Circadian Rhythm of Glucocorticoid Administration Entrains Clock Genes in Immune Cells: A DREAM Trial Ancillary Study

Circadian Rhythm of Glucocorticoid Administration Entrains Clock Genes in Immune Cells: A DREAM... Abstract Context Adrenal insufficiency (AI) requires lifelong glucocorticoid (GC) replacement. Conventional therapies do not mimic the endogenous cortisol circadian rhythm. Clock genes are essential components of the machinery controlling circadian functions and are influenced by GCs. However, clock gene expression has never been investigated in patients with AI. Objective To evaluate the effect of the timing of GC administration on circadian gene expression in peripheral blood mononuclear cells (PBMCs) of patients from the Dual Release Hydrocortisone vs Conventional Glucocorticoid Replacement in Hypocortisolism (DREAM) trial. Design Outcome assessor–blinded, randomized, active comparator clinical trial. Participants and Intervention Eighty-nine patients with AI were randomly assigned to continue their multiple daily GC doses or switch to an equivalent dose of once-daily modified-release hydrocortisone and were compared with 25 healthy controls; 65 patients with AI and 18 controls consented to gene expression analysis. Results Compared with healthy controls, 19 of the 68 genes were found modulated in patients with AI at baseline, 18 of which were restored to control levels 12 weeks after therapy was switched: ARNTL [BMAL] (P = 0.024), CLOCK (P = 0.016), AANAT (P = 0.021), CREB1 (P = 0.010), CREB3 (P = 0.037), MAT2A (P = 0.013); PRKAR1A, PRKAR2A, and PRKCB (all P < 0.010) and PER3, TIMELESS, CAMK2D, MAPK1, SP1, WEE1, CSNK1A1, ONP3, and PRF1 (all P < 0.001). Changes in WEE1, PRF1, and PER3 expression correlated with glycated hemoglobin, inflammatory monocytes, and CD16+ natural killer cells. Conclusions Patients with AI on standard therapy exhibit a dysregulation of circadian genes in PBMCs. The once-daily administration reconditions peripheral tissue gene expression to levels close to controls, paralleling the clinical outcomes of the DREAM trial (NCT02277587). Endogenous cortisol levels are tightly regulated and fluctuate in a circadian fashion, influencing the mRNA expression of ≥20% of the expressed genome, including that of the immune cells (1). Most hematopoietic cells circulating in peripheral blood exhibit a circadian rhythmicity that is inverse to that of cortisol, with a peak during night rest and a nadir during daily activity. This pattern is the net balance of release from the hematopoietic niche and extravasation to peripheral tissues and is regulated by clock-controlled gene expression of bone marrow–stimulating factors, endothelial adhesion molecules, and migratory cytokines. The circadian control of immune cells, both via intrinsic local mechanisms and via cortisol fluctuations, allows the organism to anticipate daily changes in activity, when the risk of antigen exposure is higher, and favors repair at night when the risk is lower (2). However, the pharmacokinetics of standard oral glucocorticoid (GC) replacement therapies make it impossible to precisely mimic cortisol’s physiologic circadian rhythm. The nonphysiologic multiple peaks and troughs of cortisol levels occurring with the immediate-release hydrocortisone distributed during the day may disrupt peripheral clock machinery, because cortisol acts as a robust endogenous zeitgeber synchronizing the central and peripheral clocks in many tissues (3). A once-daily modified-release hydrocortisone formulation has been developed combining an immediate-release coating with an extended-release core that avoids the multiple peaks and troughs of standard therapies, providing a more physiologic cortisol rhythm (4). Previous studies have shown that this formulation can improve cardiovascular risk factors, glucose metabolism, and quality of life (4, 5). The recent Dual Release Hydrocortisone vs Conventional Glucocorticoid Replacement in Hypocortisolism (DREAM) trial showed that patients with adrenal insufficiency (AI) have an altered immune profile with an atypical inflammation characterized by more classic monocytes and impaired innate immune responses related to a shedding of CD16 from natural killer cells (6). Evidence of immune function dysregulation was already known from the report by Bancos et al. (7), consistent with epidemiological data describing frequent infections in patients with AI (8–12). The DREAM trial revealed that patients randomly assigned to receive once-daily modified-release hydrocortisone therapy (“switch” treatment group) had an improved circulating immune cell profile and inflammatory status and a lowered number of infections compared with subjects on standard multiple-times-a-day GC (6). The peculiar clinical and molecular findings of the DREAM trial, and the time course of metabolic and immune changes, suggested that they were probably the results of a modification in the circadian cortisol rhythm, but a formal demonstration requires analysis of the expression of circadian genes. It is indeed known that GCs can acutely alter the oscillation of several clock-related genes by phase shifting their expression in peripheral tissues (acute stressor); however, whether the timing of GC administration delivered chronically affects clock gene expression in patients with AI has never been investigated. In the population of the DREAM trial, we have therefore tested whether: the morning expression of circadian genes in peripheral blood mononuclear cells (PBMCs) is altered in patients with AI compared with controls; whether the observed proinflammatory state and weakened defense of patients with AI receiving conventional GC replacement therapy can be related to a dysregulation of circadian gene expression; whether the “broken clock” can be recovered by switching to a more physiologic timing of GC administration; and whether restoration of clock gene expression correlated with clinical outcomes. Methods Study design and participants The rationale, design, inclusion and exclusion criteria, and results of the DREAM trial have been extensively reported elsewhere (6). Briefly, DREAM was a randomized, two-arm, outcome assessor–blinded (independent), active comparator, controlled clinical trial enrolling 89 patients with AI and 25 adrenally sufficient age-, sex-, and body mass index (BMI)–matched controls. Patients with AI were randomly assigned to either continue usual multiple daily doses of conventional GCs (standard treatment group) or switch to an equivalent dose of once-daily modified-release hydrocortisone (switch treatment group), and 25 controls were assigned to nonintervention. Patients allocated to once-daily, modified-release hydrocortisone (Plenadren®; Shire, Brussels, Belgium) were instructed to take the dose on waking, before leaving bed. Patients previously on multiple doses of hydrocortisone a day received the same total daily dose, whereas patients previously on cortisone received 0.8 mg of hydrocortisone per 1 mg of cortisone. All patients provided written informed consent, and the trial was approved by the local review board at Sapienza University, conducted in accordance with the Declaration of Helsinki, and performed between March 2014 and June 2016. For the current analyses (tertiary endpoint of the DREAM trial), only patients providing an additional informed consent for gene expression profiling were included. Of the 89 enrolled patients with AI, 65 patients (73%) provided consent to gene analysis, along with 18 of the 25 (72%) adrenally sufficient controls. The acceptance rate to donate tissue for genetic research was consistent with current trends (13). All participants underwent blood sampling in the morning between 8:00 am and 9:00 am, after an overnight fast (patients had to take their usual morning dose 2 hours before blood sampling) for immunophenotyping of PBMCs, as previously described (6). For 2 weeks before the start of the investigation, all study participants maintained stable sleep schedules, including a single 8-hour nighttime sleep episode and restricting naps. The prestudy sleep schedule was based on participants’ reported habitual sleep times and durations. For all participants, habitual sleep durations reported during the recruitment phase ranged between 7 and 9 hours. RNA extraction and circadian gene quantification PBMCs were freshly isolated from whole blood via Ficoll-Hypaque density gradient centrifugation. RNA was extracted with an Aurum Total RNA Mini Kit (Bio-Rad, Hercules, CA) followed by a DNase digestion step to remove genomic DNA contamination. Total RNA concentration was quantified with a Nanodrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA) and purity estimated by 260 nm/280 nm absorption. Of the 83 patients consenting to gene expression profiling, 26 patients with AI of the switch treatment group, 29 patients with AI of the standard treatment group, and 16 controls met the sample quality criteria (absorbance 260/280 ratios between 1.9 and 2.2, RNA concentrations ≥20 µg/mL, with integrity assessed by gel electrophoresis) (Fig. 1). Figure 1. View largeDownload slide Trial profile. OD, once daily. Figure 1. View largeDownload slide Trial profile. OD, once daily. We reverse transcribed 2 μg of RNA from each sample by using iScript Reverse Transcription Kit (Bio-Rad). The total cDNA pool obtained served as the template for subsequent PCR amplification in a real-time PCR assay predesigned 96-well panel for use with SYBR® Green circadian rhythms (SAB target list) H96 (PrimePCR®; Bio-Rad). PrimePCR® is a preoptimized assay designed to guarantee high assay specificity, compatibility, avoidance of secondary structures, primer annealing sites, and snips in the target region, maximized detection of transcript isoforms, fully validated for the human genome, which we found particularly indicated for clinical trials because it is easily reproducible and yields comparable data. Primers (including housekeeping genes) were lyophilized in each well, through the use of SsoAdvancedTM Universal SYBR® Green Supermix. The quantitative reverse transcription polymerase chain reaction was run on CFX Connect (Bio-Rad). In addition to the 84 genes tested, each plate contained housekeeping genes for quantitative analyses (GAPDH, ACTB, HPRT1, B2M) and specific controls for genomic DNA contamination, RNA quality, and efficiency. Only assays that passed internal controls were included in the database. For data analysis, the Cq expression of housekeeping genes was tested by CFX Manager™ software (Bio-Rad) to identify the most stable reference gene based on geometric mean of expression. Most of the reference genes passed the test, with GAPDH and ACTB being the most stably expressed among samples and thus selected for normalization. All gene expression results are expressed as relative expression level normalized against housekeeping genes. Statistical analysis The statistical plan of the study has been previously reported (6), and the full prespecified plan is available online (https://web.uniroma1.it/dip_dms/ricerca/trials-clinici). Briefly, efficacy analyses were based on an intention-to-treat approach. Normality of distribution was assessed by Shapiro-Wilk test. The estimated treatment differences (ETDs) in the change from baseline to week 12 were analyzed with an analysis of covariance (ANCOVA) model that included baseline outcome as a covariate and treatment as a fixed effect. Additional covariates included were sex, BMI, age, smoking, type and duration of AI, diabetes mellitus, and white blood cell count. The ANCOVA model used the last-observation-carried-forward principle and provided the least squares mean estimates, with 95% CI adjusted for multiple comparisons. Standardized residuals were tested for normality via Shapiro-Wilk test. Homoscedasticity and homogeneity of variances were assessed by visual inspection and Levene test. Multicollinearity was assessed by a variance inflation factor. Two levels of evidence were required for investigated genes to be considered clinically relevant: a differential expression at baseline between patients with AI and controls, and a significant ETD between randomization groups. Subgroup analysis was carried out reporting the significance of the treatment by subgroup interaction. Because of the risk of false discoveries in multiple testing, adjusted P values were also calculated for ETD through the modified Benjamini-Hochberg approach (14), with a value of <0.05 regarded as significant. The study was registered at clinicaltrials.gov with identifier NCT02277587. Results Overall, 83 subjects consented to gene testing, and 71 had a full gene expression analysis carried out on PBMCs freshly isolated in the morning at baseline and 12 weeks after randomization: 29 patients with AI assigned to the standard treatment group, 26 patients with AI to the switch treatment group, and 16 healthy controls to nonintervention follow-up (Fig. 1). Baseline characteristics were comparable between AI groups (switch vs standard), whereas controls had lower BMI and lipid levels (total/high-density lipoprotein-cholesterol and triglycerides) (Table 1). The total daily dose of GCs was well balanced between patient groups at randomization and was not different at study end. Gene expression data at baseline are shown in Supplemental Table 1 and Supplemental Fig. 1. The clinical features of all patients with AI enrolled in the main trial, those who consented to gene expression profiling, and those who passed quality control for gene analysis were similar, except for a higher prevalence of primary AI in the group consenting to the analysis, and consequently a lower BMI, compared with those who did not, but there were no other differences in the metabolic, immune, or infection data, suggesting that the current subgroup is representative of the outcomes of the main trial (Supplemental Table 2). Table 1. Baseline Characteristics of the Entire Set With Full Gene Expression Analysis Adrenally Sufficienta Adrenally Insufficienta Controls Total Randomly Assigned to Switch Treatment Randomly Assigned to Standard Treatment Number 16 55 26 29 Clinical featuresa  Age, y 41 (33–49) 48 (44–51) 46 (40–51) 50 (45–55)  Sex, F/M 7/9 31/24 15/11 16/13  Primary/secondary AI — 33/22 16/10 17/12  AI duration, mo — 48 (24–132) 48 (24–132) 48 (12–132) Comorbidities  Diabetes mellitus 0 (0%) 8 (15%) 3 (12%) 5 (17%)  Other autoimmune disorders 0 (0%) 15 (27%) 7 (27%) 8 (28%)  Pituitary tumor or surgery 0 (0%) 14 (26%) 8 (30%) 6 (21%)  Other hypothalamic-pituitary failure 0 (0%) 3 (5%) 1 (4%) 2 (7%)  Adrenalectomy 0 (0%) 4 (7%) 2 (8%) 2 (7%) GC replacement  Hydrocortisone — 20 (36%) 12 (46%) 8 (28%)  OD/BID/TID 0/16/4 0/11/1 0/5/3  Cortisone acetate — 35 (64%) 14 (54%) 21 (72%)  OD/BID/TID 0/34/1 0/13/1 0/21/0  Baseline equivalent dose (mg/m2/24 h) — 17 (16–19) 16 (14–18) 17 (15–20)  BMI (kg/m2) 23 (22–24) 25 (24–27) 25 (23–27) 26 (24–28) Adrenally Sufficienta Adrenally Insufficienta Controls Total Randomly Assigned to Switch Treatment Randomly Assigned to Standard Treatment Number 16 55 26 29 Clinical featuresa  Age, y 41 (33–49) 48 (44–51) 46 (40–51) 50 (45–55)  Sex, F/M 7/9 31/24 15/11 16/13  Primary/secondary AI — 33/22 16/10 17/12  AI duration, mo — 48 (24–132) 48 (24–132) 48 (12–132) Comorbidities  Diabetes mellitus 0 (0%) 8 (15%) 3 (12%) 5 (17%)  Other autoimmune disorders 0 (0%) 15 (27%) 7 (27%) 8 (28%)  Pituitary tumor or surgery 0 (0%) 14 (26%) 8 (30%) 6 (21%)  Other hypothalamic-pituitary failure 0 (0%) 3 (5%) 1 (4%) 2 (7%)  Adrenalectomy 0 (0%) 4 (7%) 2 (8%) 2 (7%) GC replacement  Hydrocortisone — 20 (36%) 12 (46%) 8 (28%)  OD/BID/TID 0/16/4 0/11/1 0/5/3  Cortisone acetate — 35 (64%) 14 (54%) 21 (72%)  OD/BID/TID 0/34/1 0/13/1 0/21/0  Baseline equivalent dose (mg/m2/24 h) — 17 (16–19) 16 (14–18) 17 (15–20)  BMI (kg/m2) 23 (22–24) 25 (24–27) 25 (23–27) 26 (24–28) Gene expression data at baseline in the two groups are reported in Supplemental Table 1. Abbreviations: BID, two times daily; OD, once daily; TID, three times daily. a Values are expressed as mean (lower–upper limit of 95% CI), median (25th; 75th percentile), count (n), or percentages (%) as appropriate. View Large Table 1. Baseline Characteristics of the Entire Set With Full Gene Expression Analysis Adrenally Sufficienta Adrenally Insufficienta Controls Total Randomly Assigned to Switch Treatment Randomly Assigned to Standard Treatment Number 16 55 26 29 Clinical featuresa  Age, y 41 (33–49) 48 (44–51) 46 (40–51) 50 (45–55)  Sex, F/M 7/9 31/24 15/11 16/13  Primary/secondary AI — 33/22 16/10 17/12  AI duration, mo — 48 (24–132) 48 (24–132) 48 (12–132) Comorbidities  Diabetes mellitus 0 (0%) 8 (15%) 3 (12%) 5 (17%)  Other autoimmune disorders 0 (0%) 15 (27%) 7 (27%) 8 (28%)  Pituitary tumor or surgery 0 (0%) 14 (26%) 8 (30%) 6 (21%)  Other hypothalamic-pituitary failure 0 (0%) 3 (5%) 1 (4%) 2 (7%)  Adrenalectomy 0 (0%) 4 (7%) 2 (8%) 2 (7%) GC replacement  Hydrocortisone — 20 (36%) 12 (46%) 8 (28%)  OD/BID/TID 0/16/4 0/11/1 0/5/3  Cortisone acetate — 35 (64%) 14 (54%) 21 (72%)  OD/BID/TID 0/34/1 0/13/1 0/21/0  Baseline equivalent dose (mg/m2/24 h) — 17 (16–19) 16 (14–18) 17 (15–20)  BMI (kg/m2) 23 (22–24) 25 (24–27) 25 (23–27) 26 (24–28) Adrenally Sufficienta Adrenally Insufficienta Controls Total Randomly Assigned to Switch Treatment Randomly Assigned to Standard Treatment Number 16 55 26 29 Clinical featuresa  Age, y 41 (33–49) 48 (44–51) 46 (40–51) 50 (45–55)  Sex, F/M 7/9 31/24 15/11 16/13  Primary/secondary AI — 33/22 16/10 17/12  AI duration, mo — 48 (24–132) 48 (24–132) 48 (12–132) Comorbidities  Diabetes mellitus 0 (0%) 8 (15%) 3 (12%) 5 (17%)  Other autoimmune disorders 0 (0%) 15 (27%) 7 (27%) 8 (28%)  Pituitary tumor or surgery 0 (0%) 14 (26%) 8 (30%) 6 (21%)  Other hypothalamic-pituitary failure 0 (0%) 3 (5%) 1 (4%) 2 (7%)  Adrenalectomy 0 (0%) 4 (7%) 2 (8%) 2 (7%) GC replacement  Hydrocortisone — 20 (36%) 12 (46%) 8 (28%)  OD/BID/TID 0/16/4 0/11/1 0/5/3  Cortisone acetate — 35 (64%) 14 (54%) 21 (72%)  OD/BID/TID 0/34/1 0/13/1 0/21/0  Baseline equivalent dose (mg/m2/24 h) — 17 (16–19) 16 (14–18) 17 (15–20)  BMI (kg/m2) 23 (22–24) 25 (24–27) 25 (23–27) 26 (24–28) Gene expression data at baseline in the two groups are reported in Supplemental Table 1. Abbreviations: BID, two times daily; OD, once daily; TID, three times daily. a Values are expressed as mean (lower–upper limit of 95% CI), median (25th; 75th percentile), count (n), or percentages (%) as appropriate. View Large The expression of circadian genes was quantified relative to the housekeeping genes through the PrimePCR® circadian rhythms pathway assay in CFX Manager™ software. Of the 84 genes included in the panel, 68 were found to be expressed in the majority of samples. At baseline, 19 genes displayed a statistically different level of expression in PBMCs drawn from healthy controls vs subjects with AI (Fig. 2 and Supplemental Fig. 1). Global inspection of the panel revealed a generalized downregulation (from black to green) of clock-controlled gene expression in patients with AI, consistent with a flattening of the endogenous oscillators. The estimated marginal differences in the relative expression are shown in Fig. 2A. In the CLOCK gene cluster, ARNTL [BMAL1] (P < 0.001) and CLOCK (P < 0.001) were found to be downregulated, whereas PER3 (P = 0.013) and TIMELESS (P = 0.005) were upregulated in patients with AI compared with controls. The CREB pathway cluster was deeply affected, with most genes underexpressed in patients with AI: CAMK2D (P = 0.001), CREB1 (P < 0.001), CREB3 (P = 0.012), MAPK1 (P = 0.007), PRKAR1A (P = 0.003), PRKAR2A (P < 0.001), and PRKCB (P = 0.003), whereas AANAT (P = 0.009) and MAT2A (P = 0.008) appeared marginally increased