Intervention

As previously described [9, 10], we utilized a prospective, longitudinal study design with a cohort of patients with T2D from the greater Lafayette, Indiana, USA, region who self-selected to participate in the CCI (Clinicaltrials.gov Identifier NCT02519309). Participants in the CCI were provided access to a web-based software application (app) for biomarker reporting and monitoring including body weight, blood glucose and blood betahydroxybutyrate (BHB; a marker of ketosis). The remote care team consisted of a health coach and physician or nurse practitioner who provided nutritional advice and medication management, respectively. Participants were guided by individualized nutrition recommendations to achieve and sustain nutritional ketosis. Notably, if participants reported headaches, constipation or lightheadedness, the remote care team recommended individualized adjustments to sodium and fluid intake [10]. CCI participants self-selected to receive education via either an onsite group setting (CCI-onsite) or via the app (CCI-web). There were no instructions given to the CCI group on counting or restricting calories. The CCI participants were instructed to restrict carbohydrate, eat protein in moderation, and consume fat to satiety from the start of the study. Due to the well-known systematic errors associated with dietary records in an obese population [38], we chose not to collect diet records. Social support was provided via an online peer community. Inclusion and exclusion criteria were previously described [10]. This study was approved by the Franciscan Health Lafayette Institutional Review Board, and participants provided written informed consent.

The frequency of glucose and BHB monitoring, along with glycemic control medication management, were previously described in detail [9, 10]. Briefly, glucose and BHB levels were initially obtained daily using a blood glucose and ketone meter (Precision Xtra, Abbott; Alameda, CA, USA) to personalize nutrition recommendations and also provide a marker of adherence. The frequency of measurement was modified by the care team for each participant based on individual care needs and preferences. For participants with a history of hypertension, a home automatic sphygmomanometer was supplied. Participants reported their home readings in the app daily to weekly depending on recent control and instruction from the supervising physician. Antihypertensive prescriptions were adjusted based on home readings and reported symptoms. Health coaches responded to patient app reported readings of systolic blood pressure less than 110 mmHg with specific questions about symptoms of hypotension. Following resolution of hypertension, diuretics were the first antihypertensive medications to be discontinued, followed by beta blockers, unless the participant had a history of coronary artery disease. Angiotensin-converting-enzyme inhibitors (ACEs) and angiotensin II receptor blockers (ARBs) were generally continued due to known renal protection with diabetes [39, 40]. Statin medications were adjusted when appropriate to maintain a goal of LDL-P under 1000 nmol L−1 or participant preference after full risk—benefit discussion.

To track T2D progression in the same geography and health system as the CCI, an independent cohort of patients with T2D who received usual care (UC) were recruited. These patients were referred to registered dietitians providing dietary advice according to American Diabetes Association guidelines [41].

Outcome measures

Anthropometrics and vital signs for the CCI group were obtained at baseline, 70 days, and 1 year. A stadiometer was used to assess height and used in the calculation of body mass index. A calibrated scale in the clinic measured weight to the nearest 0.1 lb (Model 750, Detecto; Webb City, MO, USA) and values were converted to kg. Participants were provided a cellular-connected home scale for daily weight. Blood pressure was obtained manually by trained staff after participants rested in a seated position for 5 min. Adverse events were reported and reviewed by the Principal Investigator and the Institutional Review Board.

Fasting blood draws for the CCI group were collected at baseline, 70 days, and 1-year follow-up (ranging from 11 to 15 months). Blood analytes were determined via standard procedures at a Clinical Laboratory Improvement Amendment (CLIA) accredited laboratory on the day of sample collection or from stored serum. Serum aliquots were stored at − 80 °C and thawed for determination of ApoB, ApoA1, total cholesterol, triglycerides, and direct HDL-C concentrations by FDA approved methods (Cobas c501, Roche Diagnostics; Indianapolis, IN, USA). LDL was calculated using the Friedewald equation [42]. Lipid subfractions were quantified using clinical NMR LipoProfile® (LabCorp, Burlington NC, USA; [43]). The LipoProfile3 algorithm used in the present investigation was used previously to relate lipid subfractions to CVD risk [35, 44, 45]. The NMR-derived lipoprotein insulin resistance score (LP-IR) is proposed to be associated with the homeostasis model assessment of insulin resistance (HOMA-IR) and glucose disposal rate (GDR) [46]. The multifactorial 10-year atherosclerotic cardiovascular disease (ASCVD) risk score was also computed [47].

Anthropometrics, vital signs and fasting blood draws for the UC group were obtained at baseline and 1 year as described above using the same clinical facilities and laboratory and data collection methods. Home biometrics for the UC group were not tracked and 70-day outcomes were not measured.

Carotid ultrasonography for cIMT measure was performed at baseline and 1 year in CCI and UC groups to characterize atherosclerotic risk. Ultrasound technicians were trained according to protocols that were previously tested and used to assess subclinical atherosclerosis [48, 49]. The right and left common carotid arteries were imaged 1 cm distal to the carotid bulb using a L12-3 multi-frequency linear-array transducer attached to a high-resolution ultrasound system (Phillips EPIQ 5, Amsterdam, Netherlands). Longitudinal images were captured in three imaging planes: anterior, lateral, and posterior. Digital images were analyzed using edge-detection software (Carotid Analyzer for Research; Medical Imaging Application, Coralville, IA) to trace the lumen-intima and intima-medial boundaries of the artery. Analyses were performed by the same blinded investigator to obtain right and left mean arterial diameter and mean cIMT. The current study was powered to detect a ∆cIMT difference of 0.019 mm between the CCI and UC groups at alpha = 0.05 and power = 80%.

Statistics

JMP software (version 5.1, SAS Institute; Cary, SC, USA) was used for all statistical analyses except multiple imputation. Multiple imputation using multivariate normal distribution, conducted with Stata software (version 11, StataCorp; College Station, TX, USA), was used to estimate means and standard errors describing the variability between imputations. Seven hundred imputations from multivariate normal regression were run to estimate the missing values (4% missing at baseline and 22% missing at 1 year). Two-sample t tests were used to test for significance of the differences in baseline biomarker values between groups. Two-sample t tests were also used to test for differences between 1-year changes between groups. Paired t tests and analysis of covariance (ANCOVA) when adjusted for baseline covariates (sex, age, baseline BMI, insulin use (user vs. non-user), and African–American race) were used to test for significance of within-group changes. A secondary analysis was conducted with the addition of smoking status as a sixth covariate. To reduce skewness before testing for significance, triglyceride, triglyceride/HDL-C ratio and hsCRP were first log-transformed, however aside from P values, the tables present results from the untransformed data. Percent change in a given biomarker was calculated as the mean difference value divided by the mean baseline value. The standard error of percent change of a given biomarker was calculated as the standard error of the change divided by the mean baseline value. Significant changes in proportions (e.g. medication use) were tested using McNemar’s test with continuity correction in completers, and linear regression of the changes in the dichotomous states when missing outcome data were imputed.

Throughout the manuscript, standard deviations are presented within parentheses and standard errors are presented following “±” symbol. Nominal significance levels (P) are presented in the tables; however, a significance level of P < 0.0019 ensures simultaneous significance at P < 0.05 for a Bonferroni adjustment for the 26 variables examined. Unless otherwise noted, results presented are intention-to-treat analyses (all starters) with missing values estimated by imputation. Some results are designated as completer analyses (excluding participants who withdrew or lacked biomarkers at 1 year).