups. Interventions to promote better cardiovascular health may improve patient outcomes and reduce overall healthcare expenditures.

Willingness to take less medication for type 2 diabetes among older patients: The Diabetes & Aging Study.

BACKGROUND: To examine the willingness of older patients to take less diabetes medication (de-intensify) and to identify characteristics associated with willingness to de-intensify treatment. METHODS: Survey conducted in 2019 in an age-stratified, random sample of older (65-100 years) adults with diabetes on glucose-lowering medications in the Kaiser Permanente Northern California Diabetes Registry. We classified survey responses to the question: "I would be willing to take less medication for my diabetes" as willing, neutral, or unwilling to de-intensify. Willingness to de-intensify treatment was examined by several clinical characteristics, including American Diabetes Association (ADA) health status categories used for individualizing glycemic targets. Analyses were weighted to account for over-sampling of older individuals. RESULTS: A total of 1337 older adults on glucose-lowering medication(s) were included (age 74.2 ± 6.0 years, 44% female, 54.4% non-Hispanic white). The proportions of participants willing, neutral, or unwilling to take less medication were 51.2%, 27.3%, and 21.5%, respectively. Proportions of willing to take less medication varied by age (65-74 years: 54.2% vs. 85+ years: 38.5%) and duration of diabetes (0-4 years: 61.0% vs. 15+ years: 44.2%), both p < 0.001. Patients on 1-2 medications were more willing to take less medication(s) compared with patients on 10+ medications (62.1% vs. 46.6%, p = 0.03). Similar proportions of willingness to take less medications were seen across ADA health status, and HbA1c. Willingness to take less medication(s) was similar across survey responses to questions about patient-clinician relationships. CONCLUSIONS: Clinical guidelines suggest considering treatment de-intensification in older patients with longer duration of diabetes, yet patients with these characteristics are less likely to be willing to take less medication(s).

Continuous Glucose Monitor Metrics Are Associated with Emergency Department Visits and Hospitalizations for Hypoglycemia and Hyperglycemia, but Have Low Predictive Value.

Objective: Determine whether continuous glucose monitor (CGM) metrics can provide actionable advance warning of an emergency department (ED) visit or hospitalization for hypoglycemic or hyperglycemic (dysglycemic) events. Research Design and Methods: Two nested case-control studies were conducted among insulin-treated diabetes patients at Kaiser Permanente, who shared their CGM data with their providers. Cases included dysglycemic events identified from ED and hospital records (2016-2021). Controls were selected using incidence density sampling. Multiple CGM metrics were calculated among patients using CGM >70% of the time, using CGM data from two lookback periods (0-7 and 8-14 days) before each event. Generalized estimating equations were specified to estimate odds ratios and C-statistics. Results: Among 3626 CGM users, 108 patients had 154 hypoglycemic events and 165 patients had 335 hyperglycemic events. Approximately 25% of patients had no CGM data during either lookback; these patients had >2 × the odds of a hypoglycemic event and 3-4 × the odds of a hyperglycemic event. While several metrics were strongly associated with a dysglycemic event, none had good discrimination. Conclusion: Several CGM metrics were strongly associated with risk of dysglycemic events, and these can be used to identify higher risk patients. Also, patients who are not using their CGM device may be at elevated risk of adverse outcomes. However, no CGM metric or absence of CGM data had adequate discrimination to reliably provide actionable advance warning of an event and thus justify a rapid intervention.

Severe hypoglycemia and falls in older adults with diabetes: The Diabetes & Aging Study.