in patients with AI compared with controls (Fig. 2B). Among the remaining genes, baseline differences were found in transcription factors with upregulated SP1 (P < 0.001) and downregulated WEE1 (P = 0.001; Fig. 2C) and in the casein kinase gene group, with upregulated CSNK1A1 (P < 0.001) and CSNK1E (P = 0.033) and downregulated ONP3 (P = 0.037) and PRF1 (P < 0.001) (Fig. 2D). The relative expression of several genes correlated with the metabolic and immune phenotype of the entire study population (Supplemental Table 3), showing that overexpression and underexpression of circadian genes including WEE1, TIMELESS, PRF1, and PER3 are associated with the increased CD16 shedding, ADAM17 levels, inflammatory monocytes, and ultimately metabolic derangement and susceptibility to infections. Most genes did not display differential levels of expression when we compared primary and secondary AI at baseline, except for ONP3, which was significantly suppressed in primary AI only (Supplemental Table 4). Figure 2. View largeDownload slide Difference in gene expression between adrenally sufficient and insufficient groups at baseline. Relative expression of clock-related genes at baseline. Data are presented as a marginal estimated distance of AI vs healthy controls (set as reference). Means and 95% CIs are presented as data markers and bars, respectively. (A) Circadian clock genes; (B) CREB signaling genes; (C) circadian regulated transcription factors; (D) common circadian regulated genes. *P < 0.05, **P < 0.01, ***P < 0.001. Figure 2. View largeDownload slide Difference in gene expression between adrenally sufficient and insufficient groups at baseline. Relative expression of clock-related genes at baseline. Data are presented as a marginal estimated distance of AI vs healthy controls (set as reference). Means and 95% CIs are presented as data markers and bars, respectively. (A) Circadian clock genes; (B) CREB signaling genes; (C) circadian regulated transcription factors; (D) common circadian regulated genes. *P < 0.05, **P < 0.01, ***P < 0.001. At week 12, switching to once-daily modified-release hydrocortisone robustly modulated the relative expression of 22 genes when compared with patients randomly assigned to multiple-daily-dose standard treatment after adjustment for multiple comparisons (Table 2,Fig. 3,Supplemental Table 5, and Supplemental Fig. 2). Specifically, the once-daily switched treatment increased ARNTL, ARNTL2, CLOCK, and RORA expression (Fig. 3) and reduced the previously overexpressed PER3 and TIMELESS levels. The ETD between the intervention groups in the CREB signaling cluster consisted in a significant reduction of AANAT and MAT2A and a significant increase in CAMK2D, CREB1, CREB3, MAPK1, PRKAR1A, PRKAR2A, and PRKCB (Fig. 3). Regarding the other circadian regulated genes, a significant ETD was found for SP1 and WEE1, which were inversely modulated; for CSNK1A1, which was downregulated; and for the three upregulated genes GUSB, ONP3, and PRF1 (Fig. 3). Table 2. Gene Expression Data at 12 Weeks, Change From Baseline, and Treatment-Related Differences for Modulated Genes Gene Switched-Treatment Group, Mean (95% CI) (n = 26) Standard-Treatment Group, Mean (95% CI) (n = 29) Treatment-Related Differencea P Benjamini-Hochberg Adjusted P AANAT 0.072 (−0.109 to 0.253) 0.439 (0.269 to 0.609) −0.367 (−0.616 to −0.118) 0.006 0.021  Within-group change −0.406 (−0.587 to −0.225) −0.039 (−0.209 to 0.131) ARNTL 1.593 (1.186 to 2.000) 0.779 (0.398 to 1.160) 0.814 (0.241 to 1.387) 0.007 0.024  Within-group change 0.942 (0.535 to 1.349) 0.128 (−0.253 to 0.509) ARNTL2 2.297 (1.746 to 2.848) 1.248 (0.715 to 1.781) 1.049 (0.279 to 1.819) 0.010 0.032  Within-group change 0.954 (0.403 to 1.505) −0.095 (−0.628 to 0.438) CAMK2D 2.256 (1.779 to 2.733) 0.911 (0.479 to 1.343) 1.345 (0.694 to 1.995) <0.001 <0.001  Within-group change 1.333 (0.856 to 1.810) −0.011 (−0.443 to 0.421) CLOCK 2.263 (1.684 to 2.842) 1.037 (0.494 to 1.581) 1.226 (0.429 to 2.022) 0.004 0.016  Within-group change 1.310 (0.731 to 1.889) 0.084 (−0.459 to 0.628) CREB1 2.344 (1.861 to 2.828) 1.183 (0.730 to 1.637) 1.161 (0.491 to 1.832) 0.002 0.010  Within-group change 1.311 (0.827 to 1.794) 0.150 (−0.304 to 0.603) CREB3 1.768 (1.455 to 2.081) 1.197 (0.903 to 1.490) 0.571 (0.137 to 1.006) 0.012 0.037  Within-group change 0.431 (0.118 to 0.744) −0.140 (−0.434 to 0.153) CSNK1A1 0.336 (0.176 to 0.496) 0.829 (0.679 to 0.979) −0.493 (−0.713 to −0.273) <0.001 0.001  Within-group change −0.547 (−0.707 to −0.387) −0.054 (−0.204 to 0.096) GUSB 1.850 (1.497 to 2.203) 1.053 (0.722 to 1.384) 0.797 (0.305 to 1.289) 0.003 0.013  Within-group change 0.665 (0.312 to 1.018) −0.132 (−0.462 to 0.199) MAPK1 2.784 (2.063 to 3.505) 0.758 (0.105 to 1.412) 2.025 (1.045 to 3.006) <0.001 <0.001  Within-group change 2.035 (1.314 to 2.756) 0.010 (−0.644 to 0.663) MAT2A 0.234 (0.019 to 0.449) 0.702 (0.500 to 0.904) −0.468 (−0.764 to −0.172) 0.003 0.013  Within-group change −0.503 (−0.718 to −0.288) −0.035 (−0.237 to 0.167) ONP3 1.910 (1.736 to 2.083) 1.422 (1.264 to 1.579) 0.488 (0.252 to 0.724) <0.001 0.001  Within-group change 0.558 (0.385 to 0.732) 0.070 (−0.087 to 0.227) PER3 0.764 (0.590 to 0.938) 1.328 (1.159 to 1.497) −0.564 (−0.808 to −0.321) <0.001 <0.001  Within-group change −0.585 (−0.759 to −0.411) −0.021 (−0.190 to 0.149) PRF1 1.451 (1.310 to 1.592) 0.636 (0.504 to 0.769) 0.814 (0.621 to 1.008) <0.001 0.001  Within-group change 0.851 (0.710 to 0.992) 0.037 (−0.096 to 0.169) PRKAR1A 2.179 (1.784 to 2.575) 1.144 (0.773 to 1.516) 1.035 (0.489 to 1.581) 0.001 0.006  Within-group change 0.950 (0.555 to 1.346) −0.085 (−0.456 to 0.287) PRKAR2A 1.993 (1.532 to 2.454) 0.881 (0.449 to 1.312) 1.112 (0.479 to 1.746) 0.001 0.006  Within-group change 1.090 (0.629 to 1.511) −0.022 (−0.454 to 0.409) PRKAR2B 0.837 (0.671 to 1.003) 0.462 (0.306 to 0.618) 0.375 (0.147 to 0.603) 0.002 0.010  Within-group change 0.428 (0.262 to 0.593) 0.053 (−0.103 to 0.209) PRKCB 2.129 (1.757 to 2.501) 1.228 (0.878 to 1.577) 0.902 (0.386 to 1.417) 0.001 0.006  Within-group change 0.792 (0.420 to 1.164) −0.109 (−0.458 to 0.240) RORA 2.088 (1.397 to 2.779) 0.677 (0.029 to 1.325) 1.411 (0.453 to 2.369) 0.006 0.021  Within-group change 1.271 (0.581 to 1.962) −0.140 (−0.788 to 0.508) SP1 0.758 (0.541 to 0.975) 1.424 (1.227 to 1.620) −0.665 (−0.962 to −0.369) <0.001 <0.001  Within-group change −0.715 (−0.932 to −0.497) −0.049 (−0.246 to 0.148) TIMELESS 0.356 (0.185 to 0.526) 1.038 (0.878 to 1.198) −0.682 (−0.920 to −0.444) <0.001 <0.001  Within-group change −0.786 (−0.957 to −0.616) −0.104 (−0.264 to 0.056) WEE1 1.723 (1.513 to 1.932) 0.818 (0.622 to 1.015) 0.904 (0.616 to 1.193) <0.001 <0.001  Within-group change 0.850 (0.641 to 1.059) −0.054 (−0.251 to 0.142) Gene Switched-Treatment Group, Mean (95% CI) (n = 26) Standard-Treatment Group, Mean (95% CI) (n = 29) Treatment-Related Differencea P Benjamini-Hochberg Adjusted P AANAT 0.072 (−0.109 to 0.253) 0.439 (0.269 to 0.609) −0.367 (−0.616 to −0.118) 0.006 0.021  Within-group change −0.406 (−0.587 to −0.225) −0.039 (−0.209 to 0.131) ARNTL 1.593 (1.186 to 2.000) 0.779 (0.398 to 1.160) 0.814 (0.241 to 1.387) 0.007 0.024  Within-group change 0.942 (0.535 to 1.349) 0.128 (−0.253 to 0.509) ARNTL2 2.297 (1.746 to 2.848) 1.248 (0.715 to 1.781) 1.049 (0.279 to 1.819) 0.010 0.032  Within-group change 0.954 (0.403 to 1.505) −0.095 (−0.628 to 0.438) CAMK2D 2.256 (1.779 to 2.733) 0.911 (0.479 to 1.343) 1.345 (0.694 to 1.995) <0.001 <0.001  Within-group change 1.333 (0.856 to 1.810) −0.011 (−0.443 to 0.421) CLOCK 2.263 (1.684 to 2.842) 1.037 (0.494 to 1.581) 1.226 (0.429 to 2.022) 0.004 0.016  Within-group change 1.310 (0.731 to 1.889) 0.084 (−0.459 to 0.628) CREB1 2.344 (1.861 to 2.828) 1.183 (0.730 to 1.637) 1.161 (0.491 to 1.832) 0.002 0.010  Within-group change 1.311 (0.827 to 1.794) 0.150 (−0.304 to 0.603) CREB3 1.768 (1.455 to 2.081) 1.197 (0.903 to 1.490) 0.571 (0.137 to 1.006) 0.012 0.037  Within-group change 0.431 (0.118 to 0.744) −0.140 (−0.434 to 0.153) CSNK1A1 0.336 (0.176 to 0.496) 0.829 (0.679 to 0.979) −0.493 (−0.713 to −0.273) <0.001 0.001  Within-group change −0.547 (−0.707 to −0.387) −0.054 (−0.204 to 0.096) GUSB 1.850 (1.497 to 2.203) 1.053 (0.722 to 1.384) 0.797 (0.305 to 1.289) 0.003 0.013  Within-group change 0.665 (0.312 to 1.018) −0.132 (−0.462 to 0.199) MAPK1 2.784 (2.063 to 3.505) 0.758 (0.105 to 1.412) 2.025 (1.045 to 3.006) <0.001 <0.001  Within-group change 2.035 (1.314 to 2.756) 0.010 (−0.644 to 0.663) MAT2A 0.234 (0.019 to 0.449) 0.702 (0.500 to 0.904) −0.468 (−0.764 to −0.172) 0.003 0.013  Within-group change −0.503 (−0.718 to −0.288) −0.035 (−0.237 to 0.167) ONP3 1.910 (1.736 to 2.083) 1.422 (1.264 to 1.579) 0.488 (0.252 to 0.724) <0.001 0.001  Within-group change 0.558 (0.385 to 0.732) 0.070 (−0.087 to 0.227) PER3 0.764 (0.590 to 0.938) 1.328 (1.159 to 1.497) −0.564 (−0.808 to −0.321) <0.001 <0.001  Within-group change −0.585 (−0.759 to −0.411) −0.021 (−0.190 to 0.149) PRF1 1.451 (1.310 to 1.592) 0.636 (0.504 to 0.769) 0.814 (0.621 to 1.008) <0.001 0.001  Within-group change 0.851 (0.710 to 0.992) 0.037 (−0.096 to 0.169) PRKAR1A 2.179 (1.784 to 2.575) 1.144 (0.773 to 1.516) 1.035 (0.489 to 1.581) 0.001 0.006  Within-group change 0.950 (0.555 to 1.346) −0.085 (−0.456 to 0.287) PRKAR2A 1.993 (1.532 to 2.454) 0.881 (0.449 to 1.312) 1.112 (0.479 to 1.746) 0.001 0.006  Within-group change 1.090 (0.629 to 1.511) −0.022 (−0.454 to 0.409) PRKAR2B 0.837 (0.671 to 1.003) 0.462 (0.306 to 0.618) 0.375 (0.147 to 0.603) 0.002 0.010  Within-group change 0.428 (0.262 to 0.593) 0.053 (−0.103 to 0.209) PRKCB 2.129 (1.757 to 2.501) 1.228 (0.878 to 1.577) 0.902 (0.386 to 1.417) 0.001 0.006  Within-group change 0.792 (0.420 to 1.164) −0.109 (−0.458 to 0.240) RORA 2.088 (1.397 to 2.779) 0.677 (0.029 to 1.325) 1.411 (0.453 to 2.369) 0.006 0.021  Within-group change 1.271 (0.581 to 1.962) −0.140 (−0.788 to 0.508) SP1 0.758 (0.541 to 0.975) 1.424 (1.227 to 1.620) −0.665 (−0.962 to −0.369) <0.001 <0.001  Within-group change −0.715 (−0.932 to −0.497) −0.049 (−0.246 to 0.148) TIMELESS 0.356 (0.185 to 0.526) 1.038 (0.878 to 1.198) −0.682 (−0.920 to −0.444) <0.001 <0.001  Within-group change −0.786 (−0.957 to −0.616) −0.104 (−0.264 to 0.056) WEE1 1.723 (1.513 to 1.932) 0.818 (0.622 to 1.015) 0.904 (0.616 to 1.193) <0.001 <0.001  Within-group change 0.850 (0.641 to 1.059) −0.054 (−0.251 to 0.142) Nonmodulated gene expression data are reported in Supplemental Table 5. a Covariates in the ANCOVA model: age, sex, BMI, type of AI, diabetes mellitus, smoking, and outcome at baseline. View Large Table 2. Gene Expression Data at 12 Weeks, Change From Baseline, and Treatment-Related Differences for Modulated Genes Gene Switched-Treatment Group, Mean (95% CI) (n = 26) Standard-Treatment Group, Mean (95% CI) (n = 29) Treatment-Related Differencea P Benjamini-Hochberg Adjusted P AANAT 0.072 (−0.109 to 0.253) 0.439 (0.269 to 0.609) −0.367 (−0.616 to −0.118) 0.006 0.021  Within-group change −0.406 (−0.587 to −0.225) −0.039 (−0.209 to 0.131) ARNTL 1.593 (1.186 to 2.000) 0.779 (0.398 to 1.160) 0.814 (0.241 to 1.387) 0.007 0.024  Within-group change 0.942 (0.535 to 1.349) 0.128 (−0.253 to 0.509) ARNTL2 2.297 (1.746 to 2.848) 1.248 (0.715 to 1.781) 1.049 (0.279 to 1.819) 0.010 0.032  Within-group change 0.954 (0.403 to 1.505) −0.095 (−0.628 to 0.438) CAMK2D 2.256 (1.779 to 2.733) 0.911 (0.479 to 1.343) 1.345 (0.694 to 1.995) <0.001 <0.001  Within-group change 1.333 (0.856 to 1.810) −0.011 (−0.443 to 0.421) CLOCK 2.263 (1.684 to 2.842) 1.037 (0.494 to 1.581) 1.226 (0.429 to 2.022) 0.004 0.016  Within-group change 1.310 (0.731 to 1.889) 0.084 (−0.459 to 0.628) CREB1 2.344 (1.861 to 2.828) 1.183 (0.730 to 1.637) 1.161 (0.491 to 1.832) 0.002 0.010  Within-group change 1.311 (0.827 to 1.794) 0.150 (−0.304 to 0.603) CREB3 1.768 (1.455 to 2.081) 1.197 (0.903 to 1.490) 0.571 (0.137 to 1.006) 0.012 0.037  Within-group change 0.431 (0.118 to 0.744) −0.140 (−0.434 to 0.153) CSNK1A1 0.336 (0.176 to 0.496) 0.829 (0.679 to 0.979) −0.493 (−0.713 to −0.273) <0.001 0.001  Within-group change −0.547 (−0.707 to −0.387) −0.054 (−0.204 to 0.096) GUSB 1.850 (1.497 to 2.203) 1.053 (0.722 to 1.384) 0.797 (0.305 to 1.289) 0.003 0.013  Within-group change 0.665 (0.312 to 1.018) −0.132 (−0.462 to 0.199) MAPK1 2.784 (2.063 to 3.505) 0.758 (0.105 to 1.412) 2.025 (1.045 to 3.006) <0.001 <0.001  Within-group change 2.035 (1.314 to 2.756) 0.010 (−0.644 to 0.663) MAT2A 0.234 (0.019 to 0.449) 0.702 (0.500 to 0.904) −0.468 (−0.764 to −0.172) 0.003 0.013  Within-group change −0.503 (−0.718 to −0.288) −0.035 (−0.237 to 0.167) ONP3 1.910 (1.736 to 2.083) 1.422 (1.264 to 1.579) 0.488 (0.252 to 0.724) <0.001 0.001  Within-group change 0.558 (0.385 to 0.732) 0.070 (−0.087 to 0.227) PER3 0.764 (0.590 to 0.938) 1.328 (1.159 to 1.497) −0.564 (−0.808 to −0.321) <0.001 <0.001  Within-group change −0.585 (−0.759 to −0.411) −0.021 (−0.190 to 0.149) PRF1 1.451 (1.310 to 1.592) 0.636 (0.504 to 0.769) 0.814 (0.621 to 1.008) <0.001 0.001  Within-group change 0.851 (0.710 to 0.992) 0.037 (−0.096 to 0.169) PRKAR1A 2.179 (1.784 to 2.575) 1.144 (0.773 to 1.516) 1.035 (0.489 to 1.581) 0.001 0.006  Within-group change 0.950 (0.555 to 1.346) −0.085 (−0.456 to 0.287) PRKAR2A 1.993 (1.532 to 2.454) 0.881 (0.449 to 1.312) 1.112 (0.479 to 1.746) 0.001 0.006  Within-group change 1.090 (0.629 to 1.511) −0.022 (−0.454 to 0.409) PRKAR2B 0.837 (0.671 to 1.003) 0.462 (0.306 to 0.618) 0.375 (0.147 to 0.603) 0.002 0.010  Within-group change 0.428 (0.262 to 0.593) 0.053 (−0.103 to 0.209) PRKCB 2.129 (1.757 to 2.501) 1.228 (0.878 to 1.577) 0.902 (0.386 to 1.417) 0.001 0.006  Within-group change 0.792 (0.420 to 1.164) −0.109 (−0.458 to 0.240) RORA 2.088 (1.397 to 2.779) 0.677 (0.029 to 1.325) 1.411 (0.453 to 2.369) 0.006 0.021  Within-group change 1.271 (0.581 to 1.962) −0.140 (−0.788 to 0.508) SP1 0.758 (0.541 to 0.975) 1.424 (1.227 to 1.620) −0.665 (−0.962 to −0.369) <0.001 <0.001  Within-group change −0.715 (−0.932 to −0.497) −0.049 (−0.246 to 0.148) TIMELESS 0.356 (0.185 to 0.526) 1.038 (0.878 to 1.198) −0.682 (−0.920 to −0.444) <0.001 <0.001  Within-group change −0.786 (−0.957 to −0.616) −0.104 (−0.264 to 0.056) WEE1 1.723 (1.513 to 1.932) 0.818 (0.622 to 1.015) 0.904 (0.616 to 1.193) <0.001 <0.001  Within-group change 0.850 (0.641 to 1.059) −0.054 (−0.251 to 0.142) Gene Switched-Treatment Group, Mean (95% CI) (n = 26) Standard-Treatment Group, Mean (95% CI) (n = 29) Treatment-Related Differencea P Benjamini-Hochberg Adjusted P AANAT 0.072 (−0.109 to 0.253) 0.439 (0.269 to 0.609) −0.367 (−0.616 to −0.118) 0.006 0.021  Within-group change −0.406 (−0.587 to −0.225) −0.039 (−0.209 to 0.131) ARNTL 1.593 (1.186 to 2.000) 0.779 (0.398 to 1.160) 0.814 (0.241 to 1.387) 0.007 0.024  Within-group change 0.942 (0.535 to 1.349) 0.128 (−0.253 to 0.509) ARNTL2 2.297 (1.746 to 2.848) 1.248 (0.715 to 1.781) 1.049 (0.279 to 1.819) 0.010 0.032  Within-group change 0.954 (0.403 to 1.505) −0.095 (−0.628 to 0.438) CAMK2D 2.256 (1.779 to 2.733) 0.911 (0.479 to 1.343) 1.345 (0.694 to 1.995) <0.001 <0.001  Within-group change 1.333 (0.856 to 1.810) −0.011 (−0.443 to 0.421) CLOCK 2.263 (1.684 to 2.842) 1.037 (0.494 to 1.581) 1.226 (0.429 to 2.022) 0.004 0.016  Within-group change 1.310 (0.731 to 1.889) 0.084 (−0.459 to 0.628) CREB1 2.344 (1.861 to 2.828) 1.183 (0.730 to 1.637) 1.161 (0.491 to 1.832) 0.002 0.010  Within-group change 1.311 (0.827 to 1.794) 0.150 (−0.304 to 0.603) CREB3 1.768 (1.455 to 2.081) 1.197 (0.903 to 1.490) 0.571 (0.137 to 1.006) 0.012 0.037  Within-group change 0.431 (0.118 to 0.744) −0.140 (−0.434 to 0.153) CSNK1A1 0.336 (0.176 to 0.496) 0.829 (0.679 to 0.979) −0.493 (−0.713 to −0.273) <0.001 0.001  Within-group change −0.547 (−0.707 to −0.387) −0.054 (−0.204 to 0.096) GUSB 1.850 (1.497 to 2.203) 1.053 (0.722 to 1.384) 0.797 (0.305 to 1.289) 0.003 0.013  Within-group change 0.665 (0.312 to 1.018) −0.132 (−0.462 to 0.199) MAPK1 2.784 (2.063 to 3.505) 0.758 (0.105 to 1.412) 2.025 (1.045 to 3.006) <0.001 <0.001  Within-group change 2.035 (1.314 to 2.756) 0.010 (−0.644 to 0.663) MAT2A 0.234 (0.019 to 0.449) 0.702 (0.500 to 0.904) −0.468 (−0.764 to −0.172) 0.003 0.013  Within-group change −0.503 (−0.718 to −0.288) −0.035 (−0.237 to 0.167) ONP3 1.910 (1.736 to 2.083) 1.422 (1.264 to 1.579) 0.488 (0.252 to 0.724) <0.001 0.001  Within-group change 0.558 (0.385 to 0.732) 0.070 (−0.087 to 0.227) PER3 0.764 (0.590 to 0.938) 1.328 (1.159 to 1.497) −0.564 (−0.808 to −0.321) <0.001 <0.001  Within-group change −0.585 (−0.759 to −0.411) −0.021 (−0.190 to 0.149) PRF1 1.451 (1.310 to 1.592) 0.636 (0.504 to 0.769) 0.814 (0.621 to 1.008) <0.001 0.001  Within-group change 0.851 (0.710 to 0.992) 0.037 (−0.096 to 0.169) PRKAR1A 2.179 (1.784 to 2.575) 1.144 (0.773 to 1.516) 1.035 (0.489 to 1.581) 0.001 0.006  Within-group change 0.950 (0.555 to 1.346) −0.085 (−0.456 to 0.287) PRKAR2A 1.993 (1.532 to 2.454) 0.881 (0.449 to 1.312) 1.112 (0.479 to 1.746) 0.001 0.006  Within-group change 1.090 (0.629 to 1.511) −0.022 (−0.454 to 0.409) PRKAR2B 0.837 (0.671 to 1.003) 0.462 (0.306 to 0.618) 0.375 (0.147 to 0.603) 0.002 0.010  Within-group change 0.428 (0.262 to 0.593) 0.053 (−0.103 to 0.209) PRKCB 2.129 (1.757 to 2.501) 1.228 (0.878 to 1.577) 0.902 (0.386 to 1.417) 0.001 0.006  Within-group change 0.792 (0.420 to 1.164) −0.109 (−0.458 to 0.240) RORA 2.088 (1.397 to 2.779) 0.677 (0.029 to 1.325) 1.411 (0.453 to 2.369) 0.006 0.021  Within-group change 1.271 (0.581 to 1.962) −0.140 (−0.788 to 0.508) SP1 0.758 (0.541 to 0.975) 1.424 (1.227 to 1.620) −0.665 (−0.962 to −0.369) <0.001 <0.001  Within-group change −0.715 (−0.932 to −0.497) −0.049 (−0.246 to 0.148) TIMELESS 0.356 (0.185 to 0.526) 1.038 (0.878 to 1.198) −0.682 (−0.920 to −0.444) <0.001 <0.001  Within-group change −0.786 (−0.957 to −0.616) −0.104 (−0.264 to 0.056) WEE1 1.723 (1.513 to 1.932) 0.818 (0.622 to 1.015) 0.904 (0.616 to 1.193) <0.001 <0.001  Within-group change 0.850 (0.641 to 1.059) −0.054 (−0.251 to 0.142) Nonmodulated gene expression data are reported in Supplemental Table 5. a Covariates in the ANCOVA model: age, sex, BMI, type of AI, diabetes mellitus, smoking, and outcome at baseline. View Large Figure 3. View largeDownload slide Differentially modulated genes in all groups at baseline and after treatment. The relative expression of the CLOCK-related (upper), CREB signaling–related (middle), and other circadian-controlled genes (bottom) at baseline and 12 weeks after randomization in all groups. Means ± SEM are presented as data markers and bars, respectively, and changes within subjects are presented as lines: controls (gray), switch-treatment group (green), and standard group (orange). Figure 3. View largeDownload slide Differentially modulated genes in all groups at baseline and after treatment. The relative expression of the CLOCK-related (upper), CREB signaling–related (middle), and other circadian-controlled genes (bottom) at baseline and 12 weeks after randomization in all groups. Means ± SEM are presented as data markers and bars, respectively, and changes within subjects are presented as lines: controls (gray), switch-treatment group (green), and standard group (orange). Of the 19 genes that were differentially modulated at baseline when we compared subjects with AI with control subjects, all but one (CSNK1E) were affected by treatment allocation (Fig. 3), thus matching the two prerequisites for relevance: a differential expression at baseline compared with healthy controls and a significant treatment difference between randomization groups. For all 18 genes the modulation was toward the level of expression found in healthy controls, that is, toward normalization. However, a significant ETD was found for another four genes that were not found modulated at baseline (ARNTL2, GUSB, PRKR2B, and