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Insulin receptor autophosphorylation was studied using CHO cells overexpressing either IR-A or IR-B. For detection an immunocytochemical microplate assay (In-Cell Western) based on two-colour fluorescence was applied [31] . The observed EC 50 values for stimulation of autophosphorylation of the IR isoforms with human insulin, insulin glargine, its metabolites IM, M1 and M2 and IGF-1 and IGF-2 correlated well with their binding affinities to the corresponding receptors ( Tab. 1 , Fig. 2 C and D ). For human insulin, insulin glargine and its metabolites the EC 50 values were comparable for IR-A and IR-B with insulin glargine and its metabolites exhibiting a 30–50% decrease in activity towards both isoforms relative to human insulin. IGF-1 and more potently IGF-2 were capable in stimulating the autophosphorylation of IR-A. In contrast, stimulation of IR-B autophosphorylation was less pronounced with IGF-1 (EC 50 value >400 nmol/L) and IGF-2 (EC 50 value = 384±68 nmol/L). The most potent stimulant for both IR-A and IR-B autophosphorylation was [Asp B10 ]insulin with comparable EC 50 values, which did not reflect the differences observed in the IR isoform affinities [12] .

For determination of the metabolic activity primary rat adipocytes were used that possess a high insulin sensitivity and responsiveness. They reflect the physiological situation of insulin target cells with regard to signal transduction processes and regulation of glucose and lipid metabolism (e.g. glucose uptake and esterification into lipids) more closely than cultured muscle cells and adipocyte cell lines. mRNA expression of IR-B is significantly higher than that of IR-A in rat epididymal white adipose tissue from which primary adipocytes are derived [23] . By using fully differentiated and maximally insulin-sensitive adipocytes from young rats, any difference in metabolic activity between human insulin, insulin glargine and glargine metabolites is due to differential interaction with the IR-B rather than the IR-A.

Assaying lipid synthesis with primary rat adipocytes for the measurement of metabolic activity is based on the incorporation of radiolabelled glucose into total lipids. This pathway is under the strict control of insulin at the low glucose concentrations used.

As expected on the basis of previously reported data on the stimulation of lipid synthesis in rat adipocytes [12] , [Asp B10 ]insulin and IGF-1 exerted the highest and lowest metabolic activities, respectively, among all proteins tested, with EC 50 value of 0.2-fold lower and 422-fold higher, respectively, relative to insulin. Furthermore, insulin glargine and M1 were reported to exhibit 60% and 88%, respectively, of the metabolic activity of human insulin [12] . These findings were confirmed in the present study as reflected in the considerable right-ward shifts of the concentration-response curves for insulin glargine and M1 vs . human insulin resulting in 1.4- and 3.0-fold higher EC 50 values, respectively ( Tab. 1 , Fig. 3 ). The glargine metabolites M2 and IM, here investigated for biological activity for the first time, were also less active (1.2- and 1.4-fold) than insulin glargine but more active than M1 (0.6-fold lower EC 50 values). The maximal responses in stimulating lipid synthesis are the same for human insulin, insulin glargine and its metabolites M1, M2 and IM (15- to 20-fold above basal, depending on the batch of adipocytes used). GitHub Enterprise 2.13 GitHub Enterprise 2.12 GitHub Enterprise 2.11
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Fenway Health, a free-standing CHC, was founded in 1971. In its early response to the AIDS epidemic, Fenway Health developed the capacity to support clinical research and has received significant federal funding [ 20 ]. Fenway has been a partner with OCHIN since 2010 [ 21 ]. Fenway has received national recognition for reducing healthcare disparities for sexual and gender minority populations, and is the home of the National Center for Lesbian, Gay, Bisexual and Transgender Health Education [ 22 ]. Fenway Health has had an EHR for >15years and has participated in several national research consortia using EHR-based data.

We will also link OCHIN EHR data to Oregon Medicaid claims data in order to measure changes in Medicaid expenditures. Oregon’s Medicaid recipients are assigned unique individual identification (ID) numbers, facilitating data linkages across multiple databases, including the ADVANCE data warehouse. As we have done previously [ 23 , 24 , 25 ], we will use claims data from Oregon’s Medicaid Management Information System, recognized for exemplary data validation protocols by Centers for Medicare and Medicaid Services.