Objective: To estimate rates of severe hypoglycemia and falls among older adults with diabetes and evaluate their association. Research Design and Methods: Survey in an age-stratified, random sample adults with diabetes age 65-100 years; respondents were asked about severe hypoglycemia (requiring assistance) and falls in the past 12 months. Prevalence ratios (adjusted for age, sex, race/ethnicity) estimated the increased risk of falls associated with severe hypoglycemia. Results: Among 2,158 survey respondents, 79 (3.7%) reported severe hypoglycemia, of whom 68 (86.1%) had no ED visit or hospitalization for hypoglycemia. Falls were reported by 847 (39.2%), of whom 745 (88.0%) had no fall documented in outpatient or inpatient records. Severe hypoglycemia was associated with a 70% greater prevalence of falls (adjusted prevalence ratio = 1.7 (95% CI, 1.3-2.2)). Conclusion: While clinical documentation of events likely reflects severity or care-seeking behavior, severe hypoglycemia and falls are common, under-reported life-threatening events.

Racial and Ethnic Differences in the Association Between Mean Glucose and Hemoglobin A1c.

Background: Studies have reported significantly higher hemoglobin A1c (A1C) in African American patients than in White patients with the same mean glucose, but less is known about other racial/ethnic groups. We evaluated racial/ethnic differences in the association between mean glucose, based on continuous glucose monitor (CGM) data, and A1C. Methods: Retrospective study among 1788 patients with diabetes from Kaiser Permanente Northern California (KPNC) who used CGM devices during 2016 to 2021. In this study population, there were 5264 A1C results; mean glucose was calculated from 124,388,901 CGM readings captured during the 90 days before each A1C result. Hierarchical mixed models were specified to estimate racial/ethnic differences in the association between mean glucose and A1C. Results: Mean A1C was 0.33 (95% confidence interval: 0.23-0.44; P < 0.0001) percentage points higher among African American patients relative to White patients for a given mean glucose. A1C results for Asians, Latinos, and multiethnic patients were not significantly different from those of White patients. The slope of the association between mean glucose and A1C did not differ significantly across racial/ethnic groups. Variance for the association between mean glucose and A1C was substantially greater within groups than between racial/ethnic groups (65% vs. 9%, respectively). Conclusions: For African American patients, A1C results may overestimate glycemia and could lead to premature diabetes diagnoses, overtreatment, or invalid assessments of health disparities. However, most of the variability in the mean glucose-A1C association was within racial/ethnic groups. Treatment decisions driven by guideline-based A1C targets should be individualized and supported by direct measurement of glycemia.

Glycemic control and diabetes complications across health status categories in older adults treated with insulin or insulin secretagogues: The Diabetes & Aging Study.

BACKGROUND: For older adults with type 2 diabetes (T2D) treated with insulin or sulfonylureas, Endocrine Society guideline recommends HbA1c between 7% to <7.5% for those in good health, 7.5% to <8% for those in intermediate health, and 8% to <8.5% for those in poor health. Our aim was to examine associations between attained HbA1c below, within (reference), or above recommended target range and risk of complication or mortality. METHODS: Retrospective cohort study of adults ≥65 years old with T2D treated with insulin or sulfonylureas from an integrated healthcare delivery system. Cox proportional hazards models of complications during 2019 were adjusted for sociodemographic and clinical variables. Primary outcome was a combined outcome of any microvascular or macrovascular event, severe hypoglycemia, or mortality during 12-month follow-up. RESULTS: Among 63,429 patients (mean age: 74.2 years, 46.8% women), 8773 (13.8%) experienced a complication. Complication risk was significantly elevated for patients in good health (n = 16,895) whose HbA1c was above (HR 1.97, 95% CI 1.62-2.41) or below (HR 1.29, 95% CI 1.02-1.63) compared to within recommended range. Among those in intermediate health (n = 30,129), complication risk was increased for those whose HbA1c was above (HR 1.45, 95% CI 1.30-1.60) but not those below the recommended range (HR 0.99, 95% CI 0.89-1.09). Among those in poor health (n = 16,405), complication risk was not significantly different for those whose HbA1c was below (HR 0.98, 95% CI 0.89-1.09) or above (HR 0.96, 95% CI 0.88-1.06) recommended range. CONCLUSIONS: For older adults with T2D in good health, HbA1c below or above the recommended range was associated with significantly elevated complication risk. However, for those in poor health, achieving specific HbA1c levels may not be helpful in reducing the risk of complications.