RORA) (Supplemental Fig. 3). Subgroup analysis revealed no treatment by subgroup interaction for any of the modulated genes (Supplemental Table 6), suggesting that the effects of treatment switch were independent of the underlying etiology of the AI. Because treatment allocation produced a shift in the phenotype of some subsets of circulating PBMCs, namely a reduction in CD14+CD16− and an increase in CD16+CD56+CD3− cells (6), we also investigated gene expression in the subset that remained stable during the trial, the CD3+ T lymphocytes, which were unaffected by treatment. Of the 19 differentially expressed genes, 16 were also modulated in lymphocytes sorted from the entire set of PBMCs pooled according to treatment allocation (Supplemental Figure 4). Significant correlations were found between the change in several clock gene expression and the change in clinical outcomes including the glycated hemoglobin, blood pressure, levels of circulating soluble CD16, ADAM17, classic proinflammatory monocytes, and ultimately the frequency of infections (Table 3), suggesting that the extent of reprogramming of circadian gene expression can be linked to the magnitude of improvement in clinically measurable outcomes. Table 3. Delta Change Correlation Matrix Gene Δ-BMI Δ-HbA1c Δ-High-Density Lipoprotein Δ-TGa Δ-SBPb Δ-DBPc Δ-CD16+ CD14− Cells Δ-CD14+ CD16− Cells Δ-CD16+ NK Cells Δ-ADAM17d Δ-CD16sd Δ-Infectionse Δ-AANAT r −0.272 0.394f 0.176 −0.096 −0.092 0.183 −0.016 −0.009 0.008 0.197 0.359 −0.185 P 0.132 0.038 0.352 0.603 0.617 0.315 0.929 0.963 0.970 0.393 0.110 0.303 Δ-ARNTL r −0.279 0.023 0.023 −0.091 0.184 −0.136 0.171 −0.166 −0.011 −0.398 −0.252 −0.252 P 0.122 0.909 0.905 0.620 0.314 0.457 0.349 0.364 0.958 0.074 0.270 0.157 Δ-ARNTL2 r −0.294 −0.071 −0.039 −0.222 −0.055 0.495g 0.060 −0.041 0.038 −0.493f −0.352 −0.182 P 0.109 0.724 0.840 0.230 0.768 0.005 0.749 0.826 0.857 0.023 0.118 0.318 Δ-CAMK2D r 0.186 −0.384f −0.209 0.003 0.271 −0.178 0.234 −0.400f 0.232 −0.615g −0.378 −0.053 P 0.317 0.048 0.276 0.985 0.141 0.339 0.205 0.026 0.265 0.004 0.100 0.775 Δ-CLOCK r −0.094 −0.088 0.017 0.017 0.334 −0.008 0.255 −0.196 0.062 −0.360 −0.253 −0.246 P 0.609 0.657 0.927 0.927 0.061 0.966 0.159 0.282 0.764 0.109 0.268 0.167 Δ-CREB1 r 0.067 −0.165 −0.098 0.021 0.364f −0.300 0.135 −0.119 −0.081 −0.191 −0.092 −0.003 P 0.715 0.403 0.607 0.907 0.040 0.095 0.462 0.518 0.695 0.407 0.690 0.986 Δ-CREB3 r 0.075 −0.200 −0.034 −0.180 0.060 −0.026 0.272 −0.082 0.189 −0.343 −0.303 −0.056 P 0.683 0.298 0.857 0.325 0.746 0.889 0.133 0.655 0.345 0.118 0.170 0.757 Δ-CSNK1A1 r −0.366f 0.507g 0.254 0.092 −0.110 0.199 −0.148 0.343 −0.197 0.585g 0.414 −0.108 P 0.040 0.006 0.175 0.615 0.549 0.276 0.418 0.055 0.335 0.005 0.062 0.550 Δ-CSNK1E r 0.018 0.258 −0.223 0.344 −0.082 0.137 0.139 −0.080 0.062 0.104 0.081 0.244 P 0.924 0.177 0.229 0.054 0.657 0.453 0.449 0.665 0.759 0.646 0.720 0.171 Δ-GUSB r −0.335 0.014 −0.041 −0.216 −0.112 −0.251 0.358f −0.310 0.275 −0.685g −0.565g −0.402* P 0.061 0.943 0.831 0.236 0.540 0.165 0.044 0.084 0.173 0.001 0.008 0.020 Δ-MAPK1 r −0.021 −0.293 −0.028 −0.161 0.184 −0.001 0.177 −0.219 0.078 −0.566g −0.449f −0.143 P 0.910 0.137 0.884 0.388 0.323 0.996 0.342 0.235 0.712 0.009 0.047 0.435 Δ-MAT2A r −0.255 0.439f 0.222 −0.051 0.081 −0.024 −0.032 0.072 −0.068 0.195 0.326 −0.098 P 0.158 0.019 0.238 0.780 0.661 0.894 0.860 0.694 0.742 0.396 0.149 0.586 Δ-ONP3 r 0.257 −0.352 −0.222 −0.002 0.041 0.059 0.581g −0.355f 0.312 −0.527f −0.475f −0.306 P 0.162 0.066 0.237 0.991 0.826 0.752 0.001 0.050 0.121 0.012 0.025 0.089 Δ-PER3 r 0.070 0.325 0.125 −0.019 0.289 −0.315 −0.280 0.327 −0.175 0.505f 0.443f 0.433f P 0.698 0.085 0.504 0.918 0.103 0.074 0.115 0.063 0.394 0.017 0.039 0.010 Δ-PRF1 r 0.093 −0.446f −0.268 −0.046 −0.042 0.044 0.489g −0.437f 0.442f −0.697g −0.691g −0.309 P 0.612 0.017 0.153 0.803 0.821 0.812 0.004 0.012 0.024 <0.001 0.001 0.080 Δ-PRKAR1A r 0.011 −0.239 −0.005 −0.168 0.223 0.175 0.313 −0.288 0.137 −0.427 −0.357 −0.187 P 0.952 0.220 0.977 0.358 0.219 0.337 0.081 0.110 0.503 0.054 0.112 0.297 Δ-PRKAR2A r −0.189 −0.163 −0.041 0.084 0.035 0.123 0.363f −0.308 0.143 −0.592g −0.420 −0.465g P 0.318 0.426 0.838 0.660 0.856 0.518 0.049 0.098 0.507 0.008 0.073 0.008 Δ-PRKCB r −0.111 0.108 0.266 0.262 0.185 0.114 0.202 −0.245 0.156 0.025 0.302 0.177 P 0.457 0.500 0.084 0.082 0.213 0.447 0.269 0.177 0.446 0.885 0.077 0.233 Δ-RORA r −0.252 −0.069 0.009 −0.092 0.099 0.088 0.100 −0.199 0.088 −0.504f −0.358 −0.243 P 0.163 0.728 0.961 0.615 0.590 0.631 0.587 0.275 0.670 0.020 0.111 0.172 Δ-SP1 r −0.017 0.236 0.214 −0.305 0.063 0.015 −0.323 0.350 −0.093 0.462f 0.659g 0.307 P 0.929 0.226 0.257 0.095 0.738 0.935 0.076 0.053 0.650 0.031 0.001 0.087 Δ-TIMELESS r 0.183 0.210 0.057 0.103 0.377f −0.329 −0.181 0.303 −0.186 0.587g 0.736g 0.408g P 0.317 0.284 0.764 0.574 0.034 0.066 0.321 0.092 0.363 0.005 <0.001 0.019 Δ-WEE1 r −0.105 −0.375f −0.144 0.180 −0.011 −0.013 0.583g −0.542g 0.334 −0.778g −0.645g −0.527g P 0.553 0.041 0.432 0.307 0.951 0.940 <0.001 0.001 0.096 <0.001 0.001 0.001 Gene Δ-BMI Δ-HbA1c Δ-High-Density Lipoprotein Δ-TGa Δ-SBPb Δ-DBPc Δ-CD16+ CD14− Cells Δ-CD14+ CD16− Cells Δ-CD16+ NK Cells Δ-ADAM17d Δ-CD16sd Δ-Infectionse Δ-AANAT r −0.272 0.394f 0.176 −0.096 −0.092 0.183 −0.016 −0.009 0.008 0.197 0.359 −0.185 P 0.132 0.038 0.352 0.603 0.617 0.315 0.929 0.963 0.970 0.393 0.110 0.303 Δ-ARNTL r −0.279 0.023 0.023 −0.091 0.184 −0.136 0.171 −0.166 −0.011 −0.398 −0.252 −0.252 P 0.122 0.909 0.905 0.620 0.314 0.457 0.349 0.364 0.958 0.074 0.270 0.157 Δ-ARNTL2 r −0.294 −0.071 −0.039 −0.222 −0.055 0.495g 0.060 −0.041 0.038 −0.493f −0.352 −0.182 P 0.109 0.724 0.840 0.230 0.768 0.005 0.749 0.826 0.857 0.023 0.118 0.318 Δ-CAMK2D r 0.186 −0.384f −0.209 0.003 0.271 −0.178 0.234 −0.400f 0.232 −0.615g −0.378 −0.053 P 0.317 0.048 0.276 0.985 0.141 0.339 0.205 0.026 0.265 0.004 0.100 0.775 Δ-CLOCK r −0.094 −0.088 0.017 0.017 0.334 −0.008 0.255 −0.196 0.062 −0.360 −0.253 −0.246 P 0.609 0.657 0.927 0.927 0.061 0.966 0.159 0.282 0.764 0.109 0.268 0.167 Δ-CREB1 r 0.067 −0.165 −0.098 0.021 0.364f −0.300 0.135 −0.119 −0.081 −0.191 −0.092 −0.003 P 0.715 0.403 0.607 0.907 0.040 0.095 0.462 0.518 0.695 0.407 0.690 0.986 Δ-CREB3 r 0.075 −0.200 −0.034 −0.180 0.060 −0.026 0.272 −0.082 0.189 −0.343 −0.303 −0.056 P 0.683 0.298 0.857 0.325 0.746 0.889 0.133 0.655 0.345 0.118 0.170 0.757 Δ-CSNK1A1 r −0.366f 0.507g 0.254 0.092 −0.110 0.199 −0.148 0.343 −0.197 0.585g 0.414 −0.108 P 0.040 0.006 0.175 0.615 0.549 0.276 0.418 0.055 0.335 0.005 0.062 0.550 Δ-CSNK1E r 0.018 0.258 −0.223 0.344 −0.082 0.137 0.139 −0.080 0.062 0.104 0.081 0.244 P 0.924 0.177 0.229 0.054 0.657 0.453 0.449 0.665 0.759 0.646 0.720 0.171 Δ-GUSB r −0.335 0.014 −0.041 −0.216 −0.112 −0.251 0.358f −0.310 0.275 −0.685g −0.565g −0.402* P 0.061 0.943 0.831 0.236 0.540 0.165 0.044 0.084 0.173 0.001 0.008 0.020 Δ-MAPK1 r −0.021 −0.293 −0.028 −0.161 0.184 −0.001 0.177 −0.219 0.078 −0.566g −0.449f −0.143 P 0.910 0.137 0.884 0.388 0.323 0.996 0.342 0.235 0.712 0.009 0.047 0.435 Δ-MAT2A r −0.255 0.439f 0.222 −0.051 0.081 −0.024 −0.032 0.072 −0.068 0.195 0.326 −0.098 P 0.158 0.019 0.238 0.780 0.661 0.894 0.860 0.694 0.742 0.396 0.149 0.586 Δ-ONP3 r 0.257 −0.352 −0.222 −0.002 0.041 0.059 0.581g −0.355f 0.312 −0.527f −0.475f −0.306 P 0.162 0.066 0.237 0.991 0.826 0.752 0.001 0.050 0.121 0.012 0.025 0.089 Δ-PER3 r 0.070 0.325 0.125 −0.019 0.289 −0.315 −0.280 0.327 −0.175 0.505f 0.443f 0.433f P 0.698 0.085 0.504 0.918 0.103 0.074 0.115 0.063 0.394 0.017 0.039 0.010 Δ-PRF1 r 0.093 −0.446f −0.268 −0.046 −0.042 0.044 0.489g −0.437f 0.442f −0.697g −0.691g −0.309 P 0.612 0.017 0.153 0.803 0.821 0.812 0.004 0.012 0.024 <0.001 0.001 0.080 Δ-PRKAR1A r 0.011 −0.239 −0.005 −0.168 0.223 0.175 0.313 −0.288 0.137 −0.427 −0.357 −0.187 P 0.952 0.220 0.977 0.358 0.219 0.337 0.081 0.110 0.503 0.054 0.112 0.297 Δ-PRKAR2A r −0.189 −0.163 −0.041 0.084 0.035 0.123 0.363f −0.308 0.143 −0.592g −0.420 −0.465g P 0.318 0.426 0.838 0.660 0.856 0.518 0.049 0.098 0.507 0.008 0.073 0.008 Δ-PRKCB r −0.111 0.108 0.266 0.262 0.185 0.114 0.202 −0.245 0.156 0.025 0.302 0.177 P 0.457 0.500 0.084 0.082 0.213 0.447 0.269 0.177 0.446 0.885 0.077 0.233 Δ-RORA r −0.252 −0.069 0.009 −0.092 0.099 0.088 0.100 −0.199 0.088 −0.504f −0.358 −0.243 P 0.163 0.728 0.961 0.615 0.590 0.631 0.587 0.275 0.670 0.020 0.111 0.172 Δ-SP1 r −0.017 0.236 0.214 −0.305 0.063 0.015 −0.323 0.350 −0.093 0.462f 0.659g 0.307 P 0.929 0.226 0.257 0.095 0.738 0.935 0.076 0.053 0.650 0.031 0.001 0.087 Δ-TIMELESS r 0.183 0.210 0.057 0.103 0.377f −0.329 −0.181 0.303 −0.186 0.587g 0.736g 0.408g P 0.317 0.284 0.764 0.574 0.034 0.066 0.321 0.092 0.363 0.005 <0.001 0.019 Δ-WEE1 r −0.105 −0.375f −0.144 0.180 −0.011 −0.013 0.583g −0.542g 0.334 −0.778g −0.645g −0.527g P 0.553 0.041 0.432 0.307 0.951 0.940 <0.001 0.001 0.096 <0.001 0.001 0.001 Boldface indicates statistical significance. a Triglycerides. b Systolic blood pressure. c Diastolic blood pressure. d Log10(ADAM17) and log10(CD16s). e Total infection score. f P < 0.05. g P < 0.01. View Large Table 3. Delta Change Correlation Matrix Gene Δ-BMI Δ-HbA1c Δ-High-Density Lipoprotein Δ-TGa Δ-SBPb Δ-DBPc Δ-CD16+ CD14− Cells Δ-CD14+ CD16− Cells Δ-CD16+ NK Cells Δ-ADAM17d Δ-CD16sd Δ-Infectionse Δ-AANAT r −0.272 0.394f 0.176 −0.096 −0.092 0.183 −0.016 −0.009 0.008 0.197 0.359 −0.185 P 0.132 0.038 0.352 0.603 0.617 0.315 0.929 0.963 0.970 0.393 0.110 0.303 Δ-ARNTL r −0.279 0.023 0.023 −0.091 0.184 −0.136 0.171 −0.166 −0.011 −0.398 −0.252 −0.252 P 0.122 0.909 0.905 0.620 0.314 0.457 0.349 0.364 0.958 0.074 0.270 0.157 Δ-ARNTL2 r −0.294 −0.071 −0.039 −0.222 −0.055 0.495g 0.060 −0.041 0.038 −0.493f −0.352 −0.182 P 0.109 0.724 0.840 0.230 0.768 0.005 0.749 0.826 0.857 0.023 0.118 0.318 Δ-CAMK2D r 0.186 −0.384f −0.209 0.003 0.271 −0.178 0.234 −0.400f 0.232 −0.615g −0.378 −0.053 P 0.317 0.048 0.276 0.985 0.141 0.339 0.205 0.026 0.265 0.004 0.100 0.775 Δ-CLOCK r −0.094 −0.088 0.017 0.017 0.334 −0.008 0.255 −0.196 0.062 −0.360 −0.253 −0.246 P 0.609 0.657 0.927 0.927 0.061 0.966 0.159 0.282 0.764 0.109 0.268 0.167 Δ-CREB1 r 0.067 −0.165 −0.098 0.021 0.364f −0.300 0.135 −0.119 −0.081 −0.191 −0.092 −0.003 P 0.715 0.403 0.607 0.907 0.040 0.095 0.462 0.518 0.695 0.407 0.690 0.986 Δ-CREB3 r 0.075 −0.200 −0.034 −0.180 0.060 −0.026 0.272 −0.082 0.189 −0.343 −0.303 −0.056 P 0.683 0.298 0.857 0.325 0.746 0.889 0.133 0.655 0.345 0.118 0.170 0.757 Δ-CSNK1A1 r −0.366f 0.507g 0.254 0.092 −0.110 0.199 −0.148 0.343 −0.197 0.585g 0.414 −0.108 P 0.040 0.006 0.175 0.615 0.549 0.276 0.418 0.055 0.335 0.005 0.062 0.550 Δ-CSNK1E r 0.018 0.258 −0.223 0.344 −0.082 0.137 0.139 −0.080 0.062 0.104 0.081 0.244 P 0.924 0.177 0.229 0.054 0.657 0.453 0.449 0.665 0.759 0.646 0.720 0.171 Δ-GUSB r −0.335 0.014 −0.041 −0.216 −0.112 −0.251 0.358f −0.310 0.275 −0.685g −0.565g −0.402* P 0.061 0.943 0.831 0.236 0.540 0.165 0.044 0.084 0.173 0.001 0.008 0.020 Δ-MAPK1 r −0.021 −0.293 −0.028 −0.161 0.184 −0.001 0.177 −0.219 0.078 −0.566g −0.449f −0.143 P 0.910 0.137 0.884 0.388 0.323 0.996 0.342 0.235 0.712 0.009 0.047 0.435 Δ-MAT2A r −0.255 0.439f 0.222 −0.051 0.081 −0.024 −0.032 0.072 −0.068 0.195 0.326 −0.098 P 0.158 0.019 0.238 0.780 0.661 0.894 0.860 0.694 0.742 0.396 0.149 0.586 Δ-ONP3 r 0.257 −0.352 −0.222 −0.002 0.041 0.059 0.581g −0.355f 0.312 −0.527f −0.475f −0.306 P 0.162 0.066 0.237 0.991 0.826 0.752 0.001 0.050 0.121 0.012 0.025 0.089 Δ-PER3 r 0.070 0.325 0.125 −0.019 0.289 −0.315 −0.280 0.327 −0.175 0.505f 0.443f 0.433f P 0.698 0.085 0.504 0.918 0.103 0.074 0.115 0.063 0.394 0.017 0.039 0.010 Δ-PRF1 r 0.093 −0.446f −0.268 −0.046 −0.042 0.044 0.489g −0.437f 0.442f −0.697g −0.691g −0.309 P 0.612 0.017 0.153 0.803 0.821 0.812 0.004 0.012 0.024 <0.001 0.001 0.080 Δ-PRKAR1A r 0.011 −0.239 −0.005 −0.168 0.223 0.175 0.313 −0.288 0.137 −0.427 −0.357 −0.187 P 0.952 0.220 0.977 0.358 0.219 0.337 0.081 0.110 0.503 0.054 0.112 0.297 Δ-PRKAR2A r −0.189 −0.163 −0.041 0.084 0.035 0.123 0.363f −0.308 0.143 −0.592g −0.420 −0.465g P 0.318 0.426 0.838 0.660 0.856 0.518 0.049 0.098 0.507 0.008 0.073 0.008 Δ-PRKCB r −0.111 0.108 0.266 0.262 0.185 0.114 0.202 −0.245 0.156 0.025 0.302 0.177 P 0.457 0.500 0.084 0.082 0.213 0.447 0.269 0.177 0.446 0.885 0.077 0.233 Δ-RORA r −0.252 −0.069 0.009 −0.092 0.099 0.088 0.100 −0.199 0.088 −0.504f −0.358 −0.243 P 0.163 0.728 0.961 0.615 0.590 0.631 0.587 0.275 0.670 0.020 0.111 0.172 Δ-SP1 r −0.017 0.236 0.214 −0.305 0.063 0.015 −0.323 0.350 −0.093 0.462f 0.659g 0.307 P 0.929 0.226 0.257 0.095 0.738 0.935 0.076 0.053 0.650 0.031 0.001 0.087 Δ-TIMELESS r 0.183 0.210 0.057 0.103 0.377f −0.329 −0.181 0.303 −0.186 0.587g 0.736g 0.408g P 0.317 0.284 0.764 0.574 0.034 0.066 0.321 0.092 0.363 0.005 <0.001 0.019 Δ-WEE1 r −0.105 −0.375f −0.144 0.180 −0.011 −0.013 0.583g −0.542g 0.334 −0.778g −0.645g −0.527g P 0.553 0.041 0.432 0.307 0.951 0.940 <0.001 0.001 0.096 <0.001 0.001 0.001 Gene Δ-BMI Δ-HbA1c Δ-High-Density Lipoprotein Δ-TGa Δ-SBPb Δ-DBPc Δ-CD16+ CD14− Cells Δ-CD14+ CD16− Cells Δ-CD16+ NK Cells Δ-ADAM17d Δ-CD16sd Δ-Infectionse Δ-AANAT r −0.272 0.394f 0.176 −0.096 −0.092 0.183 −0.016 −0.009 0.008 0.197 0.359 −0.185 P 0.132 0.038 0.352 0.603 0.617 0.315 0.929 0.963 0.970 0.393 0.110 0.303 Δ-ARNTL r −0.279 0.023 0.023 −0.091 0.184 −0.136 0.171 −0.166 −0.011 −0.398 −0.252 −0.252 P 0.122 0.909 0.905 0.620 0.314 0.457 0.349 0.364 0.958 0.074 0.270 0.157 Δ-ARNTL2 r −0.294 −0.071 −0.039 −0.222 −0.055 0.495g 0.060 −0.041 0.038 −0.493f −0.352 −0.182 P 0.109 0.724 0.840 0.230 0.768 0.005 0.749 0.826 0.857 0.023 0.118 0.318 Δ-CAMK2D r 0.186 −0.384f −0.209 0.003 0.271 −0.178 0.234 −0.400f 0.232 −0.615g −0.378 −0.053 P 0.317 0.048 0.276 0.985 0.141 0.339 0.205 0.026 0.265 0.004 0.100 0.775 Δ-CLOCK r −0.094 −0.088 0.017 0.017 0.334 −0.008 0.255 −0.196 0.062 −0.360 −0.253 −0.246 P 0.609 0.657 0.927 0.927 0.061 0.966 0.159 0.282 0.764 0.109 0.268 0.167 Δ-CREB1 r 0.067 −0.165 −0.098 0.021 0.364f −0.300 0.135 −0.119 −0.081 −0.191 −0.092 −0.003 P 0.715 0.403 0.607 0.907 0.040 0.095 0.462 0.518 0.695 0.407 0.690 0.986 Δ-CREB3 r 0.075 −0.200 −0.034 −0.180 0.060 −0.026 0.272 −0.082 0.189 −0.343 −0.303 −0.056 P 0.683 0.298 0.857 0.325 0.746 0.889 0.133 0.655 0.345 0.118 0.170 0.757 Δ-CSNK1A1 r −0.366f 0.507g 0.254 0.092 −0.110 0.199 −0.148 0.343 −0.197 0.585g 0.414 −0.108 P 0.040 0.006 0.175 0.615 0.549 0.276 0.418 0.055 0.335 0.005 0.062 0.550 Δ-CSNK1E r 0.018 0.258 −0.223 0.344 −0.082 0.137 0.139 −0.080 0.062 0.104 0.081 0.244 P 0.924 0.177 0.229 0.054 0.657 0.453 0.449 0.665 0.759 0.646 0.720 0.171 Δ-GUSB r −0.335 0.014 −0.041 −0.216 −0.112 −0.251 0.358f −0.310 0.275 −0.685g −0.565g −0.402* P 0.061 0.943 0.831 0.236 0.540 0.165 0.044 0.084 0.173 0.001 0.008 0.020 Δ-MAPK1 r −0.021 −0.293 −0.028 −0.161 0.184 −0.001 0.177 −0.219 0.078 −0.566g −0.449f −0.143 P 0.910 0.137 0.884 0.388 0.323 0.996 0.342 0.235 0.712 0.009 0.047 0.435 Δ-MAT2A r −0.255 0.439f 0.222 −0.051 0.081 −0.024 −0.032 0.072 −0.068 0.195 0.326 −0.098 P 0.158 0.019 0.238 0.780 0.661 0.894 0.860 0.694 0.742 0.396 0.149 0.586 Δ-ONP3 r 0.257 −0.352 −0.222 −0.002 0.041 0.059 0.581g −0.355f 0.312 −0.527f −0.475f −0.306 P 0.162 0.066 0.237 0.991 0.826 0.752 0.001 0.050 0.121 0.012 0.025 0.089 Δ-PER3 r 0.070 0.325 0.125 −0.019 0.289 −0.315 −0.280 0.327 −0.175 0.505f 0.443f 0.433f P 0.698 0.085 0.504 0.918 0.103 0.074 0.115 0.063 0.394 0.017 0.039 0.010 Δ-PRF1 r 0.093 −0.446f −0.268 −0.046 −0.042 0.044 0.489g −0.437f 0.442f −0.697g −0.691g −0.309 P 0.612 0.017 0.153 0.803 0.821 0.812 0.004 0.012 0.024 <0.001 0.001 0.080 Δ-PRKAR1A r 0.011 −0.239 −0.005 −0.168 0.223 0.175 0.313 −0.288 0.137 −0.427 −0.357 −0.187 P 0.952 0.220 0.977 0.358 0.219 0.337 0.081 0.110 0.503 0.054 0.112 0.297 Δ-PRKAR2A r −0.189 −0.163 −0.041 0.084 0.035 0.123 0.363f −0.308 0.143 −0.592g −0.420 −0.465g P 0.318 0.426 0.838 0.660 0.856 0.518 0.049 0.098 0.507 0.008 0.073 0.008 Δ-PRKCB r −0.111 0.108 0.266 0.262 0.185 0.114 0.202 −0.245 0.156 0.025 0.302 0.177 P 0.457 0.500 0.084 0.082 0.213 0.447 0.269 0.177 0.446 0.885 0.077 0.233 Δ-RORA r −0.252 −0.069 0.009 −0.092 0.099 0.088 0.100 −0.199 0.088 −0.504f −0.358 −0.243 P 0.163 0.728 0.961 0.615 0.590 0.631 0.587 0.275 0.670 0.020 0.111 0.172 Δ-SP1 r −0.017 0.236 0.214 −0.305 0.063 0.015 −0.323 0.350 −0.093 0.462f 0.659g 0.307 P 0.929 0.226 0.257 0.095 0.738 0.935 0.076 0.053 0.650 0.031 0.001 0.087 Δ-TIMELESS r 0.183 0.210 0.057 0.103 0.377f −0.329 −0.181 0.303 −0.186 0.587g 0.736g 0.408g P 0.317 0.284 0.764 0.574 0.034 0.066 0.321 0.092 0.363 0.005 <0.001 0.019 Δ-WEE1 r −0.105 −0.375f −0.144 0.180 −0.011 −0.013 0.583g −0.542g 0.334 −0.778g −0.645g −0.527g P 0.553 0.041 0.432 0.307 0.951 0.940 <0.001 0.001 0.096 <0.001 0.001 0.001 Boldface indicates statistical significance. a Triglycerides. b Systolic blood pressure. c Diastolic blood pressure. d Log10(ADAM17) and log10(CD16s). e Total infection score. f P < 0.05. g P < 0.01. View Large Discussion The DREAM trial was a head-to-head comparison of standard twice- or thrice-daily cortisone or hydrocortisone and once-daily modified-release hydrocortisone replacement therapy. We showed that patients with AI on standard replacement therapy exhibit unexpected abnormalities in circulating PBMCs, with more classic monocytes and a smaller number of CD16+ natural killer cells, that can be partially reversed by changing the timing of GC administration (6). In the current report we showed that the patients with AI on standard replacement therapy have an abnormal expression of clock-related genes in circulating blood cells that can be partially normalized by switching from a multiple-times-a-day to a once-daily modified release hydrocortisone administration. To our knowledge, this is the first report of a link between the chronopharmacology of GC administration, the expression of circadian genes in immune cells, and the metabolic outcomes in the context of a clinical trial Although the influence of GCs on immune trafficking and regulation is known (15), recent studies suggest that GC administration, or adrenalectomy, affects the expression of clock-related genes, and, in turn, the complex CLOCK/ARNLT can suppress GC receptor–induced transcriptional activity (16). The rhythm of peripheral CLOCK gene expression shifts the activity of the GC receptor out of phase to when GC peaks in blood (17), setting an additional feedback to prevent overexposure to GCs (16). Conversely, when the clock is downregulated, GC sensitivity is higher. The latter is consistent with the knowledge of more deleterious metabolic effects of GC administered late in the evening (18). The importance of time exposure as compared with dose exposure has been recently confirmed in animal studies showing that high peaks of GC are well tolerated as long as sufficiently long “off” intervals are preserved (19). In addition to metabolic regulation (20), canonical CLOCK components are also involved in immune modulation (21). Patients with AI, even when treated according to the best practice guidelines, have higher mortality and hospitalization rates, especially from infectious and cardiovascular diseases (8–12, 22, 23). The DREAM trial (6) confirmed previous observations (7) that patients with AI can suffer from defective innate immunity with an “exhausted phenotype” of natural killer cells. Clock gene dysregulation may lead to functional hypercortisolism or hypocortisolism in peripheral target tissues (24), highlighting the fact that estimating total cortisol exposure is more complex than just measuring circulating cortisol levels (25). In this study we found that, compared with adrenally sufficient controls, patients treated with standard GC therapy showed a significant downregulation of canonical CLOCK components, such as the CLOCK gene, which encodes for the Circadian Locomotor Output Cycles Kaput (CLOCK) protein and aryl hydrocarbon receptor nuclear translocator (ARNTL, BMAL), which could be restored to normal by altering the timing of GC administration. Moreover, the GC switch restored CSNK1A1 and PRF1 expression to levels correlated with the treatment-induced reduction in HbA1c, confirming the importance of the timing of GC administration for metabolic function. A recent study in mice showed that raising the peak of the GC oscillations up to 40-fold by injecting corticosteroids for 21 days produced no relevant increase in adipogenesis, as long as GC were given in the correct circadian periods, whereas losing the nadirs or “off periods” of GC administration produced a striking increase in adipogenesis (19). Interestingly, we found PER3 upregulated and correlated with inflammation in patients with AI. Recent evidence supports a prominent role for PER3 oscillation, as compared with PER1, in adipose tissue function (26). We found a significant downregulation of WEE1 in patients with AI at baseline, inversely related to BMI and triglycerides. Interestingly, WEE1 is the transcriptional factor that appears to coordinate the transition between DNA replication and mitosis by arresting G2 phase and inhibiting progression toward mitosis. Consequently, reduced WEE1 synthesis favors entry into mitosis and may even shorten its duration. Downregulated WEE1 has also been found in pituitary adenomas, suggesting a potential role in tumorigenesis of the loss of its protective function (27). WEE1 expression is controlled by CLOCK-ARNLT (28), and in our study it was restored in the switch treatment group after 12 weeks. Interestingly, the upregulation of WEE1 in the switched treatment group correlated with a reduction in glycated hemoglobin and inflammatory monocytes. The cAMP-responsive element-binding protein, known as CREB/CRE, plays a crucial role in several cell functions, including proliferation, survival, differentiation, adaptive responses, glucose homeostasis, spermatogenesis, synaptic plasticity associated with memory, and circadian rhythms (29). CREB is induced by a variety of growth factors and inflammatory signals; it can also promote anti-inflammatory immune responses, such as the inhibition of NF-kB activity, the induction of IL-10, and the generation of Treg, and promotes activation and proliferation of T and B lymphocytes (30). We found a significant downregulation in most cAMP downstream targets (CREB1, PRKAR1A, and PRKAR2A) in patients with AI compared with controls, a finding consistent with the recurrent infections seen in patients with AI. Accordingly, PRF1, the predominant cytolytic protein secreted by natural killer cells (31), was also found downregulated in patients with AI compared with controls. Of note is that PRF1 null mice exhibit increased body weight and adiposity, glucose intolerance, and insulin resistance caused by an M1-polarization of macrophages infiltrating visceral adipose tissue (32). We found that restoration of PRF1 to control levels in patients with AI in the switched treatment group correlated with a reduction in HbA1c. Taken together, our findings on PRF1 and PER3 modulation support a role for adipocyte dysfunction in explaining the metabolic impairment and low-grade inflammation observed in patients with AI on multiple-times-a-day GC treatment. The latter could also explain the higher risk of atherosclerosis in these patients in the absence of fat accumulation (33). An upregulated expression of AANAT was also observed in patients with AI at baseline, with a significant reduction in the switched treatment group at 12 weeks. AANAT encodes for arylalkylamine N-acetyltransferase, also known as the “Timenzyme,” which controls daily changes in melatonin production by the pineal gland. AANAT is also expressed in the retina, where it may play other roles, including neurotransmission and detoxification (34). Melatonin peaks at night, swiftly decreasing in the morning after light exposure; AANAT follows the same pattern. We found ANAAT overexpressed in the morning in patients with AI under standard therapy (measured 2 hours after awakening). The lack of response (i.e., expression decrease) to a strong zeitgeber, such as daylight, observed in our patients provides insights on the detrimental effect of a nonphysiologic GC profile on the entire circadian machinery (35). The altered expressions of selected genes with a pivotal role in metabolism and innate immunity were all reversed to near normal when patients with AI switched from the standard multiple-times-a-day regimen to the once-daily modified-release hydrocortisone. Although the ETD between the two regimens could be artifactual and not necessarily linked to a clinical outcome, the fact that the postswitched gene analysis was more similar to that of controls (who are not taking exogenous GCs) suggest that the more physiologic replacement is the main cause for the entrainment of circadian genes. Finally, the correlation between the change in clinical variables (glycated hemoglobin, infection score, sADAM17, and sCD16) and the modulation in gene expression profile suggests that CAMK2D, CSNK1A1, GUSB, ONP3, PER3, PRF1, SP1, TIMELESS, and WEE1 are causally linked to the clinical outcomes observed in the main DREAM trial report (6). The current study has several advantages but also some limitations. Advantages include the random allocation, blinding of the assessors, strict inclusion criteria, non-crossover design, high number of circadian genes simultaneously evaluated in both nonpooled and pooled samples, and inclusion of a control group. The main limitation was the single-time evaluation for circadian gene expression. However, the presence of a control group, the standardization of timing and type of therapy, and the modality of sample collection increase the value of our results. Another limitation is that the two regimens can lead to a different total GC exposure, and some of the effects occur via GC-mediated activation of the mineralocorticoid receptor in monocytes (36, 37). A third limitation is that our study did not include protein analysis, requiring an abundant source material difficult to store in the context of a clinical trial, thus limiting functional relevance of the observed findings. Finally, some of the differences in expression of some genes observed in patients with AI could be related to the change in PBMC populations. To evaluate this aspect, we normalized gene expression through housekeeping genes and performed a pooled analysis on the T lymphocyte subset that remained stable among study groups throughout the trial. However, such analyses cannot be considered conclusive, and to address this complex biological bias newly designed studies are necessary, such as the use of single-cell approach analysis. In conclusion, cortisol acts as crucial synchronizer of the expression of several circadian genes. In AI, the multiple-times-a-day administration of GCs, as occurs with the standard replacement schemes, causes a desynchronization of the endogenous and exogenous zeitgebers that can be measured as a flattening of the oscillator in PBMCs (Supplemental Fig. 5). Switching to a once-daily regimen allows better entrainment of exogenous administration and endogenous clocks, particularly to CLOCK/BMAL- and CREB-related genes. The resynchronization correlates with clinical improvement. This study shows that assessment of the peripheral circadian oscillators in PBMCs offers a tool to elucidate clinical disorders related to circadian rhythm disruption, such as metabolic syndrome and malignancies in night-shift workers. A deeper knowledge of the role of the molecular clock misalignment in adrenal disorders will enable development of better treatment strategies. Abbreviations: Abbreviations: AI adrenal insufficiency ANCOVA analysis of covariance BMI body mass index DREAM Dual Release Hydrocortisone vs Conventional Glucocorticoid Replacement in Hypocortisolism ETD estimated treatment difference GC glucocorticoid PBMC peripheral blood mononuclear cell Acknowledgments The authors thank Letizia Ciccone for technical assistance in gene expression analysis. Financial Support: The study was funded by the Ministry of Education, University and Research Grants 2015ZTT5KB and RBAP109BLT (to A.M.I.).The funder of the study had no role in the study design, data collection, data analysis, data interpretation, or writing of the report. The funder had no role in the decision to submit for publication. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication. Clinical Trial Information: ClinicalTrials.gov no. NCT02277587 (registered 29 October 2014). Disclosure Summary: A.M.I. reports grants and personal fees from Shire and Novartis, personal fees from Otsuka and Menarini, and personal fees and nonfinancial support from Ipsen, beyond the confines of this work. R.P. reports grants and personal fees from Novartis, Pfizer, HRA Pharma, Shire, and Ipsen and personal fees from Ferring and Italfarmaco, beyond the confines of this work. A.L. reports personal fees from MSD, Novartis, Shire, Novo Nordisk, and Aegerion, beyond the confines of this work. A.B.G. confirms lecture fees and grants from Shire and Novartis, beyond the confines of this work. The remaining authors have nothing to disclose. References 1. Chrousos GP . Stress and disorders of the stress system . Nat Rev Endocrinol . 2009 ; 5 ( 7 ): 374 – 381 . Google Scholar CrossRef Search ADS PubMed 2. Scheiermann C , Kunisaki Y , Frenette PS . Circadian control of the immune system . Nat Rev Immunol . 2013 ; 13 ( 3 ): 190 – 198 . Google Scholar CrossRef Search ADS PubMed 3. Balsalobre A , Brown SA , Marcacci L , Tronche F , Kellendonk C , Reichardt HM , Schütz G , Schibler U . Resetting of circadian time in peripheral tissues by glucocorticoid signaling . Science . 2000 ; 289 ( 5488 ): 2344 – 2347 . Google Scholar CrossRef Search ADS PubMed 4. Johannsson G , Nilsson AG , Bergthorsdottir R , Burman P , Dahlqvist P , Ekman B , Engström BE , Olsson T , Ragnarsson O , Ryberg M , Wahlberg J , Biller BM , Monson JP , Stewart PM , Lennernäs H , Skrtic S . Improved cortisol exposure-time profile and outcome in patients with adrenal insufficiency: a prospective randomized trial of a novel hydrocortisone dual-release formulation . J Clin Endocrinol Metab . 2012 ; 97 ( 2 ): 473 – 481 . Google Scholar CrossRef Search ADS PubMed 5. Quinkler M , Miodini Nilsen R , Zopf K , Ventz M , Oksnes M. Modified-release hydrocortisone decreases BMI and HbA1c in patients with primary and secondary adrenal insufficiency . Eur J Endocrinol . 2015 ; 172 : 619 – 626 . Google Scholar CrossRef Search ADS PubMed 6. Isidori AM , Venneri MA , Graziadio C , Simeoli C , Fiore D , Hasenmajer V , Sbardella E , Gianfrilli D , Pozza C , Pasqualetti P , Morrone S , Santoni A , Naro F , Colao A , Pivonello R , Lenzi A . Effect of once-daily, modified-release hydrocortisone versus standard glucocorticoid therapy on metabolism and innate immunity in patients with adrenal insufficiency (DREAM): a single-blind, randomised controlled trial . Lancet Diabetes Endocrinol . 2018 ; 6 ( 3 ): 173 – 185 . Google Scholar CrossRef Search ADS PubMed 7. Bancos I , Hazeldine J , Chortis V , Hampson P , Taylor AE , Lord JM , Arlt W . Primary adrenal insufficiency is associated with impaired natural killer cell function: a potential link to increased mortality . Eur J Endocrinol . 2017 ; 176 : 471 – 480 . Google Scholar CrossRef Search ADS PubMed 8. Erichsen MM , Lovas K , Fougner KJ , Svartberg J , Hauge ER , Bollerslev J , Berg JP , Mella B , Husebye ES . Normal overall mortality rate in Addison’s disease, but young patients are at risk of premature death . Eur J Endocrinol . 2009 ; 160 : 233 – 237 . Google Scholar CrossRef Search ADS PubMed 9. Björnsdottir S , Sundström A , Ludvigsson JF , Blomqvist P , Kämpe O , Bensing S . Drug prescription patterns in patients with Addison’s disease: a Swedish population-based cohort study . J Clin Endocrinol Metab . 2013 ; 98 ( 5 ): 2009 – 2018 . Google Scholar CrossRef Search ADS PubMed 10. Burman P , Mattsson AF , Johannsson G , Höybye C , Holmer H , Dahlqvist P , Berinder K , Engström BE , Ekman B , Erfurth EM , Svensson J , Wahlberg J , Karlsson FA . Deaths among adult patients with hypopituitarism: hypocortisolism during acute stress, and de novo malignant brain tumors contribute to an increased mortality . J Clin Endocrinol Metab . 2013 ; 98 ( 4 ): 1466 – 1475 . Google Scholar CrossRef Search ADS PubMed 11. Smans LC , Souverein PC , Leufkens HG , Hoepelman AI , Zelissen PM . Increased use of antimicrobial agents and hospital admission for infections in patients with primary adrenal insufficiency: a cohort study . Eur J Endocrinol . 2013 ; 168 : 609 – 614 . Google Scholar CrossRef Search ADS PubMed 12. Hahner S , Spinnler C , Fassnacht M , Burger-Stritt S , Lang K , Milovanovic D , Beuschlein F , Willenberg HS , Quinkler M , Allolio B . High incidence of adrenal crisis in educated patients with chronic adrenal insufficiency: a prospective study . J Clin Endocrinol Metab . 2015 ; 100 ( 2 ): 407 – 416 . Google Scholar CrossRef Search ADS PubMed 13. McVeigh TP , Sweeney KJ , Kerin MJ , Gallagher DJ . A qualitative analysis of the attitudes of Irish patients towards participation in genetic-based research . Ir J Med Sci . 2016 ; 185 ( 4 ): 825 – 831 . Google Scholar CrossRef Search ADS PubMed 14. Storey JD , Tibshirani R . Statistical significance for genomewide studies . Proc Natl Acad Sci USA . 2003 ; 100 ( 16 ): 9440 – 9445 . Google Scholar CrossRef Search ADS PubMed 15. Geiger AM , Pitts KP , Feldkamp J , Kirschbaum C , Wolf JM . Cortisol-dependent stress effects on cell distribution in healthy individuals and individuals suffering from chronic adrenal insufficiency . Brain Behav Immun . 2015 ; 50 : 241 – 248 . Google Scholar CrossRef Search ADS PubMed 16. Nader N , Chrousos GP , Kino T . Circadian rhythm transcription factor CLOCK regulates the transcriptional activity of the glucocorticoid receptor by acetylating its hinge region lysine cluster: potential physiological implications . FASEB J . 2009 ; 23 ( 5 ): 1572 – 1583 . Google Scholar CrossRef Search ADS PubMed 17. Nader N , Chrousos GP , Kino T . Interactions of the circadian CLOCK system and the HPA axis . Trends Endocrinol Metab . 2010 ; 21 ( 5 ): 277 – 286 . Google Scholar CrossRef Search ADS PubMed 18. Plat L , Leproult R , L’Hermite-Baleriaux M , Fery F , Mockel J , Polonsky KS , Van Cauter E . Metabolic effects of short-term elevations of plasma cortisol are more pronounced in the evening than in the morning . J Clin Endocrinol Metab . 1999 ; 84 ( 9 ): 3082 – 3092 . Google Scholar PubMed 19. Bahrami-Nejad Z , Zhao ML , Tholen S , Hunerdosse D , Tkach KE , van Schie S , Chung M , Teruel MN . A transcriptional circuit filters oscillating circadian hormonal inputs to regulate fat cell differentiation . Cell Metab . 2018 ; 27 ( 4 ): 854 – 868.e8 . Google Scholar CrossRef Search ADS PubMed 20. Lin E , Kuo PH , Liu YL , Yang AC , Kao CF , Tsai SJ . Effects of circadian clock genes and health-related behavior on metabolic syndrome in a Taiwanese population: evidence from association and interaction analysis . PLoS One . 2017 ; 12 ( 3 ): e0173861 . Google Scholar CrossRef Search ADS PubMed 21. O’Neill JS , Reddy AB . Circadian clocks in human red blood cells . Nature . 2011 ; 469 ( 7331 ): 498 – 503 . Google Scholar CrossRef Search ADS PubMed 22. Bergthorsdottir R , Leonsson-Zachrisson M , Odén A , Johannsson G . Premature mortality in patients with Addison’s disease: a population-based study . J Clin Endocrinol Metab . 2006 ; 91 ( 12 ): 4849 – 4853 . Google Scholar CrossRef Search ADS PubMed 23. Quinkler M , Ekman B , Zhang P , Isidori AM , Murray RD ; EU-AIR Investigators . Mortality data from the European Adrenal Insufficiency Registry: patient characterization and associations [published online ahead of print April 22, 2018]. Clin Endocrinol (Oxf). doi: 10.1111/cen.13609 . 24. Shostak A , Meyer-Kovac J , Oster H . Circadian regulation of lipid mobilization in white adipose tissues . Diabetes . 2013 ; 62 ( 7 ): 2195 – 2203 . Google Scholar CrossRef Search ADS PubMed 25. Morgan SA , McCabe EL , Gathercole LL , Hassan-Smith ZK , Larner DP , Bujalska IJ , Stewart PM , Tomlinson JW , Lavery GG . 11β-HSD1 is the major regulator of the tissue-specific effects of circulating glucocorticoid excess . Proc Natl Acad Sci USA . 2014 ; 111 ( 24 ): E2482 – E2491 . Google Scholar CrossRef Search ADS PubMed 26. Aggarwal A , Costa MJ , Rivero-Gutiérrez B , Ji L , Morgan SL , Feldman BJ . The circadian clock regulates adipogenesis by a Per3 crosstalk pathway to Klf15 . Cell Reports . 2017 ; 21 ( 9 ): 2367 – 2375 . Google Scholar CrossRef Search ADS PubMed 27. Butz H , Likó I , Czirják S , Igaz P , Khan MM , Zivkovic V , Bálint K , Korbonits M , Rácz K , Patócs A . Down-regulation of Wee1 kinase by a specific subset of microRNA in human sporadic pituitary adenomas . J Clin Endocrinol Metab . 2010 ; 95 ( 10 ): E181 – E191 . Google Scholar CrossRef Search ADS PubMed 28. Gérard C , Goldbeter A . Entrainment of the mammalian cell cycle by the circadian clock: modeling two coupled cellular rhythms . PLOS Comput Biol . 2012 ; 8 ( 5 ): e1002516 . Google Scholar CrossRef Search ADS PubMed 29. Mayr B , Montminy M . Transcriptional regulation by the phosphorylation-dependent factor CREB . Nat Rev Mol Cell Biol . 2001 ; 2 ( 8 ): 599 – 609 . Google Scholar CrossRef Search ADS PubMed 30. Wen AY , Sakamoto KM , Miller LS . The role of the transcription factor CREB in immune function . J Immunol . 2010 ; 185 ( 11 ): 6413 – 6419 . Google Scholar CrossRef Search ADS PubMed 31. Baran K , Dunstone M , Chia J , Ciccone A , Browne KA , Clarke CJ , Lukoyanova N , Saibil H , Whisstock JC , Voskoboinik I , Trapani JA . The molecular basis for perforin oligomerization and transmembrane pore assembly . Immunity . 2009 ; 30 ( 5 ): 684 – 695 . Google Scholar CrossRef Search ADS PubMed 32. Revelo XS , Tsai S , Lei H , Luck H , Ghazarian M , Tsui H , Shi SY , Schroer S , Luk CT , Lin GH , Mak TW , Woo M , Winer S , Winer DA . Perforin is a novel immune regulator of obesity-related insulin resistance . Diabetes . 2015 ; 64 ( 1 ): 90 – 103 . Google Scholar CrossRef Search ADS PubMed 33. Bergthorsdottir R , Ragnarsson O , Skrtic S , Glad CAM , Nilsson S , Ross IL , Leonsson-Zachrisson M , Johannsson G . Visceral fat and novel biomarkers of cardiovascular disease in patients with Addison’s disease: a case-control study . J Clin Endocrinol Metab . 2017 ; 102 ( 11 ): 4264 – 4272 . Google Scholar CrossRef Search ADS PubMed 34. Tosini G , Chaurasia SS , Michael Iuvone P . Regulation of arylalkylamine N-acetyltransferase (AANAT) in the retina . Chronobiol Int . 2006 ; 23 ( 1–2 ): 381 – 391 . Google Scholar CrossRef Search ADS PubMed 35. Klein DC . Arylalkylamine N-acetyltransferase: “the Timezyme.” J Biol Chem . 2007 ; 282 ( 7 ): 4233 – 4237 . Google Scholar CrossRef Search ADS PubMed 36. Marzolla V , Armani A , Feraco A , De Martino MU , Fabbri A , Rosano G , Caprio M . Mineralocorticoid receptor in adipocytes and macrophages: a promising target to fight metabolic syndrome . Steroids . 2014 ; 91 : 46 – 53 . Google Scholar CrossRef Search ADS PubMed 37. Usher MG , Duan SZ , Ivaschenko CY , Frieler RA , Berger S , Schütz G , Lumeng CN , Mortensen RM . Myeloid mineralocorticoid receptor controls macrophage polarization and cardiovascular hypertrophy and remodeling in mice . J Clin Invest . 2010 ; 120 ( 9 ): 3350 – 3364 . Google Scholar CrossRef Search ADS PubMed Copyright © 2018 Endocrine Society http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Clinical Endocrinology and Metabolism Oxford University Press

Circadian Rhythm of Glucocorticoid Administration Entrains Clock Genes in Immune Cells: A DREAM Trial Ancillary Study

Loading next page...