We will include patients with DM risk, pre-DM, or diagnosed DM, between the ages of 19 and 64, with one or more ambulatory visit. Data will derive from >700 CHCs in 20 states for which their EHR were ‘live’ as of 1/1/2013. We set these age criteria because the Medicaid expansion was aimed at adults aged 19 and older, many states’ Medicaid programs cover children through age 18, and nationally, individuals aged 65 and older are eligible for Medicare. We will exclude pregnant women to eliminate the possibility of having patients with gestational DM in the dataset.

Patients at-risk for DM :

Patients aged 45 and older and with a BMI ≥ 25, following criteria from the Centers for Disease Control and Prevention [ 26 ].

Patients with pre-DM :

Patients with a single HbA1c between 5.7 and 6.4% and/or a fasting glucose between 100 and125 mg/deciliter.

Patients with DM :

At least two visits with a DM-related International Classification of Disease (ICD)-9 or 10 code,

One ICD-9/10-coded visit and one HbA1c or glucose test positive for DM, according to American Diabetes Association thresholds [ 30 ],

One ICD-9/10 coded visit and a diabetes-related medication order, or

A diabetes-related medication order and a positive HbA1c or glucose test.

Table 1

ADVANCE patients (aged 19–64) with DM risk, pre-DM, or DM during the pre-period (01/01/2012-12/31/2013) by expansion status

States who expanded Medicaid as of January 1, 2014 (CA, HI, MA, MD, MN, NM, NV, OH, OR, RI, and WA)

States who had not expanded Medicaid as of January 1, 2014 (AK, IN, FL, KS, MO, MT, NC, TX, WI)

DM categories defined prior to January1, 2014

This project has two main independent variables: Medicaid expansion status (states that expanded versus not) or insurance status.

Study population

The Valencia Community is a Mediterranean region located on the east coast of Spain, with a total population of 4.980.689 according to the 2015 census. The cohort was recruited from a sample of patients receiving healthcare by the Valencia Health System. Every user of this system has a unique patient identifier, corresponding to a centralized, individual electronic clinical record. The unique patient identifier allows linkage between relevant clinical databases where various variables were collected. Detailed information about the sample size recruitment has been published elsewhere [ 11 ].

Briefly, we included 73,302 participants of both sexes, aged 30 years or older with a diagnosis of hypertension, diabetes mellitus, and/or dyslipidemia, with no previous cardiovascular events who attended a primary healthcare center for routine health services. Of the total population, there was missing data on body weight for 12,209 participants, on serum creatinine for 5,175 participants, and on other variables of interest for 4,456 participants. After excluding these participants, our final sample size included 51,462 participants. Information was collected from ABUCASIS, which is the electronic health record (EHR) that registers patient data in the Valencia region.

Baseline data collection

Data on age, sex, smoking and medication for treating hypertension, diabetes, and hypercholesterolemia was collected from the EHR. Blood pressure was measured up to three times on the same day in the sitting position following the European guidelines on CVD prevention in clinical practice [ adidas Ultraboost Mid Solar / HiRes Blue/ Ftw White rfeSEi9
]. Hypertension was defined as a mean systolic blood pressure ≥140 mm Hg, a mean diastolic blood pressure ≥90 mm Hg, a recorded physician diagnosis, or medication use. Diabetes was defined as a non-fasting glucose level of ≥200 mg/dl, a recorded physician diagnosis, medication use, or an HbA1c ≥ 6.5%. TC was measured enzymatically using the Cholesterol High Performance reagent (Roche Diagnostics). HDL-C was measured using a direct HDL reagent (Roche Diagnostics). LDL-C was calculated using the Friedwald formula [ 13 ]. High cholesterol was defined as a serum total cholesterol >200 mg/dL, recorded diagnosis or medication use. Non-HDL cholesterol was measured according to the difference between TC and HDL-C. Triglycerides were measured using Hitachi 704 Analyzer which is serviced by Roche Diagnostics (formerly Boehringer-Mannheim Diagnostics), Indianapolis. Also non-HDL minus LDL-cholesterol was calculated. Body mass index (BMI) was calculated by dividing measured weight in kilograms by height in squared meters and obesity was defined as a BMI ≥30 kg/m.