Development and Validation of the Life Expectancy Estimator for Older Adults with Diabetes (LEAD): the Diabetes and Aging Study.

BACKGROUND: Estimated life expectancy for older patients with diabetes informs decisions about treatment goals, cancer screening, long-term and advanced care, and inclusion in clinical trials. Easily implementable, evidence-based, diabetes-specific approaches for identifying patients with limited life expectancy are needed. OBJECTIVE: Develop and validate an electronic health record (EHR)-based tool to identify older adults with diabetes who have limited life expectancy. DESIGN: Predictive modeling based on survival analysis using Cox-Gompertz models in a retrospective cohort. PARTICIPANTS: Adults with diabetes aged ≥ 65 years from Kaiser Permanente Northern California: a 2015 cohort (N = 121,396) with follow-up through 12/31/2019, randomly split into training (N = 97,085) and test (N = 24,311) sets. Validation was conducted in the test set and two temporally distinct cohorts: a 2010 cohort (n = 89,563; 10-year follow-up through 2019) and a 2019 cohort (n = 152,357; 2-year follow-up through 2020). MAIN MEASURES: Demographics, diagnoses, utilization and procedures, medications, behaviors and vital signs; mortality. KEY RESULTS: In the training set (mean age 75 years; 49% women; 48% racial and ethnic minorities), 23% died during 5 years follow-up. A mortality prediction model was developed using 94 candidate variables, distilled into a life expectancy model with 11 input variables, and transformed into a risk-scoring tool, the Life Expectancy Estimator for Older Adults with Diabetes (LEAD). LEAD discriminated well in the test set (C-statistic = 0.78), 2010 cohort (C-statistic = 0.74), and 2019 cohort (C-statistic = 0.81); comparisons of observed and predicted survival curves indicated good calibration. CONCLUSIONS: LEAD estimates life expectancy in older adults with diabetes based on only 11 patient characteristics widely available in most EHRs and claims data. LEAD is simple and has potential application for shared decision-making, clinical trial inclusion, and resource allocation.

Validation of a Hypoglycemia Risk Stratification Tool Using Data From Continuous Glucose Monitors.

This cohort study uses data from continuous glucose monitoring to validate a hypoglycemia risk stratification tool.

Data-driven classification of health status of older adults with diabetes: The diabetes and aging study.

BACKGROUND: We set out to identify empirically-derived health status classes of older adults with diabetes based on clusters of comorbid conditions which are associated with future complications. METHODS: We conducted a cohort study among 105,786 older (≥65 years of age) adults with type 2 diabetes enrolled in an integrated healthcare delivery system. We used latent class analysis of 19 baseline comorbidities to derive health status classes and then compared incident complication rates (events per 100 person-years) by health status class during 5 years of follow-up. Complications included infections, hyperglycemic events, hypoglycemic events, microvascular events, cardiovascular events, and all-cause mortality. RESULTS: Three health status classes were identified: Class 1 (58% of the cohort) had the lowest prevalence of most baseline comorbidities, Class 2 (22%) had the highest prevalence of obesity, arthritis, and depression, and Class 3 (20%) had the highest prevalence of cardiovascular conditions. The risk for incident complications was highest for Class 3, intermediate for Class 2 and lowest for Class 1. For example, the age, sex and race-adjusted rates for cardiovascular events (per 100 person-years) for Class 3, Class 2 and Class 1 were 6.5, 2.3, and 1.6, respectively; 2.1, 1.2, 0.7 for hypoglycemia; and 8.0, 3.8, and 2.3 for mortality. CONCLUSIONS: Three health status classes of older adults with diabetes were identified based on prevalent comorbidities and were associated with marked differences in risk of complications. These health status classes can inform population health management and guide the individualization of diabetes care.