 
/lp/ou_press/circadian-rhythm-of-glucocorticoid-administration-entrains-clock-genes-M56uHJc1Cy
Publisher
Oxford University Press
Copyright
Copyright © 2018 Endocrine Society
ISSN
0021-972X
eISSN
1945-7197
D.O.I.
10.1210/jc.2018-00346
Publisher site
See Article on Publisher Site

Abstract

Abstract Context Adrenal insufficiency (AI) requires lifelong glucocorticoid (GC) replacement. Conventional therapies do not mimic the endogenous cortisol circadian rhythm. Clock genes are essential components of the machinery controlling circadian functions and are influenced by GCs. However, clock gene expression has never been investigated in patients with AI. Objective To evaluate the effect of the timing of GC administration on circadian gene expression in peripheral blood mononuclear cells (PBMCs) of patients from the Dual Release Hydrocortisone vs Conventional Glucocorticoid Replacement in Hypocortisolism (DREAM) trial. Design Outcome assessor–blinded, randomized, active comparator clinical trial. Participants and Intervention Eighty-nine patients with AI were randomly assigned to continue their multiple daily GC doses or switch to an equivalent dose of once-daily modified-release hydrocortisone and were compared with 25 healthy controls; 65 patients with AI and 18 controls consented to gene expression analysis. Results Compared with healthy controls, 19 of the 68 genes were found modulated in patients with AI at baseline, 18 of which were restored to control levels 12 weeks after therapy was switched: ARNTL [BMAL] (P = 0.024), CLOCK (P = 0.016), AANAT (P = 0.021), CREB1 (P = 0.010), CREB3 (P = 0.037), MAT2A (P = 0.013); PRKAR1A, PRKAR2A, and PRKCB (all P < 0.010) and PER3, TIMELESS, CAMK2D, MAPK1, SP1, WEE1, CSNK1A1, ONP3, and PRF1 (all P < 0.001). Changes in WEE1, PRF1, and PER3 expression correlated with glycated hemoglobin, inflammatory monocytes, and CD16+ natural killer cells. Conclusions Patients with AI on standard therapy exhibit a dysregulation of circadian genes in PBMCs. The once-daily administration reconditions peripheral tissue gene expression to levels close to controls, paralleling the clinical outcomes of the DREAM trial (NCT02277587). Endogenous cortisol levels are tightly regulated and fluctuate in a circadian fashion, influencing the mRNA expression of ≥20% of the expressed genome, including that of the immune cells (1). Most hematopoietic cells circulating in peripheral blood exhibit a circadian rhythmicity that is inverse to that of cortisol, with a peak during night rest and a nadir during daily activity. This pattern is the net balance of release from the hematopoietic niche and extravasation to peripheral tissues and is regulated by clock-controlled gene expression of bone marrow–stimulating factors, endothelial adhesion molecules, and migratory cytokines. The circadian control of immune cells, both via intrinsic local mechanisms and via cortisol fluctuations, allows the organism to anticipate daily changes in activity, when the risk of antigen exposure is higher, and favors repair at night when the risk is lower (2). However, the pharmacokinetics of standard oral glucocorticoid (GC) replacement therapies make it impossible to precisely mimic cortisol’s physiologic circadian rhythm. The nonphysiologic multiple peaks and troughs of cortisol levels occurring with the immediate-release hydrocortisone distributed during the day may disrupt peripheral clock machinery, because cortisol acts as a robust endogenous zeitgeber synchronizing the central and peripheral clocks in many tissues (3). A once-daily modified-release hydrocortisone formulation has been developed combining an immediate-release coating with an extended-release core that avoids the multiple peaks and troughs of standard therapies, providing a more physiologic cortisol rhythm (4). Previous studies have shown that this formulation can improve cardiovascular risk factors, glucose metabolism, and quality of life (4, 5). The recent Dual Release Hydrocortisone vs Conventional Glucocorticoid Replacement in Hypocortisolism (DREAM) trial showed that patients with adrenal insufficiency (AI) have an altered immune profile with an atypical inflammation characterized by more classic monocytes and impaired innate immune responses related to a shedding of CD16 from natural killer cells (6). Evidence of immune function dysregulation was already known from the report by Bancos et al. (7), consistent with epidemiological data describing frequent infections in patients with AI (8–12). The DREAM trial revealed that patients randomly assigned to receive once-daily modified-release hydrocortisone therapy (“switch” treatment group) had an improved circulating immune cell profile and inflammatory status and a lowered number of infections compared with subjects on standard multiple-times-a-day GC (6). The peculiar clinical and molecular findings of the DREAM trial, and the time course of metabolic and immune changes, suggested that they were probably the results of a modification in the circadian cortisol rhythm, but a formal demonstration requires analysis of the expression of circadian genes. It is indeed known that GCs can acutely alter the oscillation of several clock-related genes by phase shifting their expression in peripheral tissues (acute stressor); however, whether the timing of GC administration delivered chronically affects clock gene expression in patients with AI has never been investigated. In the population of the DREAM trial, we have therefore tested whether: the morning expression of circadian genes in peripheral blood mononuclear cells (PBMCs) is altered in patients with AI compared with controls; whether the observed proinflammatory state and weakened defense of patients with AI receiving conventional GC replacement therapy can be related to a dysregulation of circadian gene expression; whether the “broken clock” can be recovered by switching to a more physiologic timing of GC administration; and whether restoration of clock gene expression correlated with clinical outcomes. Methods Study design and participants The rationale, design, inclusion and exclusion criteria, and results of the DREAM trial have been extensively reported elsewhere (6). Briefly, DREAM was a randomized, two-arm, outcome assessor–blinded (independent), active comparator, controlled clinical trial enrolling 89 patients with AI and 25 adrenally sufficient age-, sex-, and body mass index (BMI)–matched controls. Patients with AI were randomly assigned to either continue usual multiple daily doses of conventional GCs (standard treatment group) or switch to an equivalent dose of once-daily modified-release hydrocortisone (switch treatment group), and 25 controls were assigned to nonintervention. Patients allocated to once-daily, modified-release hydrocortisone (Plenadren®; Shire, Brussels, Belgium) were instructed to take the dose on waking, before leaving bed. Patients previously on multiple doses of hydrocortisone a day received the same total daily dose, whereas patients previously on cortisone received 0.8 mg of hydrocortisone per 1 mg of cortisone. All patients provided written informed consent, and the trial was approved by the local review board at Sapienza University, conducted in accordance with the Declaration of Helsinki, and performed between March 2014 and June 2016. For the current analyses (tertiary endpoint of the DREAM trial), only patients providing an additional informed consent for gene expression profiling were included. Of the 89 enrolled patients with AI, 65 patients (73%) provided consent to gene analysis, along with 18 of the 25 (72%) adrenally sufficient controls. The acceptance rate to donate tissue for genetic research was consistent with current trends (13). All participants underwent blood sampling in the morning between 8:00 am and 9:00 am, after an overnight fast (patients had to take their usual morning dose 2 hours before blood sampling) for immunophenotyping of PBMCs, as previously described (6). For 2 weeks before the start of the investigation, all study participants maintained stable sleep schedules, including a single 8-hour nighttime sleep episode and restricting naps. The prestudy sleep schedule was based on participants’ reported habitual sleep times and durations. For all participants, habitual sleep durations reported during the recruitment phase ranged between 7 and 9 hours. RNA extraction and circadian gene quantification PBMCs were freshly isolated from whole blood via Ficoll-Hypaque density gradient centrifugation. RNA was extracted with an Aurum Total RNA Mini Kit (Bio-Rad, Hercules, CA) followed by a DNase digestion step to remove genomic DNA contamination. Total RNA concentration was quantified with a Nanodrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA) and purity estimated by 260 nm/280 nm absorption. Of the 83 patients consenting to gene expression profiling, 26 patients with AI of the switch treatment group, 29 patients with AI of the standard treatment group, and 16 controls met the sample quality criteria (absorbance 260/280 ratios between 1.9 and 2.2, RNA concentrations ≥20 µg/mL, with integrity assessed by gel electrophoresis) (Fig. 1). Figure 1. View largeDownload slide Trial profile. OD, once daily. Figure 1. View largeDownload slide Trial profile. OD, once daily. We reverse transcribed 2 μg of RNA from each sample by using iScript Reverse Transcription Kit (Bio-Rad). The total cDNA pool obtained served as the template for subsequent PCR amplification in a real-time PCR assay predesigned 96-well panel for use with SYBR® Green circadian rhythms (SAB target list) H96 (PrimePCR®; Bio-Rad). PrimePCR® is a preoptimized assay designed to guarantee high assay specificity, compatibility, avoidance of secondary structures, primer annealing sites, and snips in the target region, maximized detection of transcript isoforms, fully validated for the human genome, which we found particularly indicated for clinical trials because it is easily reproducible and yields comparable data. Primers (including housekeeping genes) were lyophilized in each well, through the use of SsoAdvancedTM Universal SYBR® Green Supermix. The quantitative reverse transcription polymerase chain reaction was run on CFX Connect (Bio-Rad). In addition to the 84 genes tested, each plate contained housekeeping genes for quantitative analyses (GAPDH, ACTB, HPRT1, B2M) and specific controls for genomic DNA contamination, RNA quality, and efficiency. Only assays that passed internal controls were included in the database. For data analysis, the Cq expression of housekeeping genes was tested by CFX Manager™ software (Bio-Rad) to identify the most stable reference gene based on geometric mean of expression. Most of the reference genes passed the test, with GAPDH and ACTB being the most stably expressed among samples and thus selected for normalization. All gene expression results are expressed as relative expression level normalized against housekeeping genes. Statistical analysis The statistical plan of the study has been previously reported (6), and the full prespecified plan is available online (https://web.uniroma1.it/dip_dms/ricerca/trials-clinici). Briefly, efficacy analyses were based on an intention-to-treat approach. Normality of distribution was assessed by Shapiro-Wilk test. The estimated treatment differences (ETDs) in the change from baseline to week 12 were analyzed with an analysis of covariance (ANCOVA) model that included baseline outcome as a covariate and treatment as a fixed effect. Additional covariates included were sex, BMI, age, smoking, type and duration of AI, diabetes mellitus, and white blood cell count. The ANCOVA model used the last-observation-carried-forward principle and provided the least squares mean estimates, with 95% CI adjusted for multiple comparisons. Standardized residuals were tested for normality via Shapiro-Wilk test. Homoscedasticity and homogeneity of variances were assessed by visual inspection and Levene test. Multicollinearity was assessed by a variance inflation factor. Two levels of evidence were required for investigated genes to be considered clinically relevant: a differential expression at baseline between patients with AI and controls, and a significant ETD between randomization groups. Subgroup analysis was carried out reporting the significance of the treatment by subgroup interaction. Because of the risk of false discoveries in multiple testing, adjusted P values were also calculated for ETD through the modified Benjamini-Hochberg approach (14), with a value of <0.05 regarded as significant. The study was registered at clinicaltrials.gov with identifier NCT02277587. Results Overall, 83 subjects consented to gene testing, and 71 had a full gene expression analysis carried out on PBMCs freshly isolated in the morning at baseline and 12 weeks after randomization: 29 patients with AI assigned to the standard treatment group, 26 patients with AI to the switch treatment group, and 16 healthy controls to nonintervention follow-up (Fig. 1). Baseline characteristics were comparable between AI groups (switch vs standard), whereas controls had lower BMI and lipid levels (total/high-density lipoprotein-cholesterol and triglycerides) (Table 1). The total daily dose of GCs was well balanced between patient groups at randomization and was not different at study end. Gene expression data at baseline are shown in Supplemental Table 1 and Supplemental Fig. 1. The clinical features of all patients with AI enrolled in the main trial, those who consented to gene expression profiling, and those who passed quality control for gene analysis were similar, except for a higher prevalence of primary AI in the group consenting to the analysis, and consequently a lower BMI, compared with those who did not, but there were no other differences in the metabolic, immune, or infection data, suggesting that the current subgroup is representative of the outcomes of the main trial (Supplemental Table 2). Table 1. Baseline Characteristics of the Entire Set With Full Gene Expression Analysis Adrenally Sufficienta Adrenally Insufficienta Controls Total Randomly Assigned to Switch Treatment Randomly Assigned to Standard Treatment Number 16 55 26 29 Clinical featuresa  Age, y 41 (33–49) 48 (44–51) 46 (40–51) 50 (45–55)  Sex, F/M 7/9 31/24 15/11 16/13  Primary/secondary AI — 33/22 16/10 17/12  AI duration, mo — 48 (24–132) 48 (24–132) 48 (12–132) Comorbidities  Diabetes mellitus 0 (0%) 8 (15%) 3 (12%) 5 (17%)  Other autoimmune disorders 0 (0%) 15 (27%) 7 (27%) 8 (28%)  Pituitary tumor or surgery 0 (0%) 14 (26%) 8 (30%) 6 (21%)  Other hypothalamic-pituitary failure 0 (0%) 3 (5%) 1 (4%) 2 (7%)  Adrenalectomy 0 (0%) 4 (7%) 2 (8%) 2 (7%) GC replacement  Hydrocortisone — 20 (36%) 12 (46%) 8 (28%)  OD/BID/TID 0/16/4 0/11/1 0/5/3  Cortisone acetate — 35 (64%) 14 (54%) 21 (72%)  OD/BID/TID 0/34/1 0/13/1 0/21/0  Baseline equivalent dose (mg/m2/24 h) — 17 (16–19) 16 (14–18) 17 (15–20)  BMI (kg/m2) 23 (22–24) 25 (24–27) 25 (23–27) 26 (24–28) Adrenally Sufficienta Adrenally Insufficienta Controls Total Randomly Assigned to Switch Treatment Randomly Assigned to Standard Treatment Number 16 55 26 29 Clinical featuresa  Age, y 41 (33–49) 48 (44–51) 46 (40–51) 50 (45–55)  Sex, F/M 7/9 31/24 15/11 16/13  Primary/secondary AI — 33/22 16/10 17/12  AI duration, mo — 48 (24–132) 48 (24–132) 48 (12–132) Comorbidities  Diabetes mellitus 0 (0%) 8 (15%) 3 (12%) 5 (17%)  Other autoimmune disorders 0 (0%) 15 (27%) 7 (27%) 8 (28%)  Pituitary tumor or surgery 0 (0%) 14 (26%) 8 (30%) 6 (21%)  Other hypothalamic-pituitary failure 0 (0%) 3 (5%) 1 (4%) 2 (7%)  Adrenalectomy 0 (0%) 4 (7%) 2 (8%) 2 (7%) GC replacement  Hydrocortisone — 20 (36%) 12 (46%) 8 (28%)  OD/BID/TID 0/16/4 0/11/1 0/5/3  Cortisone acetate — 35 (64%) 14 (54%) 21 (72%)  OD/BID/TID 0/34/1 0/13/1 0/21/0  Baseline equivalent dose (mg/m2/24 h) — 17 (16–19) 16 (14–18) 17 (15–20)  BMI (kg/m2) 23 (22–24) 25 (24–27) 25 (23–27) 26 (24–28) Gene expression data at baseline in the two groups are reported in Supplemental Table 1. Abbreviations: BID, two times daily; OD, once daily; TID, three times daily. a Values are expressed as mean (lower–upper limit of 95% CI), median (25th; 75th percentile), count (n), or percentages (%) as appropriate. View Large Table 1. Baseline Characteristics of the Entire Set With Full Gene Expression Analysis Adrenally Sufficienta Adrenally Insufficienta Controls Total Randomly Assigned to Switch Treatment Randomly Assigned to Standard Treatment Number 16 55 26 29 Clinical featuresa  Age, y 41 (33–49) 48 (44–51) 46 (40–51) 50 (45–55)  Sex, F/M 7/9 31/24 15/11 16/13  Primary/secondary AI — 33/22 16/10 17/12  AI duration, mo — 48 (24–132) 48 (24–132) 48 (12–132) Comorbidities  Diabetes mellitus 0 (0%) 8 (15%) 3 (12%) 5 (17%)  Other autoimmune disorders 0 (0%) 15 (27%) 7 (27%) 8 (28%)  Pituitary tumor or surgery 0 (0%) 14 (26%) 8 (30%) 6 (21%)  Other hypothalamic-pituitary failure 0 (0%) 3 (5%) 1 (4%) 2 (7%)  Adrenalectomy 0 (0%) 4 (7%) 2 (8%) 2 (7%) GC replacement  Hydrocortisone — 20 (36%) 12 (46%) 8 (28%)  OD/BID/TID 0/16/4 0/11/1 0/5/3  Cortisone acetate — 35 (64%) 14 (54%) 21 (72%)  OD/BID/TID 0/34/1 0/13/1 0/21/0  Baseline equivalent dose (mg/m2/24 h) — 17 (16–19) 16 (14–18) 17 (15–20)  BMI (kg/m2) 23 (22–24) 25 (24–27) 25 (23–27) 26 (24–28) Adrenally Sufficienta Adrenally Insufficienta Controls Total Randomly Assigned to Switch Treatment Randomly Assigned to Standard Treatment Number 16 55 26 29 Clinical featuresa  Age, y 41 (33–49) 48 (44–51) 46 (40–51) 50 (45–55)  Sex, F/M 7/9 31/24 15/11 16/13  Primary/secondary AI — 33/22 16/10 17/12  AI duration, mo — 48 (24–132) 48 (24–132) 48 (12–132) Comorbidities  Diabetes mellitus 0 (0%) 8 (15%) 3 (12%) 5 (17%)  Other autoimmune disorders 0 (0%) 15 (27%) 7 (27%) 8 (28%)  Pituitary tumor or surgery 0 (0%) 14 (26%) 8 (30%) 6 (21%)  Other hypothalamic-pituitary failure 0 (0%) 3 (5%) 1 (4%) 2 (7%)  Adrenalectomy 0 (0%) 4 (7%) 2 (8%) 2 (7%) GC replacement  Hydrocortisone — 20 (36%) 12 (46%) 8 (28%)  OD/BID/TID 0/16/4 0/11/1 0/5/3  Cortisone acetate — 35 (64%) 14 (54%) 21 (72%)  OD/BID/TID 0/34/1 0/13/1 0/21/0  Baseline equivalent dose (mg/m2/24 h) — 17 (16–19) 16 (14–18) 17 (15–20)  BMI (kg/m2) 23 (22–24) 25 (24–27) 25 (23–27) 26 (24–28) Gene expression data at baseline in the two groups are reported in Supplemental Table 1. Abbreviations: BID, two times daily; OD, once daily; TID, three times daily. a Values are expressed as mean (lower–upper limit of 95% CI), median (25th; 75th percentile), count (n), or percentages (%) as appropriate. View Large The expression of circadian genes was quantified relative to the housekeeping genes through the PrimePCR® circadian rhythms pathway assay in CFX Manager™ software. Of the 84 genes included in the panel, 68 were found to be expressed in the majority of samples. At baseline, 19 genes displayed a statistically different level of expression in PBMCs drawn from healthy controls vs subjects with AI (Fig. 2 and Supplemental Fig. 1). Global inspection of the panel revealed a generalized downregulation (from black to green) of clock-controlled gene expression in patients with AI, consistent with a flattening of the endogenous oscillators. The estimated marginal differences in the relative expression are shown in Fig. 2A. In the CLOCK gene cluster, ARNTL [BMAL1] (P < 0.001) and CLOCK (P < 0.001) were found to be downregulated, whereas PER3 (P = 0.013) and TIMELESS (P = 0.005) were upregulated in patients with AI compared with controls. The CREB pathway cluster was deeply affected, with most genes underexpressed in patients with AI: CAMK2D (P = 0.001), CREB1 (P < 0.001), CREB3 (P = 0.012), MAPK1 (P = 0.007), PRKAR1A (P = 0.003), PRKAR2A (P < 0.001), and PRKCB (P = 