Mortality and hospitalization follow-up

The follow up period was from January 2008 to December 2012. Participants were followed up until the first episode of hospitalization for CHD or stroke or for death. Data on all-cause mortality was collected. At the time of inclusion, information about cardiovascular risk factors and their active treatments as well as smoking habit and biochemistry lab values were collected from the EHR. Mortality data were obtained from death certificates registered in the Spanish National Death Index. The cause of hospitalization was determined by the codes assigned according to the International Classification of Diseases, 10 Revision (ICD-10). Cause-specific hospitalization was defined as the first in-hospital admission for CHD (ICD codes 410–414) or stroke (ICD codes 430–438, 444). Cardiovascular hospitalizations or mortality during follow-up were assessed by annual mortality and morbidity surveillance reviews of hospitalization and death records. Follow-up data was available for 99.8% of subjects for mortality and for 99.2% of subjects for morbid events. Time to first event was calculated as the difference between the date of the baseline examination and the date of the hospital admission, date of death or 31 December 2012, whichever occurred first.

The study was conducted according to the standards of the International Guidelines for Ethical Review of Epidemiological Studies (Council for International Organizations of Medical Sciences-CIOMS-Geneva, 1991). The ESCARVAL-RISK study [ 11 ] was reviewed and approved by the Valencia Committee for Ethics and Clinical Trials of the Center for Public Health Research (DGSP-CSISP). Patient data collected from the ABUCASIS EHR during the study were anonymized, making it impossible to use the information to identify the patients. The data generated during the study were handled according the Spanish Law 5/1999 and corresponding regulations. All of the researchers with access to study data were required to sign a document guaranteeing confidentiality. No informed consent from patients was required.

Statistical analysis

Age-adjusted rates for mortality and cardiovascular hospitalization end-points were estimated using Poisson regression for individual data with over-dispersion correction. Multi-adjusted rate differences were estimated from semi-parametric Aalen additive hazard models. Statistical models were adjusted for age (continuous-modelled as restricted cubic splines with five knots), sex (male, female), BMI (continuous), hypertension (no, yes), hypertension medication (no, yes), diabetes (no, yes), diabetes medication (no, yes), smoking status (never, former, current), high LDL-C (<130 mg/dL, ≥130 mg/dL), low HDL-C (≤40 mg/dL for men; ≤50 mg/dL for women) and use of cholesterol-lowering medication (no, yes). Adjusted population attributable risks (PARs) for dichotomous lipid biomarkers were calculated by using the standard formula PAR = 1 – ΣΣ p / RR [ 14 ]. In this formula, the subscript denotes one of two categories of the lipid biomarkers (with each participant classified according to the presence of the corresponding biomarker being used to calculate the PAR), the subscript is an index for all strata obtained after cross-classifying the study sample for all adjusted covariates, p is the proportion of total cases in the study population in each stratum after cross-classifying the dichotomous biomarker category and all adjusted covariates, and RR is the adjusted hazard ratio for the endpoint of interest comparing participants with and without the biomarker in stratum of covariates, from Cox proportional hazards regression. Adjusted PARs represent the estimated fraction of deaths that would be avoided in the population, had participants above a given cut-off of the biomarker been below it, assuming that the effects are causal and that other risk factors remain unchanged. We created 55,000 bootstrap samples to obtain the standard errors and 95% confidence intervals for PAR.

A total of 51,462 patients with at least one cardiovascular risk factor were included in the study. The main characteristics of the study population, grouped by the study endpoints, are shown in Table 1 . Hypertension was present in 79% and diabetes in 37% of the participants. Thirty percent were receiving lipid-lowering drug treatment. During an average follow-up of 3.2 years, the EHR recorded 919 deaths (80,705.3 person-years at risk) 1666 hospitalizations for CHD (78,643.85 person-years at risk) and 1510 stroke hospitalizations (79,130.76 person-years at risk). Age-adjusted rates (deaths/10,000 person-years) of CVD and mortality endpoints by CVD risk factors per quartiles of each lipid parameter are described in Table 2 .

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