Development and Validation of a Diabetic Retinopathy Risk Stratification Algorithm.

OBJECTIVE: Although diabetic retinopathy is a leading cause of blindness worldwide, diabetes-related blindness can be prevented through effective screening, detection, and treatment of disease. The study goal was to develop risk stratification algorithms for the onset of retinal complications of diabetes, including proliferative diabetic retinopathy, referable retinopathy, and macular edema. RESEARCH DESIGN AND METHODS: Retrospective cohort analysis of patients from the Kaiser Permanente Northern California Diabetes Registry who had no evidence of diabetic retinopathy at a baseline diabetic retinopathy screening during 2008-2020 was performed. Machine learning and logistic regression prediction models for onset of proliferative diabetic retinopathy, diabetic macular edema, and referable retinopathy detected through routine screening were trained and internally validated. Model performance was assessed using area under the curve (AUC) metrics. RESULTS: The study cohort (N = 276,794) was 51.9% male and 42.1% White. Mean (±SD) age at baseline was 60.0 (±13.1) years. A machine learning XGBoost algorithm was effective in identifying patients who developed proliferative diabetic retinopathy (AUC 0.86; 95% CI, 0.86-0.87), diabetic macular edema (AUC 0.76; 95% CI, 0.75-0.77), and referable retinopathy (AUC 0.78; 95% CI, 0.78-0.79). Similar results were found using a simpler nine-covariate logistic regression model: proliferative diabetic retinopathy (AUC 0.82; 95% CI, 0.80-0.83), diabetic macular edema (AUC 0.73; 95% CI, 0.72-0.74), and referable retinopathy (AUC 0.75; 95% CI, 0.75-0.76). CONCLUSIONS: Relatively simple logistic regression models using nine readily available clinical variables can be used to rank order patients for onset of diabetic eye disease and thereby more efficiently prioritize and target screening for at risk patients.

Continuous Glucose Monitor Use Prevents Glycemic Deterioration in Insulin-Treated Patients with Type 2 Diabetes.

Continuous glucose monitoring (CGM) is indicated in poorly controlled insulin-treated patients with type 2 diabetes (T2D) to improve glycemic control and reduce the risk of hypoglycemia, but the benefits of CGM for lower risk patients have not been well studied. Among 17,422 insulin-treated patients with T2D with hemoglobin A1c (HbA1c) <8% and no recent severe hypoglycemia (based on emergency room visits or hospitalizations), CGM initiation occurred in 149 patients (17,273 noninitiators served as reference). Changes in HbA1c and severe hypoglycemia rates for the 12 months before and after CGM initiation were calculated. CGM initiation was associated with decreased HbA1c (-0.06%), whereas noninitiation was associated with increased HbA1c (+0.32%); a weighted adjusted difference-in-difference model of change in HbA1c yielded a net benefit of -0.30%; 95% CI -0.50%, -0.10%; P = 0.004). No significant differences were observed for severe hypoglycemia. CGM may be useful in preventing glycemic deterioration in well-controlled patients with insulin-treated T2D.

Association of Low-Density Lipoprotein Testing After an Atherosclerotic Cardiovascular Event with Subsequent Statin Adherence and Intensification.