0.003), whereas AANAT (P = 0.009) and MAT2A (P = 0.008) appeared marginally increased in patients with AI compared with controls (Fig. 2B). Among the remaining genes, baseline differences were found in transcription factors with upregulated SP1 (P < 0.001) and downregulated WEE1 (P = 0.001; Fig. 2C) and in the casein kinase gene group, with upregulated CSNK1A1 (P < 0.001) and CSNK1E (P = 0.033) and downregulated ONP3 (P = 0.037) and PRF1 (P < 0.001) (Fig. 2D). The relative expression of several genes correlated with the metabolic and immune phenotype of the entire study population (Supplemental Table 3), showing that overexpression and underexpression of circadian genes including WEE1, TIMELESS, PRF1, and PER3 are associated with the increased CD16 shedding, ADAM17 levels, inflammatory monocytes, and ultimately metabolic derangement and susceptibility to infections. Most genes did not display differential levels of expression when we compared primary and secondary AI at baseline, except for ONP3, which was significantly suppressed in primary AI only (Supplemental Table 4). Figure 2. View largeDownload slide Difference in gene expression between adrenally sufficient and insufficient groups at baseline. Relative expression of clock-related genes at baseline. Data are presented as a marginal estimated distance of AI vs healthy controls (set as reference). Means and 95% CIs are presented as data markers and bars, respectively. (A) Circadian clock genes; (B) CREB signaling genes; (C) circadian regulated transcription factors; (D) common circadian regulated genes. *P < 0.05, **P < 0.01, ***P < 0.001. Figure 2. View largeDownload slide Difference in gene expression between adrenally sufficient and insufficient groups at baseline. Relative expression of clock-related genes at baseline. Data are presented as a marginal estimated distance of AI vs healthy controls (set as reference). Means and 95% CIs are presented as data markers and bars, respectively. (A) Circadian clock genes; (B) CREB signaling genes; (C) circadian regulated transcription factors; (D) common circadian regulated genes. *P < 0.05, **P < 0.01, ***P < 0.001. At week 12, switching to once-daily modified-release hydrocortisone robustly modulated the relative expression of 22 genes when compared with patients randomly assigned to multiple-daily-dose standard treatment after adjustment for multiple comparisons (Table 2,Fig. 3,Supplemental Table 5, and Supplemental Fig. 2). Specifically, the once-daily switched treatment increased ARNTL, ARNTL2, CLOCK, and RORA expression (Fig. 3) and reduced the previously overexpressed PER3 and TIMELESS levels. The ETD between the intervention groups in the CREB signaling cluster consisted in a significant reduction of AANAT and MAT2A and a significant increase in CAMK2D, CREB1, CREB3, MAPK1, PRKAR1A, PRKAR2A, and PRKCB (Fig. 3). Regarding the other circadian regulated genes, a significant ETD was found for SP1 and WEE1, which were inversely modulated; for CSNK1A1, which was downregulated; and for the three upregulated genes GUSB, ONP3, and PRF1 (Fig. 3). Table 2. Gene Expression Data at 12 Weeks, Change From Baseline, and Treatment-Related Differences for Modulated Genes Gene Switched-Treatment Group, Mean (95% CI) (n = 26) Standard-Treatment Group, Mean (95% CI) (n = 29) Treatment-Related Differencea P Benjamini-Hochberg Adjusted P AANAT 0.072 (−0.109 to 0.253) 0.439 (0.269 to 0.609) −0.367 (−0.616 to −0.118) 0.006 0.021  Within-group change −0.406 (−0.587 to −0.225) −0.039 (−0.209 to 0.131) ARNTL 1.593 (1.186 to 2.000) 0.779 (0.398 to 1.160) 0.814 (0.241 to 1.387) 0.007 0.024  Within-group change 0.942 (0.535 to 1.349) 0.128 (−0.253 to 0.509) ARNTL2 2.297 (1.746 to 2.848) 1.248 (0.715 to 1.781) 1.049 (0.279 to 1.819) 0.010 0.032  Within-group change 0.954 (0.403 to 1.505) −0.095 (−0.628 to 0.438) CAMK2D 2.256 (1.779 to 2.733) 0.911 (0.479 to 1.343) 1.345 (0.694 to 1.995) <0.001 <0.001  Within-group change 1.333 (0.856 to 1.810) −0.011 (−0.443 to 0.421) CLOCK 2.263 (1.684 to 2.842) 1.037 (0.494 to 1.581) 1.226 (0.429 to 2.022) 0.004 0.016  Within-group change 1.310 (0.731 to 1.889) 0.084 (−0.459 to 0.628) CREB1 2.344 (1.861 to 2.828) 1.183 (0.730 to 1.637) 1.161 (0.491 to 1.832) 0.002 0.010  Within-group change 1.311 (0.827 to 1.794) 0.150 (−0.304 to 0.603) CREB3 1.768 (1.455 to 2.081) 1.197 (0.903 to 1.490) 0.571 (0.137 to 1.006) 0.012 0.037  Within-group change 0.431 (0.118 to 0.744) −0.140 (−0.434 to 0.153) CSNK1A1 0.336 (0.176 to 0.496) 0.829 (0.679 to 0.979) −0.493 (−0.713 to −0.273) <0.001 0.001  Within-group change −0.547 (−0.707 to −0.387) −0.054 (−0.204 to 0.096) GUSB 1.850 (1.497 to 2.203) 1.053 (0.722 to 1.384) 0.797 (0.305 to 1.289) 0.003 0.013  Within-group change 0.665 (0.312 to 1.018) −0.132 (−0.462 to 0.199) MAPK1 2.784 (2.063 to 3.505) 0.758 (0.105 to 1.412) 2.025 (1.045 to 3.006) <0.001 <0.001  Within-group change 2.035 (1.314 to 2.756) 0.010 (−0.644 to 0.663) MAT2A 0.234 (0.019 to 0.449) 0.702 (0.500 to 0.904) −0.468 (−0.764 to −0.172) 0.003 0.013  Within-group change −0.503 (−0.718 to −0.288) −0.035 (−0.237 to 0.167) ONP3 1.910 (1.736 to 2.083) 1.422 (1.264 to 1.579) 0.488 (0.252 to 0.724) <0.001 0.001  Within-group change 0.558 (0.385 to 0.732) 0.070 (−0.087 to 0.227) PER3 0.764 (0.590 to 0.938) 1.328 (1.159 to 1.497) −0.564 (−0.808 to −0.321) <0.001 <0.001  Within-group change −0.585 (−0.759 to −0.411) −0.021 (−0.190 to 0.149) PRF1 1.451 (1.310 to 1.592) 0.636 (0.504 to 0.769) 0.814 (0.621 to 1.008) <0.001 0.001  Within-group change 0.851 (0.710 to 0.992) 0.037 (−0.096 to 0.169) PRKAR1A 2.179 (1.784 to 2.575) 1.144 (0.773 to 1.516) 1.035 (0.489 to 1.581) 0.001 0.006  Within-group change 0.950 (0.555 to 1.346) −0.085 (−0.456 to 0.287) PRKAR2A 1.993 (1.532 to 2.454) 0.881 (0.449 to 1.312) 1.112 (0.479 to 1.746) 0.001 0.006  Within-group change 1.090 (0.629 to 1.511) −0.022 (−0.454 to 0.409) PRKAR2B 0.837 (0.671 to 1.003) 0.462 (0.306 to 0.618) 0.375 (0.147 to 0.603) 0.002 0.010  Within-group change 0.428 (0.262 to 0.593) 0.053 (−0.103 to 0.209) PRKCB 2.129 (1.757 to 2.501) 1.228 (0.878 to 1.577) 0.902 (0.386 to 1.417) 0.001 0.006  Within-group change 0.792 (0.420 to 1.164) −0.109 (−0.458 to 0.240) RORA 2.088 (1.397 to 2.779) 0.677 (0.029 to 1.325) 1.411 (0.453 to 2.369) 0.006 0.021  Within-group change 1.271 (0.581 to 1.962) −0.140 (−0.788 to 0.508) SP1 0.758 (0.541 to 0.975) 1.424 (1.227 to 1.620) −0.665 (−0.962 to −0.369) <0.001 <0.001  Within-group change −0.715 (−0.932 to −0.497) −0.049 (−0.246 to 0.148) TIMELESS 0.356 (0.185 to 0.526) 1.038 (0.878 to 1.198) −0.682 (−0.920 to −0.444) <0.001 <0.001  Within-group change −0.786 (−0.957 to −0.616) −0.104 (−0.264 to 0.056) WEE1 1.723 (1.513 to 1.932) 0.818 (0.622 to 1.015) 0.904 (0.616 to 1.193) <0.001 <0.001  Within-group change 0.850 (0.641 to 1.059) −0.054 (−0.251 to 0.142) Gene Switched-Treatment Group, Mean (95% CI) (n = 26) Standard-Treatment Group, Mean (95% CI) (n = 29) Treatment-Related Differencea P Benjamini-Hochberg Adjusted P AANAT 0.072 (−0.109 to 0.253) 0.439 (0.269 to 0.609) −0.367 (−0.616 to −0.118) 0.006 0.021  Within-group change −0.406 (−0.587 to −0.225) −0.039 (−0.209 to 0.131) ARNTL 1.593 (1.186 to 2.000) 0.779 (0.398 to 1.160) 0.814 (0.241 to 1.387) 0.007 0.024  Within-group change 0.942 (0.535 to 1.349) 0.128 (−0.253 to 0.509) ARNTL2 2.297 (1.746 to 2.848) 1.248 (0.715 to 1.781) 1.049 (0.279 to 1.819) 0.010 0.032  Within-group change 0.954 (0.403 to 1.505) −0.095 (−0.628 to 0.438) CAMK2D 2.256 (1.779 to 2.733) 0.911 (0.479 to 1.343) 1.345 (0.694 to 1.995) <0.001 <0.001  Within-group change 1.333 (0.856 to 1.810) −0.011 (−0.443 to 0.421) CLOCK 2.263 (1.684 to 2.842) 1.037 (0.494 to 1.581) 1.226 (0.429 to 2.022) 0.004 0.016  Within-group change 1.310 (0.731 to 1.889) 0.084 (−0.459 to 0.628) CREB1 2.344 (1.861 to 2.828) 1.183 (0.730 to 1.637) 1.161 (0.491 to 1.832) 0.002 0.010  Within-group change 1.311 (0.827 to 1.794) 0.150 (−0.304 to 0.603) CREB3 1.768 (1.455 to 2.081) 1.197 (0.903 to 1.490) 0.571 (0.137 to 1.006) 0.012 0.037  Within-group change 0.431 (0.118 to 0.744) −0.140 (−0.434 to 0.153) CSNK1A1 0.336 (0.176 to 0.496) 0.829 (0.679 to 0.979) −0.493 (−0.713 to −0.273) <0.001 0.001  Within-group change −0.547 (−0.707 to −0.387) −0.054 (−0.204 to 0.096) GUSB 1.850 (1.497 to 2.203) 1.053 (0.722 to 1.384) 0.797 (0.305 to 1.289) 0.003 0.013  Within-group change 0.665 (0.312 to 1.018) −0.132 (−0.462 to 0.199) MAPK1 2.784 (2.063 to 3.505) 0.758 (0.105 to 1.412) 2.025 (1.045 to 3.006) <0.001 <0.001  Within-group change 2.035 (1.314 to 2.756) 0.010 (−0.644 to 0.663) MAT2A 0.234 (0.019 to 0.449) 0.702 (0.500 to 0.904) −0.468 (−0.764 to −0.172) 0.003 0.013  Within-group change −0.503 (−0.718 to −0.288) −0.035 (−0.237 to 0.167) ONP3 1.910 (1.736 to 2.083) 1.422 (1.264 to 1.579) 0.488 (0.252 to 0.724) <0.001 0.001  Within-group change 0.558 (0.385 to 0.732) 0.070 (−0.087 to 0.227) PER3 0.764 (0.590 to 0.938) 1.328 (1.159 to 1.497) −0.564 (−0.808 to −0.321) <0.001 <0.001  Within-group change −0.585 (−0.759 to −0.411) −0.021 (−0.190 to 0.149) PRF1 1.451 (1.310 to 1.592) 0.636 (0.504 to 0.769) 0.814 (0.621 to 1.008) <0.001 0.001  Within-group change 0.851 (0.710 to 0.992) 0.037 (−0.096 to 0.169) PRKAR1A 2.179 (1.784 to 2.575) 1.144 (0.773 to 1.516) 1.035 (0.489 to 1.581) 0.001 0.006  Within-group change 0.950 (0.555 to 1.346) −0.085 (−0.456 to 0.287) PRKAR2A 1.993 (1.532 to 2.454) 0.881 (0.449 to 1.312) 1.112 (0.479 to 1.746) 0.001 0.006  Within-group change 1.090 (0.629 to 1.511) −0.022 (−0.454 to 0.409) PRKAR2B 0.837 (0.671 to 1.003) 0.462 (0.306 to 0.618) 0.375 (0.147 to 0.603) 0.002 0.010  Within-group change 0.428 (0.262 to 0.593) 0.053 (−0.103 to 0.209) PRKCB 2.129 (1.757 to 2.501) 1.228 (0.878 to 1.577) 0.902 (0.386 to 1.417) 0.001 0.006  Within-group change 0.792 (0.420 to 1.164) −0.109 (−0.458 to 0.240) RORA 2.088 (1.397 to 2.779) 0.677 (0.029 to 1.325) 1.411 (0.453 to 2.369) 0.006 0.021  Within-group change 1.271 (0.581 to 1.962) −0.140 (−0.788 to 0.508) SP1 0.758 (0.541 to 0.975) 1.424 (1.227 to 1.620) −0.665 (−0.962 to −0.369) <0.001 <0.001  Within-group change −0.715 (−0.932 to −0.497) −0.049 (−0.246 to 0.148) TIMELESS 0.356 (0.185 to 0.526) 1.038 (0.878 to 1.198) −0.682 (−0.920 to −0.444) <0.001 <0.001  Within-group change −0.786 (−0.957 to −0.616) −0.104 (−0.264 to 0.056) WEE1 1.723 (1.513 to 1.932) 0.818 (0.622 to 1.015) 0.904 (0.616 to 1.193) <0.001 <0.001  Within-group change 0.850 (0.641 to 1.059) −0.054 (−0.251 to 0.142) Nonmodulated gene expression data are reported in Supplemental Table 5. a Covariates in the ANCOVA model: age, sex, BMI, type of AI, diabetes mellitus, smoking, and outcome at baseline. View Large Table 2. Gene Expression Data at 12 Weeks, Change From Baseline, and Treatment-Related Differences for Modulated Genes Gene Switched-Treatment Group, Mean (95% CI) (n = 26) Standard-Treatment Group, Mean (95% CI) (n = 29) Treatment-Related Differencea P Benjamini-Hochberg Adjusted P AANAT 0.072 (−0.109 to 0.253) 0.439 (0.269 to 0.609) −0.367 (−0.616 to −0.118) 0.006 0.021  Within-group change −0.406 (−0.587 to −0.225) −0.039 (−0.209 to 0.131) ARNTL 1.593 (1.186 to 2.000) 0.779 (0.398 to 1.160) 0.814 (0.241 to 1.387) 0.007 0.024  Within-group change 0.942 (0.535 to 1.349) 0.128 (−0.253 to 0.509) ARNTL2 2.297 (1.746 to 2.848) 1.248 (0.715 to 1.781) 1.049 (0.279 to 1.819) 0.010 0.032  Within-group change 0.954 (0.403 to 1.505) −0.095 (−0.628 to 0.438) CAMK2D 2.256 (1.779 to 2.733) 0.911 (0.479 to 1.343) 1.345 (0.694 to 1.995) <0.001 <0.001  Within-group change 1.333 (0.856 to 1.810) −0.011 (−0.443 to 0.421) CLOCK 2.263 (1.684 to 2.842) 1.037 (0.494 to 1.581) 1.226 (0.429 to 2.022) 0.004 0.016  Within-group change 1.310 (0.731 to 1.889) 0.084 (−0.459 to 0.628) CREB1 2.344 (1.861 to 2.828) 1.183 (0.730 to 1.637) 1.161 (0.491 to 1.832) 0.002 0.010  Within-group change 1.311 (0.827 to 1.794) 0.150 (−0.304 to 0.603) CREB3 1.768 (1.455 to 2.081) 1.197 (0.903 to 1.490) 0.571 (0.137 to 1.006) 0.012 0.037  Within-group change 0.431 (0.118 to 0.744) −0.140 (−0.434 to 0.153) CSNK1A1 0.336 (0.176 to 0.496) 0.829 (0.679 to 0.979) −0.493 (−0.713 to −0.273) <0.001 0.001  Within-group change −0.547 (−0.707 to −0.387) −0.054 (−0.204 to 0.096) GUSB 1.850 (1.497 to 2.203) 1.053 (0.722 to 1.384) 0.797 (0.305 to 1.289) 0.003 0.013  Within-group change 0.665 (0.312 to 1.018) −0.132 (−0.462 to 0.199) MAPK1 2.784 (2.063 to 3.505) 0.758 (0.105 to 1.412) 2.025 (1.045 to 3.006) <0.001 <0.001  Within-group change 2.035 (1.314 to 2.756) 0.010 (−0.644 to 0.663) MAT2A 0.234 (0.019 to 0.449) 0.702 (0.500 to 0.904) −0.468 (−0.764 to −0.172) 0.003 0.013  Within-group change −0.503 (−0.718 to −0.288) −0.035 (−0.237 to 0.167) ONP3 1.910 (1.736 to 2.083) 1.422 (1.264 to 1.579) 0.488 (0.252 to 0.724) <0.001 0.001  Within-group change 0.558 (0.385 to 0.732) 0.070 (−0.087 to 0.227) PER3 0.764 (0.590 to 0.938) 1.328 (1.159 to 1.497) −0.564 (−0.808 to −0.321) <0.001 <0.001  Within-group change −0.585 (−0.759 to −0.411) −0.021 (−0.190 to 0.149) PRF1 1.451 (1.310 to 1.592) 0.636 (0.504 to 0.769) 0.814 (0.621 to 1.008) <0.001 0.001  Within-group change 0.851 (0.710 to 0.992) 0.037 (−0.096 to 0.169) PRKAR1A 2.179 (1.784 to 2.575) 1.144 (0.773 to 1.516) 1.035 (0.489 to 1.581) 0.001 0.006  Within-group change 0.950 (0.555 to 1.346) −0.085 (−0.456 to 0.287) PRKAR2A 1.993 (1.532 to 2.454) 0.881 (0.449 to 1.312) 1.112 (0.479 to 1.746) 0.001 0.006  Within-group change 1.090 (0.629 to 1.511) −0.022 (−0.454 to 0.409) PRKAR2B 0.837 (0.671 to 1.003) 0.462 (0.306 to 0.618) 0.375 (0.147 to 0.603) 0.002 0.010  Within-group change 0.428 (0.262 to 0.593) 0.053 (−0.103 to 0.209) PRKCB 2.129 (1.757 to 2.501) 1.228 (0.878 to 1.577) 0.902 (0.386 to 1.417) 0.001 0.006  Within-group change 0.792 (0.420 to 1.164) −0.109 (−0.458 to 0.240) RORA 2.088 (1.397 to 2.779) 0.677 (0.029 to 1.325) 1.411 (0.453 to 2.369) 0.006 0.021  Within-group change 1.271 (0.581 to 1.962) −0.140 (−0.788 to 0.508) SP1 0.758 (0.541 to 0.975) 1.424 (1.227 to 1.620) −0.665 (−0.962 to −0.369) <0.001 <0.001  Within-group change −0.715 (−0.932 to −0.497) −0.049 (−0.246 to 0.148) TIMELESS 0.356 (0.185 to 0.526) 1.038 (0.878 to 1.198) −0.682 (−0.920 to −0.444) <0.001 <0.001  Within-group change −0.786 (−0.957 to −0.616) −0.104 (−0.264 to 0.056) WEE1 1.723 (1.513 to 1.932) 0.818 (0.622 to 1.015) 0.904 (0.616 to 1.193) <0.001 <0.001  Within-group change 0.850 (0.641 to 1.059) −0.054 (−0.251 to 0.142) Gene Switched-Treatment Group, Mean (95% CI) (n = 26) Standard-Treatment Group, Mean (95% CI) (n = 29) Treatment-Related Differencea P Benjamini-Hochberg Adjusted P AANAT 0.072 (−0.109 to 0.253) 0.439 (0.269 to 0.609) −0.367 (−0.616 to −0.118) 0.006 0.021  Within-group change −0.406 (−0.587 to −0.225) −0.039 (−0.209 to 0.131) ARNTL 1.593 (1.186 to 2.000) 0.779 (0.398 to 1.160) 0.814 (0.241 to 1.387) 0.007 0.024  Within-group change 0.942 (0.535 to 1.349) 0.128 (−0.253 to 0.509) ARNTL2 2.297 (1.746 to 2.848) 1.248 (0.715 to 1.781) 1.049 (0.279 to 1.819) 0.010 0.032  Within-group change 0.954 (0.403 to 1.505) −0.095 (−0.628 to 0.438) CAMK2D 2.256 (1.779 to 2.733) 0.911 (0.479 to 1.343) 1.345 (0.694 to 1.995) <0.001 <0.001  Within-group change 1.333 (0.856 to 1.810) −0.011 (−0.443 to 0.421) CLOCK 2.263 (1.684 to 2.842) 1.037 (0.494 to 1.581) 1.226 (0.429 to 2.022) 0.004 0.016  Within-group change 1.310 (0.731 to 1.889) 0.084 (−0.459 to 0.628) CREB1 2.344 (1.861 to 2.828) 1.183 (0.730 to 1.637) 1.161 (0.491 to 1.832) 0.002 0.010  Within-group change 1.311 (0.827 to 1.794) 0.150 (−0.304 to 0.603) CREB3 1.768 (1.455 to 2.081) 1.197 (0.903 to 1.490) 0.571 (0.137 to 1.006) 0.012 0.037  Within-group change 0.431 (0.118 to 0.744) −0.140 (−0.434 to 0.153) CSNK1A1 0.336 (0.176 to 0.496) 0.829 (0.679 to 0.979) −0.493 (−0.713 to −0.273) <0.001 0.001  Within-group change −0.547 (−0.707 to −0.387) −0.054 (−0.204 to 0.096) GUSB 1.850 (1.497 to 2.203) 1.053 (0.722 to 1.384) 0.797 (0.305 to 1.289) 0.003 0.013  Within-group change 0.665 (0.312 to 1.018) −0.132 (−0.462 to 0.199) MAPK1 2.784 (2.063 to 3.505) 0.758 (0.105 to 1.412) 2.025 (1.045 to 3.006) <0.001 <0.001  Within-group change 2.035 (1.314 to 2.756) 0.010 (−0.644 to 0.663) MAT2A 0.234 (0.019 to 0.449) 0.702 (0.500 to 0.904) −0.468 (−0.764 to −0.172) 0.003 0.013  Within-group change −0.503 (−0.718 to −0.288) −0.035 (−0.237 to 0.167) ONP3 1.910 (1.736 to 2.083) 1.422 (1.264 to 1.579) 0.488 (0.252 to 0.724) <0.001 0.001  Within-group change 0.558 (0.385 to 0.732) 0.070 (−0.087 to 0.227) PER3 0.764 (0.590 to 0.938) 1.328 (1.159 to 1.497) −0.564 (−0.808 to −0.321) <0.001 <0.001  Within-group change −0.585 (−0.759 to −0.411) −0.021 (−0.190 to 0.149) PRF1 1.451 (1.310 to 1.592) 0.636 (0.504 to 0.769) 0.814 (0.621 to 1.008) <0.001 0.001  Within-group change 0.851 (0.710 to 0.992) 0.037 (−0.096 to 0.169) PRKAR1A 2.179 (1.784 to 2.575) 1.144 (0.773 to 1.516) 1.035 (0.489 to 1.581) 0.001 0.006  Within-group change 0.950 (0.555 to 1.346) −0.085 (−0.456 to 0.287) PRKAR2A 1.993 (1.532 to 2.454) 0.881 (0.449 to 1.312) 1.112 (0.479 to 1.746) 0.001 0.006  Within-group change 1.090 (0.629 to 1.511) −0.022 (−0.454 to 0.409) PRKAR2B 0.837 (0.671 to 1.003) 0.462 (0.306 to 0.618) 0.375 (0.147 to 0.603) 0.002 0.010  Within-group change 0.428 (0.262 to 0.593) 0.053 (−0.103 to 0.209) PRKCB 2.129 (1.757 to 2.501) 1.228 (0.878 to 1.577) 0.902 (0.386 to 1.417) 0.001 0.006  Within-group change 0.792 (0.420 to 1.164) −0.109 (−0.458 to 0.240) RORA 2.088 (1.397 to 2.779) 0.677 (0.029 to 1.325) 1.411 (0.453 to 2.369) 0.006 0.021  Within-group change 1.271 (0.581 to 1.962) −0.140 (−0.788 to 0.508) SP1 0.758 (0.541 to 0.975) 1.424 (1.227 to 1.620) −0.665 (−0.962 to −0.369) <0.001 <0.001  Within-group change −0.715 (−0.932 to −0.497) −0.049 (−0.246 to 0.148) TIMELESS 0.356 (0.185 to 0.526) 1.038 (0.878 to 1.198) −0.682 (−0.920 to −0.444) <0.001 <0.001  Within-group change −0.786 (−0.957 to −0.616) −0.104 (−0.264 to 0.056) WEE1 1.723 (1.513 to 1.932) 0.818 (0.622 to 1.015) 0.904 (0.616 to 1.193) <0.001 <0.001  Within-group change 0.850 (0.641 to 1.059) −0.054 (−0.251 to 0.142) Nonmodulated gene expression data are reported in Supplemental Table 5. a Covariates in the ANCOVA model: age, sex, BMI, type of AI, diabetes mellitus, smoking, and outcome at baseline. View Large Figure 3. View largeDownload slide Differentially modulated genes in all groups at baseline and after treatment. The relative expression of the CLOCK-related (upper), CREB signaling–related (middle), and other circadian-controlled genes (bottom) at baseline and 12 weeks after randomization in all groups. Means ± SEM are presented as data markers and bars, respectively, and changes within subjects are presented as lines: controls (gray), switch-treatment group (green), and standard group (orange). Figure 3. View largeDownload slide Differentially modulated genes in all groups at baseline and after treatment. The relative expression of the CLOCK-related (upper), CREB signaling–related (middle), and other circadian-controlled genes (bottom) at baseline and 12 weeks after randomization in all groups. Means ± SEM are presented as data markers and bars, respectively, and changes within subjects are presented as lines: controls (gray), switch-treatment group (green), and standard group (orange). Of the 19 genes that were differentially modulated at baseline when we compared subjects with AI with control subjects, all but one (CSNK1E) were affected by treatment allocation (Fig. 3), thus matching the two prerequisites for relevance: a differential expression at baseline compared with healthy controls and a significant treatment difference between randomization groups. For all 18 genes the modulation was toward the level of expression found in healthy controls, that is, toward normalization. However, a significant ETD was found