PURPOSE: This study aimed to evaluate associations between outpatient low-density lipoprotein cholesterol (LDL-C) testing and subsequent statin adherence and intensification in patients after an atherosclerotic cardiovascular (ASCVD) event. METHODS: This was a longitudinal study of adult members of Kaiser Permanente Northern California hospitalized with an ASCVD event (myocardial infarction or stroke) during January 01, 2016, to December 31, 2017, with follow-up through December 31, 2019. Outcomes were statin adherence (estimated using continuous medication gap [CMG]) and intensification (defined by an increased dose or switch to a higher-intensity statin) based on pharmacy dispensing. The exposure of interest was first outpatient LDL-C test after an ASCVD event. Baseline for follow-up was LDL-C test date or a date assigned using incidence density sampling. Multivariate logistic regression models were specified to estimate the odds ratios for statin adherence or intensification among those with vs without an LDL-C test, with adjustment for age, sex, race/ethnicity, smoking, hypertension, diabetes, body mass index, and estimated glomerular filtration rate. RESULTS: There were 19,604 adults hospitalized with ASCVD, including 7054 adults not on high-intensity statins. The mean age was 69.5 years and 33.0% were female. Prevalence of good adherence (continuous medication gap ≤20%) was significantly higher (80.2% vs 75.9%; odds ratio 1.38; 95% confidence interval, 1.28-1.49; P <.001) among participants who had an LDL-C test compared with participants who did not. LDL-C testing was associated with significantly higher rates of treatment intensification (16.1% vs 10.7%; odds ratio 1.51; 95% confidence interval,1.29-1.76; P <0.001). CONCLUSIONS: Low-density lipoprotein cholesterol testing is recommended for patients with a history of ASCVD and may be a high-value and low-cost intervention to improve adherence and statin management.

Association of Real-time Continuous Glucose Monitoring With Glycemic Control and Acute Metabolic Events Among Patients With Insulin-Treated Diabetes.

Importance: Continuous glucose monitoring (CGM) is recommended for patients with type 1 diabetes; observational evidence for CGM in patients with insulin-treated type 2 diabetes is lacking. Objective: To estimate clinical outcomes of real-time CGM initiation. Design, Setting, and Participants: Exploratory retrospective cohort study of changes in outcomes associated with real-time CGM initiation, estimated using a difference-in-differences analysis. A total of 41 753 participants with insulin-treated diabetes (5673 type 1; 36 080 type 2) receiving care from a Northern California integrated health care delivery system (2014-2019), being treated with insulin, self-monitoring their blood glucose levels, and having no prior CGM use were included. Exposures: Initiation vs noninitiation of real-time CGM (reference group). Main Outcomes and Measures: Ten end points measured during the 12 months before and 12 months after baseline: hemoglobin A1c (HbA1c); hypoglycemia (emergency department or hospital utilization); hyperglycemia (emergency department or hospital utilization); HbA1c levels lower than 7%, lower than 8%, and higher than 9%; 1 emergency department encounter or more for any reason; 1 hospitalization or more for any reason; and number of outpatient visits and telephone visits. Results: The real-time CGM initiators included 3806 patients (mean age, 42.4 years [SD, 19.9 years]; 51% female; 91% type 1, 9% type 2); the noninitiators included 37 947 patients (mean age, 63.4 years [SD, 13.4 years]; 49% female; 6% type 1, 94% type 2). The prebaseline mean HbA1c was lower among real-time CGM initiators than among noninitiators, but real-time CGM initiators had higher prebaseline rates of hypoglycemia and hyperglycemia. Mean HbA1c declined among real-time CGM initiators from 8.17% to 7.76% and from 8.28% to 8.19% among noninitiators (adjusted difference-in-differences estimate, -0.40%; 95% CI, -0.48% to -0.32%; P < .001). Hypoglycemia rates declined among real-time CGM initiators from 5.1% to 3.0% and increased among noninitiators from 1.9% to 2.3% (difference-in-differences estimate, -2.7%; 95% CI, -4.4% to -1.1%; P = .001). There were also statistically significant differences in the adjusted net changes in the proportion of patients with HbA1c lower than 7% (adjusted difference-in-differences estimate, 9.6%; 95% CI, 7.1% to 12.2%; P < .001), lower than 8% (adjusted difference-in-differences estimate, 13.1%; 95% CI, 10.2% to 16.1%; P < .001), and higher than 9% (adjusted difference-in-differences estimate, -7.1%; 95% CI, -9.5% to -4.6%; P < .001) and in the number of outpatient visits (adjusted difference-in-differences estimate, -0.4; 95% CI, -0.6 to -0.2; P < .001) and telephone visits (adjusted difference-in-differences estimate, 1.1; 95% CI, 0.8 to 1.4; P < .001). Initiation of real-time CGM was not associated with statistically significant changes in rates of hyperglycemia, emergency department visits for any reason, or hospitalizations for any reason. Conclusions and Relevance: In this retrospective cohort study, insulin-treated patients with diabetes selected by physicians for real-time continuous glucose monitoring compared with noninitiators had significant improvements in hemoglobin A1c and reductions in emergency department visits and hospitalizations for hypoglycemia, but no significant change in emergency department visits or hospitalizations for hyperglycemia or for any reason. Because of the observational study design, findings may have been susceptible to selection bias.

Challenges and solutions to employing natural language processing and machine learning to measure patients' health literacy and physician writing complexity: The ECLIPPSE study.

OBJECTIVE: In the National Library of Medicine funded ECLIPPSE Project (Employing Computational Linguistics to Improve Patient-Provider Secure Emails exchange), we attempted to create novel, valid, and scalable measures of both patients' health literacy (HL) and physicians' linguistic complexity by employing natural language processing (NLP) techniques and machine learning (ML). We applied these techniques to > 400,000 patients' and physicians' secure messages (SMs) exchanged via an electronic patient portal, developing and validating an automated patient literacy profile (LP) and physician complexity profile (CP). Herein, we describe the challenges faced and the solutions implemented during this innovative endeavor. MATERIALS AND METHODS: To describe challenges and solutions, we used two data sources: study documents and interviews with study investigators. Over the five years of the project, the team tracked their research process using a combination of Google Docs tools and an online team organization, tracking, and management tool (Asana). In year 5, the team convened a number of times to discuss, categorize, and code primary challenges and solutions. RESULTS: We identified 23 challenges and associated approaches that emerged from three overarching process domains: (1) Data Mining related to the SM corpus; (2) Analyses using NLP indices on the SM corpus; and (3) Interdisciplinary Collaboration. With respect to Data Mining, problems included cleaning SMs to enable analyses, removing hidden caregiver proxies (e.g., other family members) and Spanish language SMs, and culling SMs to ensure that only patients' primary care physicians were included. With respect to Analyses, critical decisions needed to be made as to which computational linguistic indices and ML approaches should be selected; how to enable the NLP-based linguistic indices tools to run smoothly and to extract meaningful data from a large corpus of medical text; and how to best assess content and predictive validities of both the LP and the CP. With respect to the Interdisciplinary Collaboration, because the research required engagement between clinicians, health services researchers, biomedical informaticians, linguists, and cognitive scientists, continual effort was needed to identify and reconcile differences in scientific terminologies and resolve confusion; arrive at common understanding of tasks that needed to be completed and priorities therein; reach compromises regarding what represents "meaningful findings" in health services vs. cognitive science research; and address constraints regarding potential transportability of the final LP and CP to different health care settings. DISCUSSION: Our study represents a process evaluation of an innovative research initiative to harness "big linguistic data" to estimate patient HL and physician linguistic complexity. Any of the challenges we identified, if left unaddressed, would have either rendered impossible the effort to generate LPs and CPs, or invalidated analytic results related to the LPs and CPs. Investigators undertaking similar research in HL or using computational linguistic methods to assess patient-clinician exchange will face similar challenges and may find our solutions helpful when designing and executing their health communications research.

Risk of Cardiovascular Events in Patients With Type 2 Diabetes and Metabolic Dyslipidemia Without Prevalent Atherosclerotic Cardiovascular Disease.