for another four genes that were not found modulated at baseline (ARNTL2, GUSB, PRKR2B, and RORA) (Supplemental Fig. 3). Subgroup analysis revealed no treatment by subgroup interaction for any of the modulated genes (Supplemental Table 6), suggesting that the effects of treatment switch were independent of the underlying etiology of the AI. Because treatment allocation produced a shift in the phenotype of some subsets of circulating PBMCs, namely a reduction in CD14+CD16− and an increase in CD16+CD56+CD3− cells (6), we also investigated gene expression in the subset that remained stable during the trial, the CD3+ T lymphocytes, which were unaffected by treatment. Of the 19 differentially expressed genes, 16 were also modulated in lymphocytes sorted from the entire set of PBMCs pooled according to treatment allocation (Supplemental Figure 4). Significant correlations were found between the change in several clock gene expression and the change in clinical outcomes including the glycated hemoglobin, blood pressure, levels of circulating soluble CD16, ADAM17, classic proinflammatory monocytes, and ultimately the frequency of infections (Table 3), suggesting that the extent of reprogramming of circadian gene expression can be linked to the magnitude of improvement in clinically measurable outcomes. Table 3. Delta Change Correlation Matrix Gene Δ-BMI Δ-HbA1c Δ-High-Density Lipoprotein Δ-TGa Δ-SBPb Δ-DBPc Δ-CD16+ CD14− Cells Δ-CD14+ CD16− Cells Δ-CD16+ NK Cells Δ-ADAM17d Δ-CD16sd Δ-Infectionse Δ-AANAT r −0.272 0.394f 0.176 −0.096 −0.092 0.183 −0.016 −0.009 0.008 0.197 0.359 −0.185 P 0.132 0.038 0.352 0.603 0.617 0.315 0.929 0.963 0.970 0.393 0.110 0.303 Δ-ARNTL r −0.279 0.023 0.023 −0.091 0.184 −0.136 0.171 −0.166 −0.011 −0.398 −0.252 −0.252 P 0.122 0.909 0.905 0.620 0.314 0.457 0.349 0.364 0.958 0.074 0.270 0.157 Δ-ARNTL2 r −0.294 −0.071 −0.039 −0.222 −0.055 0.495g 0.060 −0.041 0.038 −0.493f −0.352 −0.182 P 0.109 0.724 0.840 0.230 0.768 0.005 0.749 0.826 0.857 0.023 0.118 0.318 Δ-CAMK2D r 0.186 −0.384f −0.209 0.003 0.271 −0.178 0.234 −0.400f 0.232 −0.615g −0.378 −0.053 P 0.317 0.048 0.276 0.985 0.141 0.339 0.205 0.026 0.265 0.004 0.100 0.775 Δ-CLOCK r −0.094 −0.088 0.017 0.017 0.334 −0.008 0.255 −0.196 0.062 −0.360 −0.253 −0.246 P 0.609 0.657 0.927 0.927 0.061 0.966 0.159 0.282 0.764 0.109 0.268 0.167 Δ-CREB1 r 0.067 −0.165 −0.098 0.021 0.364f −0.300 0.135 −0.119 −0.081 −0.191 −0.092 −0.003 P 0.715 0.403 0.607 0.907 0.040 0.095 0.462 0.518 0.695 0.407 0.690 0.986 Δ-CREB3 r 0.075 −0.200 −0.034 −0.180 0.060 −0.026 0.272 −0.082 0.189 −0.343 −0.303 −0.056 P 0.683 0.298 0.857 0.325 0.746 0.889 0.133 0.655 0.345 0.118 0.170 0.757 Δ-CSNK1A1 r −0.366f 0.507g 0.254 0.092 −0.110 0.199 −0.148 0.343 −0.197 0.585g 0.414 −0.108 P 0.040 0.006 0.175 0.615 0.549 0.276 0.418 0.055 0.335 0.005 0.062 0.550 Δ-CSNK1E r 0.018 0.258 −0.223 0.344 −0.082 0.137 0.139 −0.080 0.062 0.104 0.081 0.244 P 0.924 0.177 0.229 0.054 0.657 0.453 0.449 0.665 0.759 0.646 0.720 0.171 Δ-GUSB r −0.335 0.014 −0.041 −0.216 −0.112 −0.251 0.358f −0.310 0.275 −0.685g −0.565g −0.402* P 0.061 0.943 0.831 0.236 0.540 0.165 0.044 0.084 0.173 0.001 0.008 0.020 Δ-MAPK1 r −0.021 −0.293 −0.028 −0.161 0.184 −0.001 0.177 −0.219 0.078 −0.566g −0.449f −0.143 P 0.910 0.137 0.884 0.388 0.323 0.996 0.342 0.235 0.712 0.009 0.047 0.435 Δ-MAT2A r −0.255 0.439f 0.222 −0.051 0.081 −0.024 −0.032 0.072 −0.068 0.195 0.326 −0.098 P 0.158 0.019 0.238 0.780 0.661 0.894 0.860 0.694 0.742 0.396 0.149 0.586 Δ-ONP3 r 0.257 −0.352 −0.222 −0.002 0.041 0.059 0.581g −0.355f 0.312 −0.527f −0.475f −0.306 P 0.162 0.066 0.237 0.991 0.826 0.752 0.001 0.050 0.121 0.012 0.025 0.089 Δ-PER3 r 0.070 0.325 0.125 −0.019 0.289 −0.315 −0.280 0.327 −0.175 0.505f 0.443f 0.433f P 0.698 0.085 0.504 0.918 0.103 0.074 0.115 0.063 0.394 0.017 0.039 0.010 Δ-PRF1 r 0.093 −0.446f −0.268 −0.046 −0.042 0.044 0.489g −0.437f 0.442f −0.697g −0.691g −0.309 P 0.612 0.017 0.153 0.803 0.821 0.812 0.004 0.012 0.024 <0.001 0.001 0.080 Δ-PRKAR1A r 0.011 −0.239 −0.005 −0.168 0.223 0.175 0.313 −0.288 0.137 −0.427 −0.357 −0.187 P 0.952 0.220 0.977 0.358 0.219 0.337 0.081 0.110 0.503 0.054 0.112 0.297 Δ-PRKAR2A r −0.189 −0.163 −0.041 0.084 0.035 0.123 0.363f −0.308 0.143 −0.592g −0.420 −0.465g P 0.318 0.426 0.838 0.660 0.856 0.518 0.049 0.098 0.507 0.008 0.073 0.008 Δ-PRKCB r −0.111 0.108 0.266 0.262 0.185 0.114 0.202 −0.245 0.156 0.025 0.302 0.177 P 0.457 0.500 0.084 0.082 0.213 0.447 0.269 0.177 0.446 0.885 0.077 0.233 Δ-RORA r −0.252 −0.069 0.009 −0.092 0.099 0.088 0.100 −0.199 0.088 −0.504f −0.358 −0.243 P 0.163 0.728 0.961 0.615 0.590 0.631 0.587 0.275 0.670 0.020 0.111 0.172 Δ-SP1 r −0.017 0.236 0.214 −0.305 0.063 0.015 −0.323 0.350 −0.093 0.462f 0.659g 0.307 P 0.929 0.226 0.257 0.095 0.738 0.935 0.076 0.053 0.650 0.031 0.001 0.087 Δ-TIMELESS r 0.183 0.210 0.057 0.103 0.377f −0.329 −0.181 0.303 −0.186 0.587g 0.736g 0.408g P 0.317 0.284 0.764 0.574 0.034 0.066 0.321 0.092 0.363 0.005 <0.001 0.019 Δ-WEE1 r −0.105 −0.375f −0.144 0.180 −0.011 −0.013 0.583g −0.542g 0.334 −0.778g −0.645g −0.527g P 0.553 0.041 0.432 0.307 0.951 0.940 <0.001 0.001 0.096 <0.001 0.001 0.001 Gene Δ-BMI Δ-HbA1c Δ-High-Density Lipoprotein Δ-TGa Δ-SBPb Δ-DBPc Δ-CD16+ CD14− Cells Δ-CD14+ CD16− Cells Δ-CD16+ NK Cells Δ-ADAM17d Δ-CD16sd Δ-Infectionse Δ-AANAT r −0.272 0.394f 0.176 −0.096 −0.092 0.183 −0.016 −0.009 0.008 0.197 0.359 −0.185 P 0.132 0.038 0.352 0.603 0.617 0.315 0.929 0.963 0.970 0.393 0.110 0.303 Δ-ARNTL r −0.279 0.023 0.023 −0.091 0.184 −0.136 0.171 −0.166 −0.011 −0.398 −0.252 −0.252 P 0.122 0.909 0.905 0.620 0.314 0.457 0.349 0.364 0.958 0.074 0.270 0.157 Δ-ARNTL2 r −0.294 −0.071 −0.039 −0.222 −0.055 0.495g 0.060 −0.041 0.038 −0.493f −0.352 −0.182 P 0.109 0.724 0.840 0.230 0.768 0.005 0.749 0.826 0.857 0.023 0.118 0.318 Δ-CAMK2D r 0.186 −0.384f −0.209 0.003 0.271 −0.178 0.234 −0.400f 0.232 −0.615g −0.378 −0.053 P 0.317 0.048 0.276 0.985 0.141 0.339 0.205 0.026 0.265 0.004 0.100 0.775 Δ-CLOCK r −0.094 −0.088 0.017 0.017 0.334 −0.008 0.255 −0.196 0.062 −0.360 −0.253 −0.246 P 0.609 0.657 0.927 0.927 0.061 0.966 0.159 0.282 0.764 0.109 0.268 0.167 Δ-CREB1 r 0.067 −0.165 −0.098 0.021 0.364f −0.300 0.135 −0.119 −0.081 −0.191 −0.092 −0.003 P 0.715 0.403 0.607 0.907 0.040 0.095 0.462 0.518 0.695 0.407 0.690 0.986 Δ-CREB3 r 0.075 −0.200 −0.034 −0.180 0.060 −0.026 0.272 −0.082 0.189 −0.343 −0.303 −0.056 P 0.683 0.298 0.857 0.325 0.746 0.889 0.133 0.655 0.345 0.118 0.170 0.757 Δ-CSNK1A1 r −0.366f 0.507g 0.254 0.092 −0.110 0.199 −0.148 0.343 −0.197 0.585g 0.414 −0.108 P 0.040 0.006 0.175 0.615 0.549 0.276 0.418 0.055 0.335 0.005 0.062 0.550 Δ-CSNK1E r 0.018 0.258 −0.223 0.344 −0.082 0.137 0.139 −0.080 0.062 0.104 0.081 0.244 P 0.924 0.177 0.229 0.054 0.657 0.453 0.449 0.665 0.759 0.646 0.720 0.171 Δ-GUSB r −0.335 0.014 −0.041 −0.216 −0.112 −0.251 0.358f −0.310 0.275 −0.685g −0.565g −0.402* P 0.061 0.943 0.831 0.236 0.540 0.165 0.044 0.084 0.173 0.001 0.008 0.020 Δ-MAPK1 r −0.021 −0.293 −0.028 −0.161 0.184 −0.001 0.177 −0.219 0.078 −0.566g −0.449f −0.143 P 0.910 0.137 0.884 0.388 0.323 0.996 0.342 0.235 0.712 0.009 0.047 0.435 Δ-MAT2A r −0.255 0.439f 0.222 −0.051 0.081 −0.024 −0.032 0.072 −0.068 0.195 0.326 −0.098 P 0.158 0.019 0.238 0.780 0.661 0.894 0.860 0.694 0.742 0.396 0.149 0.586 Δ-ONP3 r 0.257 −0.352 −0.222 −0.002 0.041 0.059 0.581g −0.355f 0.312 −0.527f −0.475f −0.306 P 0.162 0.066 0.237 0.991 0.826 0.752 0.001 0.050 0.121 0.012 0.025 0.089 Δ-PER3 r 0.070 0.325 0.125 −0.019 0.289 −0.315 −0.280 0.327 −0.175 0.505f 0.443f 0.433f P 0.698 0.085 0.504 0.918 0.103 0.074 0.115 0.063 0.394 0.017 0.039 0.010 Δ-PRF1 r 0.093 −0.446f −0.268 −0.046 −0.042 0.044 0.489g −0.437f 0.442f −0.697g −0.691g −0.309 P 0.612 0.017 0.153 0.803 0.821 0.812 0.004 0.012 0.024 <0.001 0.001 0.080 Δ-PRKAR1A r 0.011 −0.239 −0.005 −0.168 0.223 0.175 0.313 −0.288 0.137 −0.427 −0.357 −0.187 P 0.952 0.220 0.977 0.358 0.219 0.337 0.081 0.110 0.503 0.054 0.112 0.297 Δ-PRKAR2A r −0.189 −0.163 −0.041 0.084 0.035 0.123 0.363f −0.308 0.143 −0.592g −0.420 −0.465g P 0.318 0.426 0.838 0.660 0.856 0.518 0.049 0.098 0.507 0.008 0.073 0.008 Δ-PRKCB r −0.111 0.108 0.266 0.262 0.185 0.114 0.202 −0.245 0.156 0.025 0.302 0.177 P 0.457 0.500 0.084 0.082 0.213 0.447 0.269 0.177 0.446 0.885 0.077 0.233 Δ-RORA r −0.252 −0.069 0.009 −0.092 0.099 0.088 0.100 −0.199 0.088 −0.504f −0.358 −0.243 P 0.163 0.728 0.961 0.615 0.590 0.631 0.587 0.275 0.670 0.020 0.111 0.172 Δ-SP1 r −0.017 0.236 0.214 −0.305 0.063 0.015 −0.323 0.350 −0.093 0.462f 0.659g 0.307 P 0.929 0.226 0.257 0.095 0.738 0.935 0.076 0.053 0.650 0.031 0.001 0.087 Δ-TIMELESS r 0.183 0.210 0.057 0.103 0.377f −0.329 −0.181 0.303 −0.186 0.587g 0.736g 0.408g P 0.317 0.284 0.764 0.574 0.034 0.066 0.321 0.092 0.363 0.005 <0.001 0.019 Δ-WEE1 r −0.105 −0.375f −0.144 0.180 −0.011 −0.013 0.583g −0.542g 0.334 −0.778g −0.645g −0.527g P 0.553 0.041 0.432 0.307 0.951 0.940 <0.001 0.001 0.096 <0.001 0.001 0.001 Boldface indicates statistical significance. a Triglycerides. b Systolic blood pressure. c Diastolic blood pressure. d Log10(ADAM17) and log10(CD16s). e Total infection score. f P < 0.05. g P < 0.01. View Large Table 3. Delta Change Correlation Matrix Gene Δ-BMI Δ-HbA1c Δ-High-Density Lipoprotein Δ-TGa Δ-SBPb Δ-DBPc Δ-CD16+ CD14− Cells Δ-CD14+ CD16− Cells Δ-CD16+ NK Cells Δ-ADAM17d Δ-CD16sd Δ-Infectionse Δ-AANAT r −0.272 0.394f 0.176 −0.096 −0.092 0.183 −0.016 −0.009 0.008 0.197 0.359 −0.185 P 0.132 0.038 0.352 0.603 0.617 0.315 0.929 0.963 0.970 0.393 0.110 0.303 Δ-ARNTL r −0.279 0.023 0.023 −0.091 0.184 −0.136 0.171 −0.166 −0.011 −0.398 −0.252 −0.252 P 0.122 0.909 0.905 0.620 0.314 0.457 0.349 0.364 0.958 0.074 0.270 0.157 Δ-ARNTL2 r −0.294 −0.071 −0.039 −0.222 −0.055 0.495g 0.060 −0.041 0.038 −0.493f −0.352 −0.182 P 0.109 0.724 0.840 0.230 0.768 0.005 0.749 0.826 0.857 0.023 0.118 0.318 Δ-CAMK2D r 0.186 −0.384f −0.209 0.003 0.271 −0.178 0.234 −0.400f 0.232 −0.615g −0.378 −0.053 P 0.317 0.048 0.276 0.985 0.141 0.339 0.205 0.026 0.265 0.004 0.100 0.775 Δ-CLOCK r −0.094 −0.088 0.017 0.017 0.334 −0.008 0.255 −0.196 0.062 −0.360 −0.253 −0.246 P 0.609 0.657 0.927 0.927 0.061 0.966 0.159 0.282 0.764 0.109 0.268 0.167 Δ-CREB1 r 0.067 −0.165 −0.098 0.021 0.364f −0.300 0.135 −0.119 −0.081 −0.191 −0.092 −0.003 P 0.715 0.403 0.607 0.907 0.040 0.095 0.462 0.518 0.695 0.407 0.690 0.986 Δ-CREB3 r 0.075 −0.200 −0.034 −0.180 0.060 −0.026 0.272 −0.082 0.189 −0.343 −0.303 −0.056 P 0.683 0.298 0.857 0.325 0.746 0.889 0.133 0.655 0.345 0.118 0.170 0.757 Δ-CSNK1A1 r −0.366f 0.507g 0.254 0.092 −0.110 0.199 −0.148 0.343 −0.197 0.585g 0.414 −0.108 P 0.040 0.006 0.175 0.615 0.549 0.276 0.418 0.055 0.335 0.005 0.062 0.550 Δ-CSNK1E r 0.018 0.258 −0.223 0.344 −0.082 0.137 0.139 −0.080 0.062 0.104 0.081 0.244 P 0.924 0.177 0.229 0.054 0.657 0.453 0.449 0.665 0.759 0.646 0.720 0.171 Δ-GUSB r −0.335 0.014 −0.041 −0.216 −0.112 −0.251 0.358f −0.310 0.275 −0.685g −0.565g −0.402* P 0.061 0.943 0.831 0.236 0.540 0.165 0.044 0.084 0.173 0.001 0.008 0.020 Δ-MAPK1 r −0.021 −0.293 −0.028 −0.161 0.184 −0.001 0.177 −0.219 0.078 −0.566g −0.449f −0.143 P 0.910 0.137 0.884 0.388 0.323 0.996 0.342 0.235 0.712 0.009 0.047 0.435 Δ-MAT2A r −0.255 0.439f 0.222 −0.051 0.081 −0.024 −0.032 0.072 −0.068 0.195 0.326 −0.098 P 0.158 0.019 0.238 0.780 0.661 0.894 0.860 0.694 0.742 0.396 0.149 0.586 Δ-ONP3 r 0.257 −0.352 −0.222 −0.002 0.041 0.059 0.581g −0.355f 0.312 −0.527f −0.475f −0.306 P 0.162 0.066 0.237 0.991 0.826 0.752 0.001 0.050 0.121 0.012 0.025 0.089 Δ-PER3 r 0.070 0.325 0.125 −0.019 0.289 −0.315 −0.280 0.327 −0.175 0.505f 0.443f 0.433f P 0.698 0.085 0.504 0.918 0.103 0.074 0.115 0.063 0.394 0.017 0.039 0.010 Δ-PRF1 r 0.093 −0.446f −0.268 −0.046 −0.042 0.044 0.489g −0.437f 0.442f −0.697g −0.691g −0.309 P 0.612 0.017 0.153 0.803 0.821 0.812 0.004 0.012 0.024 <0.001 0.001 0.080 Δ-PRKAR1A r 0.011 −0.239 −0.005 −0.168 0.223 0.175 0.313 −0.288 0.137 −0.427 −0.357 −0.187 P 0.952 0.220 0.977 0.358 0.219 0.337 0.081 0.110 0.503 0.054 0.112 0.297 Δ-PRKAR2A r −0.189 −0.163 −0.041 0.084 0.035 0.123 0.363f −0.308 0.143 −0.592g −0.420 −0.465g P 0.318 0.426 0.838 0.660 0.856 0.518 0.049 0.098 0.507 0.008 0.073 0.008 Δ-PRKCB r −0.111 0.108 0.266 0.262 0.185 0.114 0.202 −0.245 0.156 0.025 0.302 0.177 P 0.457 0.500 0.084 0.082 0.213 0.447 0.269 0.177 0.446 0.885 0.077 0.233 Δ-RORA r −0.252 −0.069 0.009 −0.092 0.099 0.088 0.100 −0.199 0.088 −0.504f −0.358 −0.243 P 0.163 0.728 0.961 0.615 0.590 0.631 0.587 0.275 0.670 0.020 0.111 0.172 Δ-SP1 r −0.017 0.236 0.214 −0.305 0.063 0.015 −0.323 0.350 −0.093 0.462f 0.659g 0.307 P 0.929 0.226 0.257 0.095 0.738 0.935 0.076 0.053 0.650 0.031 0.001 0.087 Δ-TIMELESS r 0.183 0.210 0.057 0.103 0.377f −0.329 −0.181 0.303 −0.186 0.587g 0.736g 0.408g P 0.317 0.284 0.764 0.574 0.034 0.066 0.321 0.092 0.363 0.005 <0.001 0.019 Δ-WEE1 r −0.105 −0.375f −0.144 0.180 −0.011 −0.013 0.583g −0.542g 0.334 −0.778g −0.645g −0.527g P 0.553 0.041 0.432 0.307 0.951 0.940 <0.001 0.001 0.096 <0.001 0.001 0.001 Gene Δ-BMI Δ-HbA1c Δ-High-Density Lipoprotein Δ-TGa Δ-SBPb Δ-DBPc Δ-CD16+ CD14− Cells Δ-CD14+ CD16− Cells Δ-CD16+ NK Cells Δ-ADAM17d Δ-CD16sd Δ-Infectionse Δ-AANAT r −0.272 0.394f 0.176 −0.096 −0.092 0.183 −0.016 −0.009 0.008 0.197 0.359 −0.185 P 0.132 0.038 0.352 0.603 0.617 0.315 0.929 0.963 0.970 0.393 0.110 0.303 Δ-ARNTL r −0.279 0.023 0.023 −0.091 0.184 −0.136 0.171 −0.166 −0.011 −0.398 −0.252 −0.252 P 0.122 0.909 0.905 0.620 0.314 0.457 0.349 0.364 0.958 0.074 0.270 0.157 Δ-ARNTL2 r −0.294 −0.071 −0.039 −0.222 −0.055 0.495g 0.060 −0.041 0.038 −0.493f −0.352 −0.182 P 0.109 0.724 0.840 0.230 0.768 0.005 0.749 0.826 0.857 0.023 0.118 0.318 Δ-CAMK2D r 0.186 −0.384f −0.209 0.003 0.271 −0.178 0.234 −0.400f 0.232 −0.615g −0.378 −0.053 P 0.317 0.048 0.276 0.985 0.141 0.339 0.205 0.026 0.265 0.004 0.100 0.775 Δ-CLOCK r −0.094 −0.088 0.017 0.017 0.334 −0.008 0.255 −0.196 0.062 −0.360 −0.253 −0.246 P 0.609 0.657 0.927 0.927 0.061 0.966 0.159 0.282 0.764 0.109 0.268 0.167 Δ-CREB1 r 0.067 −0.165 −0.098 0.021 0.364f −0.300 0.135 −0.119 −0.081 −0.191 −0.092 −0.003 P 0.715 0.403 0.607 0.907 0.040 0.095 0.462 0.518 0.695 0.407 0.690 0.986 Δ-CREB3 r 0.075 −0.200 −0.034 −0.180 0.060 −0.026 0.272 −0.082 0.189 −0.343 −0.303 −0.056 P 0.683 0.298 0.857 0.325 0.746 0.889 0.133 0.655 0.345 0.118 0.170 0.757 Δ-CSNK1A1 r −0.366f 0.507g 0.254 0.092 −0.110 0.199 −0.148 0.343 −0.197 0.585g 0.414 −0.108 P 0.040 0.006 0.175 0.615 0.549 0.276 0.418 0.055 0.335 0.005 0.062 0.550 Δ-CSNK1E r 0.018 0.258 −0.223 0.344 −0.082 0.137 0.139 −0.080 0.062 0.104 0.081 0.244 P 0.924 0.177 0.229 0.054 0.657 0.453 0.449 0.665 0.759 0.646 0.720 0.171 Δ-GUSB r −0.335 0.014 −0.041 −0.216 −0.112 −0.251 0.358f −0.310 0.275 −0.685g −0.565g −0.402* P 0.061 0.943 0.831 0.236 0.540 0.165 0.044 0.084 0.173 0.001 0.008 0.020 Δ-MAPK1 r −0.021 −0.293 −0.028 −0.161 0.184 −0.001 0.177 −0.219 0.078 −0.566g −0.449f −0.143 P 0.910 0.137 0.884 0.388 0.323 0.996 0.342 0.235 0.712 0.009 0.047 0.435 Δ-MAT2A r −0.255 0.439f 0.222 −0.051 0.081 −0.024 −0.032 0.072 −0.068 0.195 0.326 −0.098 P 0.158 0.019 0.238 0.780 0.661 0.894 0.860 0.694 0.742 0.396 0.149 0.586 Δ-ONP3 r 0.257 −0.352 −0.222 −0.002 0.041 0.059 0.581g −0.355f 0.312 −0.527f −0.475f −0.306 P 0.162 0.066 0.237 0.991 0.826 0.752 0.001 0.050 0.121 0.012 0.025 0.089 Δ-PER3 r 0.070 0.325 0.125 −0.019 0.289 −0.315 −0.280 0.327 −0.175 0.505f 0.443f 0.433f P 0.698 0.085 0.504 0.918 0.103 0.074 0.115 0.063 0.394 0.017 0.039 0.010 Δ-PRF1 r 0.093 −0.446f −0.268 −0.046 −0.042 0.044 0.489g −0.437f 0.442f −0.697g −0.691g −0.309 P 0.612 0.017 0.153 0.803 0.821 0.812 0.004 0.012 0.024 <0.001 0.001 0.080 Δ-PRKAR1A r 0.011 −0.239 −0.005 −0.168 0.223 0.175 0.313 −0.288 0.137 −0.427 −0.357 −0.187 P 0.952 0.220 0.977 0.358 0.219 0.337 0.081 0.110 0.503 0.054 0.112 0.297 Δ-PRKAR2A r −0.189 −0.163 −0.041 0.084 0.035 0.123 0.363f −0.308 0.143 −0.592g −0.420 −0.465g P 0.318 0.426 0.838 0.660 0.856 0.518 0.049 0.098 0.507 0.008 0.073 0.008 Δ-PRKCB r −0.111 0.108 0.266 0.262 0.185 0.114 0.202 −0.245 0.156 0.025 0.302 0.177 P 0.457 0.500 0.084 0.082 0.213 0.447 0.269 0.177 0.446 0.885 0.077 0.233 Δ-RORA r −0.252 −0.069 0.009 −0.092 0.099 0.088 0.100 −0.199 0.088 −0.504f −0.358 −0.243 P 0.163 0.728 0.961 0.615 0.590 0.631 0.587 0.275 0.670 0.020 0.111 0.172 Δ-SP1 r −0.017 0.236 0.214 −0.305 0.063 0.015 −0.323 0.350 −0.093 0.462f 0.659g 0.307 P 0.929 0.226 0.257 0.095 0.738 0.935 0.076 0.053 0.650 0.031 0.001 0.087 Δ-TIMELESS r 0.183 0.210 0.057 0.103 0.377f −0.329 −0.181 0.303 −0.186 0.587g 0.736g 0.408g P 0.317 0.284 0.764 0.574 0.034 0.066 0.321 0.092 0.363 0.005 <0.001 0.019 Δ-WEE1 r −0.105 −0.375f −0.144 0.180 −0.011 −0.013 0.583g −0.542g 0.334 −0.778g −0.645g −0.527g P 0.553 0.041 0.432 0.307 0.951 0.940 <0.001 0.001 0.096 <0.001 0.001 0.001 Boldface indicates statistical significance. a Triglycerides. b Systolic blood pressure. c Diastolic blood pressure. d Log10(ADAM17) and log10(CD16s). e Total infection score. f P < 0.05. g P < 0.01. View Large Discussion The DREAM trial was a head-to-head comparison of standard twice- or thrice-daily cortisone or hydrocortisone and once-daily modified-release hydrocortisone replacement therapy. We showed that patients with AI on standard replacement therapy exhibit unexpected abnormalities in circulating PBMCs, with more classic monocytes and a smaller number of CD16+ natural killer cells, that can be partially reversed by changing the timing of GC administration (6). In the current report we showed that the patients with AI on standard replacement therapy have an abnormal expression of clock-related genes in circulating blood cells that can be partially normalized by switching from a multiple-times-a-day to a once-daily modified release hydrocortisone administration. To our knowledge, this is the first report of a link between the chronopharmacology of GC administration, the expression of circadian genes in immune cells, and the metabolic outcomes in the context of a clinical trial Although the influence of GCs on immune trafficking and regulation is known (15), recent studies suggest that GC administration, or adrenalectomy, affects the expression of clock-related genes, and, in turn, the complex CLOCK/ARNLT can suppress GC receptor–induced transcriptional activity (16). The rhythm of peripheral CLOCK gene expression shifts the activity of the GC receptor out of phase to when GC peaks in blood (17), setting an additional feedback to prevent overexposure to GCs (16). Conversely, when the clock is downregulated, GC sensitivity is higher. The latter is consistent with the knowledge of more deleterious metabolic effects of GC administered late in the evening (18). The importance of time exposure as compared with dose exposure has been recently confirmed in animal studies showing that high peaks of GC are well tolerated as long as sufficiently long “off” intervals are preserved (19). In addition to metabolic regulation (20), canonical CLOCK components are also involved in immune modulation (21). Patients with AI, even when treated according to the best practice guidelines, have higher mortality and hospitalization rates, especially from infectious and cardiovascular diseases (8–12, 22, 23). The DREAM trial (6) confirmed previous observations (7) that patients with AI can suffer from defective innate immunity with an “exhausted phenotype” of natural killer cells. Clock gene dysregulation may lead to functional hypercortisolism or hypocortisolism in peripheral target tissues (24), highlighting the fact that estimating total cortisol exposure is more complex than just measuring circulating cortisol levels (25). In this study we found that, compared with adrenally sufficient controls, patients treated with standard GC therapy showed a significant downregulation of canonical CLOCK components, such as the CLOCK gene, which encodes for the Circadian Locomotor