BACKGROUND: The relationship between achieved low-density lipoprotein cholesterol (LDL-C) levels and risk of incident atherosclerotic cardiovascular disease events among patients with diabetes and metabolic dyslipidemia has not been well described. METHODS: We conducted an observational cohort study of statin-treated adults (ages 21-90 years) with type 2 diabetes without established atherosclerotic cardiovascular disease (as of January 1, 2006) who had metabolic dyslipidemia (elevated triglycerides ≥150 mg/dL and low high-density lipoprotein cholesterol, <50 mg/dL [women] and <40 mg/dL [men]). All subjects were members of Kaiser Permanente Northern California, an integrated health care delivery system. Adjusted multivariable Cox models were specified to estimate hazard ratios (HRs) for incident atherosclerotic cardiovascular disease events by achieved LDL-C levels (<50, 50-<70, 70-<100, and ≥100 mg/dL). Incident atherosclerotic cardiovascular disease events were defined as a composite of nonfatal myocardial infarction, ischemic stroke, or coronary heart disease death through December 31, 2013. RESULTS: A total of 19,095 individuals met the selection criteria. Mean age was 63.4 years, 53.5% were women, and the mean follow-up was 5.9 years. Unadjusted rates of atherosclerotic cardiovascular disease events were not significantly different across specified LDL-C categories. In models adjusted for demographics and clinical characteristics, the risk was significantly lower with decreasing achieved LDL-C levels (P <0.0001 for trend). Relative to achieved LDL-C ≥100 mg/dL, LDL-C <50 mg/dL had an hazard ratio of 0.66 (95% confidence interval [CI] 0.52-0.82). CONCLUSION: In a large, contemporary cohort of statin-treated patients with type 2 diabetes and metabolic dyslipidemia without established atherosclerotic cardiovascular disease, lower achieved LDL-C levels were associated with a monotonically lower risk of incident atherosclerotic cardiovascular disease events. The benefits of achieving very-low LDL-C (<50 mg/dL) in this population requires further evaluation in prospective interventional studies.

The Legacy Effect in Type 2 Diabetes: Impact of Early Glycemic Control on Future Complications (The Diabetes & Aging Study).

OBJECTIVE: To examine for a legacy effect of early glycemic control on diabetic complications and death. RESEARCH DESIGN AND METHODS: This cohort study of managed care patients with newly diagnosed type 2 diabetes and 10 years of survival (1997-2013, average follow-up 13.0 years, N = 34,737) examined associations between HbA1c <6.5% (<48 mmol/mol), 6.5% to <7.0% (48 to <53 mmol/mol), 7.0% to <8.0% (53 to <64 mmol/mol), 8.0% to <9.0% (64 to <75 mmol/mol), or ≥9.0% (≥75 mmol/mol) for various periods of early exposure (0-1, 0-2, 0-3, 0-4, 0-5, 0-6, and 0-7 years) and incident future microvascular (end-stage renal disease, advanced eye disease, amputation) and macrovascular (stroke, heart disease/failure, vascular disease) events and death, adjusting for demographics, risk factors, comorbidities, and later HbA1c. RESULTS: Compared with HbA1c <6.5% (<48 mmol/mol) for the 0-to-1-year early exposure period, HbA1c levels ≥6.5% (≥48 mmol/mol) were associated with increased microvascular and macrovascular events (e.g., HbA1c 6.5% to <7.0% [48 to <53 mmol/mol] microvascular: hazard ratio 1.204 [95% CI 1.063-1.365]), and HbA1c levels ≥7.0% (≥53 mmol/mol) were associated with increased mortality (e.g., HbA1c 7.0% to <8.0% [53 to <64 mmol/mol]: 1.290 [1.104-1.507]). Longer periods of exposure to HbA1c levels ≥8.0% (≥64 mmol/mol) were associated with increasing microvascular event and mortality risk. CONCLUSIONS: Among patients with newly diagnosed diabetes and 10 years of survival, HbA1c levels ≥6.5% (≥48 mmol/mol) for the 1st year after diagnosis were associated with worse outcomes. Immediate, intensive treatment for newly diagnosed patients may be necessary to avoid irremediable long-term risk for diabetic complications and mortality.