Output Cycles Kaput (CLOCK) protein and aryl hydrocarbon receptor nuclear translocator (ARNTL, BMAL), which could be restored to normal by altering the timing of GC administration. Moreover, the GC switch restored CSNK1A1 and PRF1 expression to levels correlated with the treatment-induced reduction in HbA1c, confirming the importance of the timing of GC administration for metabolic function. A recent study in mice showed that raising the peak of the GC oscillations up to 40-fold by injecting corticosteroids for 21 days produced no relevant increase in adipogenesis, as long as GC were given in the correct circadian periods, whereas losing the nadirs or “off periods” of GC administration produced a striking increase in adipogenesis (19). Interestingly, we found PER3 upregulated and correlated with inflammation in patients with AI. Recent evidence supports a prominent role for PER3 oscillation, as compared with PER1, in adipose tissue function (26). We found a significant downregulation of WEE1 in patients with AI at baseline, inversely related to BMI and triglycerides. Interestingly, WEE1 is the transcriptional factor that appears to coordinate the transition between DNA replication and mitosis by arresting G2 phase and inhibiting progression toward mitosis. Consequently, reduced WEE1 synthesis favors entry into mitosis and may even shorten its duration. Downregulated WEE1 has also been found in pituitary adenomas, suggesting a potential role in tumorigenesis of the loss of its protective function (27). WEE1 expression is controlled by CLOCK-ARNLT (28), and in our study it was restored in the switch treatment group after 12 weeks. Interestingly, the upregulation of WEE1 in the switched treatment group correlated with a reduction in glycated hemoglobin and inflammatory monocytes. The cAMP-responsive element-binding protein, known as CREB/CRE, plays a crucial role in several cell functions, including proliferation, survival, differentiation, adaptive responses, glucose homeostasis, spermatogenesis, synaptic plasticity associated with memory, and circadian rhythms (29). CREB is induced by a variety of growth factors and inflammatory signals; it can also promote anti-inflammatory immune responses, such as the inhibition of NF-kB activity, the induction of IL-10, and the generation of Treg, and promotes activation and proliferation of T and B lymphocytes (30). We found a significant downregulation in most cAMP downstream targets (CREB1, PRKAR1A, and PRKAR2A) in patients with AI compared with controls, a finding consistent with the recurrent infections seen in patients with AI. Accordingly, PRF1, the predominant cytolytic protein secreted by natural killer cells (31), was also found downregulated in patients with AI compared with controls. Of note is that PRF1 null mice exhibit increased body weight and adiposity, glucose intolerance, and insulin resistance caused by an M1-polarization of macrophages infiltrating visceral adipose tissue (32). We found that restoration of PRF1 to control levels in patients with AI in the switched treatment group correlated with a reduction in HbA1c. Taken together, our findings on PRF1 and PER3 modulation support a role for adipocyte dysfunction in explaining the metabolic impairment and low-grade inflammation observed in patients with AI on multiple-times-a-day GC treatment. The latter could also explain the higher risk of atherosclerosis in these patients in the absence of fat accumulation (33). An upregulated expression of AANAT was also observed in patients with AI at baseline, with a significant reduction in the switched treatment group at 12 weeks. AANAT encodes for arylalkylamine N-acetyltransferase, also known as the “Timenzyme,” which controls daily changes in melatonin production by the pineal gland. AANAT is also expressed in the retina, where it may play other roles, including neurotransmission and detoxification (34). Melatonin peaks at night, swiftly decreasing in the morning after light exposure; AANAT follows the same pattern. We found ANAAT overexpressed in the morning in patients with AI under standard therapy (measured 2 hours after awakening). The lack of response (i.e., expression decrease) to a strong zeitgeber, such as daylight, observed in our patients provides insights on the detrimental effect of a nonphysiologic GC profile on the entire circadian machinery (35). The altered expressions of selected genes with a pivotal role in metabolism and innate immunity were all reversed to near normal when patients with AI switched from the standard multiple-times-a-day regimen to the once-daily modified-release hydrocortisone. Although the ETD between the two regimens could be artifactual and not necessarily linked to a clinical outcome, the fact that the postswitched gene analysis was more similar to that of controls (who are not taking exogenous GCs) suggest that the more physiologic replacement is the main cause for the entrainment of circadian genes. Finally, the correlation between the change in clinical variables (glycated hemoglobin, infection score, sADAM17, and sCD16) and the modulation in gene expression profile suggests that CAMK2D, CSNK1A1, GUSB, ONP3, PER3, PRF1, SP1, TIMELESS, and WEE1 are causally linked to the clinical outcomes observed in the main DREAM trial report (6). The current study has several advantages but also some limitations. Advantages include the random allocation, blinding of the assessors, strict inclusion criteria, non-crossover design, high number of circadian genes simultaneously evaluated in both nonpooled and pooled samples, and inclusion of a control group. The main limitation was the single-time evaluation for circadian gene expression. However, the presence of a control group, the standardization of timing and type of therapy, and the modality of sample collection increase the value of our results. Another limitation is that the two regimens can lead to a different total GC exposure, and some of the effects occur via GC-mediated activation of the mineralocorticoid receptor in monocytes (36, 37). A third limitation is that our study did not include protein analysis, requiring an abundant source material difficult to store in the context of a clinical trial, thus limiting functional relevance of the observed findings. Finally, some of the differences in expression of some genes observed in patients with AI could be related to the change in PBMC populations. To evaluate this aspect, we normalized gene expression through housekeeping genes and performed a pooled analysis on the T lymphocyte subset that remained stable among study groups throughout the trial. However, such analyses cannot be considered conclusive, and to address this complex biological bias newly designed studies are necessary, such as the use of single-cell approach analysis. In conclusion, cortisol acts as crucial synchronizer of the expression of several circadian genes. In AI, the multiple-times-a-day administration of GCs, as occurs with the standard replacement schemes, causes a desynchronization of the endogenous and exogenous zeitgebers that can be measured as a flattening of the oscillator in PBMCs (Supplemental Fig. 5). Switching to a once-daily regimen allows better entrainment of exogenous administration and endogenous clocks, particularly to CLOCK/BMAL- and CREB-related genes. The resynchronization correlates with clinical improvement. This study shows that assessment of the peripheral circadian oscillators in PBMCs offers a tool to elucidate clinical disorders related to circadian rhythm disruption, such as metabolic syndrome and malignancies in night-shift workers. A deeper knowledge of the role of the molecular clock misalignment in adrenal disorders will enable development of better treatment strategies. Abbreviations: Abbreviations: AI adrenal insufficiency ANCOVA analysis of covariance BMI body mass index DREAM Dual Release Hydrocortisone vs Conventional Glucocorticoid Replacement in Hypocortisolism ETD estimated treatment difference GC glucocorticoid PBMC peripheral blood mononuclear cell Acknowledgments The authors thank Letizia Ciccone for technical assistance in gene expression analysis. Financial Support: The study was funded by the Ministry of Education, University and Research Grants 2015ZTT5KB and RBAP109BLT (to A.M.I.).The funder of the study had no role in the study design, data collection, data analysis, data interpretation, or writing of the report. The funder had no role in the decision to submit for publication. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication. Clinical Trial Information: ClinicalTrials.gov no. NCT02277587 (registered 29 October 2014). Disclosure Summary: A.M.I. reports grants and personal fees from Shire and Novartis, personal fees from Otsuka and Menarini, and personal fees and nonfinancial support from Ipsen, beyond the confines of this work. R.P. reports grants and personal fees from Novartis, Pfizer, HRA Pharma, Shire, and Ipsen and personal fees from Ferring and Italfarmaco, beyond the confines of this work. A.L. reports personal fees from MSD, Novartis, Shire, Novo Nordisk, and Aegerion, beyond the confines of this work. A.B.G. confirms lecture fees and grants from Shire and Novartis, beyond the confines of this work. The remaining authors have nothing to disclose. References 1. Chrousos GP . Stress and disorders of the stress system . Nat Rev Endocrinol . 2009 ; 5 ( 7 ): 374 – 381 . Google Scholar CrossRef Search ADS PubMed 2. Scheiermann C , Kunisaki Y , Frenette PS . Circadian control of the immune system . Nat Rev Immunol . 2013 ; 13 ( 3 ): 190 – 198 . Google Scholar CrossRef Search ADS PubMed 3. Balsalobre A , Brown SA , Marcacci L , Tronche F , Kellendonk C , Reichardt HM , Schütz G , Schibler U . Resetting of circadian time in peripheral tissues by glucocorticoid signaling . Science . 2000 ; 289 ( 5488 ): 2344 – 2347 . Google Scholar CrossRef Search ADS PubMed 4. Johannsson G , Nilsson AG , Bergthorsdottir R , Burman P , Dahlqvist P , Ekman B , Engström BE , Olsson T , Ragnarsson O , Ryberg M , Wahlberg J , Biller BM , Monson JP , Stewart PM , Lennernäs H , Skrtic S . Improved cortisol exposure-time profile and outcome in patients with adrenal insufficiency: a prospective randomized trial of a novel hydrocortisone dual-release formulation . J Clin Endocrinol Metab . 2012 ; 97 ( 2 ): 473 – 481 . Google Scholar CrossRef Search ADS PubMed 5. Quinkler M , Miodini Nilsen R , Zopf K , Ventz M , Oksnes M. Modified-release hydrocortisone decreases BMI and HbA1c in patients with primary and secondary adrenal insufficiency . Eur J Endocrinol . 2015 ; 172 : 619 – 626 . Google Scholar CrossRef Search ADS PubMed 6. Isidori AM , Venneri MA , Graziadio C , Simeoli C , Fiore D , Hasenmajer V , Sbardella E , Gianfrilli D , Pozza C , Pasqualetti P , Morrone S , Santoni A , Naro F , Colao A , Pivonello R , Lenzi A . Effect of once-daily, modified-release hydrocortisone versus standard glucocorticoid therapy on metabolism and innate immunity in patients with adrenal insufficiency (DREAM): a single-blind, randomised controlled trial . Lancet Diabetes Endocrinol . 2018 ; 6 ( 3 ): 173 – 185 . Google Scholar CrossRef Search ADS PubMed 7. Bancos I , Hazeldine J , Chortis V , Hampson P , Taylor AE , Lord JM , Arlt W . Primary adrenal insufficiency is associated with impaired natural killer cell function: a potential link to increased mortality . Eur J Endocrinol . 2017 ; 176 : 471 – 480 . Google Scholar CrossRef Search ADS PubMed 8. Erichsen MM , Lovas K , Fougner KJ , Svartberg J , Hauge ER , Bollerslev J , Berg JP , Mella B , Husebye ES . Normal overall mortality rate in Addison’s disease, but young patients are at risk of premature death . Eur J Endocrinol . 2009 ; 160 : 233 – 237 . Google Scholar CrossRef Search ADS PubMed 9. Björnsdottir S , Sundström A , Ludvigsson JF , Blomqvist P , Kämpe O , Bensing S . Drug prescription patterns in patients with Addison’s disease: a Swedish population-based cohort study . J Clin Endocrinol Metab . 2013 ; 98 ( 5 ): 2009 – 2018 . Google Scholar CrossRef Search ADS PubMed 10. Burman P , Mattsson AF , Johannsson G , Höybye C , Holmer H , Dahlqvist P , Berinder K , Engström BE , Ekman B , Erfurth EM , Svensson J , Wahlberg J , Karlsson FA . Deaths among adult patients with hypopituitarism: hypocortisolism during acute stress, and de novo malignant brain tumors contribute to an increased mortality . J Clin Endocrinol Metab . 2013 ; 98 ( 4 ): 1466 – 1475 . Google Scholar CrossRef Search ADS PubMed 11. Smans LC , Souverein PC , Leufkens HG , Hoepelman AI , Zelissen PM . Increased use of antimicrobial agents and hospital admission for infections in patients with primary adrenal insufficiency: a cohort study . Eur J Endocrinol . 2013 ; 168 : 609 – 614 . Google Scholar CrossRef Search ADS PubMed 12. Hahner S , Spinnler C , Fassnacht M , Burger-Stritt S , Lang K , Milovanovic D , Beuschlein F , Willenberg HS , Quinkler M , Allolio B . High incidence of adrenal crisis in educated patients with chronic adrenal insufficiency: a prospective study . J Clin Endocrinol Metab . 2015 ; 100 ( 2 ): 407 – 416 . Google Scholar CrossRef Search ADS PubMed 13. McVeigh TP , Sweeney KJ , Kerin MJ , Gallagher DJ . A qualitative analysis of the attitudes of Irish patients towards participation in genetic-based research . Ir J Med Sci . 2016 ; 185 ( 4 ): 825 – 831 . Google Scholar CrossRef Search ADS PubMed 14. Storey JD , Tibshirani R . Statistical significance for genomewide studies . Proc Natl Acad Sci USA . 2003 ; 100 ( 16 ): 9440 – 9445 . Google Scholar CrossRef Search ADS PubMed 15. Geiger AM , Pitts KP , Feldkamp J , Kirschbaum C , Wolf JM . Cortisol-dependent stress effects on cell distribution in healthy individuals and individuals suffering from chronic adrenal insufficiency . Brain Behav Immun . 2015 ; 50 : 241 – 248 . Google Scholar CrossRef Search ADS PubMed 16. Nader N , Chrousos GP , Kino T . Circadian rhythm transcription factor CLOCK regulates the transcriptional activity of the glucocorticoid receptor by acetylating its hinge region lysine cluster: potential physiological implications . FASEB J . 2009 ; 23 ( 5 ): 1572 – 1583 . Google Scholar CrossRef Search ADS PubMed 17. Nader N , Chrousos GP , Kino T . Interactions of the circadian CLOCK system and the HPA axis . Trends Endocrinol Metab . 2010 ; 21 ( 5 ): 277 – 286 . Google Scholar CrossRef Search ADS PubMed 18. Plat L , Leproult R , L’Hermite-Baleriaux M , Fery F , Mockel J , Polonsky KS , Van Cauter E . Metabolic effects of short-term elevations of plasma cortisol are more pronounced in the evening than in the morning . J Clin Endocrinol Metab . 1999 ; 84 ( 9 ): 3082 – 3092 . Google Scholar PubMed 19. Bahrami-Nejad Z , Zhao ML , Tholen S , Hunerdosse D , Tkach KE , van Schie S , Chung M , Teruel MN . A transcriptional circuit filters oscillating circadian hormonal inputs to regulate fat cell differentiation . Cell Metab . 2018 ; 27 ( 4 ): 854 – 868.e8 . Google Scholar CrossRef Search ADS PubMed 20. Lin E , Kuo PH , Liu YL , Yang AC , Kao CF , Tsai SJ . Effects of circadian clock genes and health-related behavior on metabolic syndrome in a Taiwanese population: evidence from association and interaction analysis . PLoS One . 2017 ; 12 ( 3 ): e0173861 . Google Scholar CrossRef Search ADS PubMed 21. O’Neill JS , Reddy AB . Circadian clocks in human red blood cells . Nature . 2011 ; 469 ( 7331 ): 498 – 503 . Google Scholar CrossRef Search ADS PubMed 22. Bergthorsdottir R , Leonsson-Zachrisson M , Odén A , Johannsson G . Premature mortality in patients with Addison’s disease: a population-based study . J Clin Endocrinol Metab . 2006 ; 91 ( 12 ): 4849 – 4853 . Google Scholar CrossRef Search ADS PubMed 23. Quinkler M , Ekman B , Zhang P , Isidori AM , Murray RD ; EU-AIR Investigators . Mortality data from the European Adrenal Insufficiency Registry: patient characterization and associations [published online ahead of print April 22, 2018]. Clin Endocrinol (Oxf). doi: 10.1111/cen.13609 . 24. Shostak A , Meyer-Kovac J , Oster H . Circadian regulation of lipid mobilization in white adipose tissues . Diabetes . 2013 ; 62 ( 7 ): 2195 – 2203 . Google Scholar CrossRef Search ADS PubMed 25. Morgan SA , McCabe EL , Gathercole LL , Hassan-Smith ZK , Larner DP , Bujalska IJ , Stewart PM , Tomlinson JW , Lavery GG . 11β-HSD1 is the major regulator of the tissue-specific effects of circulating glucocorticoid excess . Proc Natl Acad Sci USA . 2014 ; 111 ( 24 ): E2482 – E2491 . Google Scholar CrossRef Search ADS PubMed 26. Aggarwal A , Costa MJ , Rivero-Gutiérrez B , Ji L , Morgan SL , Feldman BJ . The circadian clock regulates adipogenesis by a Per3 crosstalk pathway to Klf15 . Cell Reports . 2017 ; 21 ( 9 ): 2367 – 2375 . Google Scholar CrossRef Search ADS PubMed 27. Butz H , Likó I , Czirják S , Igaz P , Khan MM , Zivkovic V , Bálint K , Korbonits M , Rácz K , Patócs A . Down-regulation of Wee1 kinase by a specific subset of microRNA in human sporadic pituitary adenomas . J Clin Endocrinol Metab . 2010 ; 95 ( 10 ): E181 – E191 . Google Scholar CrossRef Search ADS PubMed 28. Gérard C , Goldbeter A . Entrainment of the mammalian cell cycle by the circadian clock: modeling two coupled cellular rhythms . PLOS Comput Biol . 2012 ; 8 ( 5 ): e1002516 . Google Scholar CrossRef Search ADS PubMed 29. Mayr B , Montminy M . Transcriptional regulation by the phosphorylation-dependent factor CREB . Nat Rev Mol Cell Biol . 2001 ; 2 ( 8 ): 599 – 609 . Google Scholar CrossRef Search ADS PubMed 30. Wen AY , Sakamoto KM , Miller LS . The role of the transcription factor CREB in immune function . J Immunol . 2010 ; 185 ( 11 ): 6413 – 6419 . Google Scholar CrossRef Search ADS PubMed 31. Baran K , Dunstone M , Chia J , Ciccone A , Browne KA , Clarke CJ , Lukoyanova N , Saibil H , Whisstock JC , Voskoboinik I , Trapani JA . The molecular basis for perforin oligomerization and transmembrane pore assembly . Immunity . 2009 ; 30 ( 5 ): 684 – 695 . Google Scholar CrossRef Search ADS PubMed 32. Revelo XS , Tsai S , Lei H , Luck H , Ghazarian M , Tsui H , Shi SY , Schroer S , Luk CT , Lin GH , Mak TW , Woo M , Winer S , Winer DA . Perforin is a novel immune regulator of obesity-related insulin resistance . Diabetes . 2015 ; 64 ( 1 ): 90 – 103 . Google Scholar CrossRef Search ADS PubMed 33. Bergthorsdottir R , Ragnarsson O , Skrtic S , Glad CAM , Nilsson S , Ross IL , Leonsson-Zachrisson M , Johannsson G . Visceral fat and novel biomarkers of cardiovascular disease in patients with Addison’s disease: a case-control study . J Clin Endocrinol Metab . 2017 ; 102 ( 11 ): 4264 – 4272 . Google Scholar CrossRef Search ADS PubMed 34. Tosini G , Chaurasia SS , Michael Iuvone P . Regulation of arylalkylamine N-acetyltransferase (AANAT) in the retina . Chronobiol Int . 2006 ; 23 ( 1–2 ): 381 – 391 . Google Scholar CrossRef Search ADS PubMed 35. Klein DC . Arylalkylamine N-acetyltransferase: “the Timezyme.” J Biol Chem . 2007 ; 282 ( 7 ): 4233 – 4237 . Google Scholar CrossRef Search ADS PubMed 36. Marzolla V , Armani A , Feraco A , De Martino MU , Fabbri A , Rosano G , Caprio M . Mineralocorticoid receptor in adipocytes and macrophages: a promising target to fight metabolic syndrome . Steroids . 2014 ; 91 : 46 – 53 . Google Scholar CrossRef Search ADS PubMed 37. Usher MG , Duan SZ , Ivaschenko CY , Frieler RA , Berger S , Schütz G , Lumeng CN , Mortensen RM . Myeloid mineralocorticoid receptor controls macrophage polarization and cardiovascular hypertrophy and remodeling in mice . J Clin Invest . 2010 ; 120 ( 9 ): 3350 – 3364 . Google Scholar CrossRef Search ADS PubMed Copyright © 2018 Endocrine Society

Journal

Journal of Clinical Endocrinology and MetabolismOxford University Press

Published: Aug 1, 2018

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off