Prospective Assessment of Mood and Quality of Life in Cushing Syndrome Before and After Biochemical Control

Abstract

Context

Cushing’s syndrome (CS) impairs quality of life (QoL) and mood. Prospective real-life data on posttreatment recovery and predictors of improvement are limited.

Objectives

Evaluate changes in QoL, depression, and anxiety in patients with CS, before and after biochemical control, and identify predictors of clinically meaningful improvement.

Design and Setting

Prospective observational study at a tertiary center.

Patients

Sixty-seven patients with endogenous CS (60 pituitary, 7 adrenal) were assessed with active disease and again after achieving biochemical control through surgery and/or medication.

Outcomes

Patient-reported outcomes included CushingQoL, Beck Depression Inventory-II (BDI-II), and State-Trait Anxiety Inventory (STAI).

Results

Mean and longest follow-up was 2.3 and 11.5 years, respectively. Treatment led to improvements in mean scores across all domains (QoL: +18.2 ± 20.9, BDI-II: −6.8 ± 8.6, STAI-State: −9.6 ± 12.5, STAI-Trait: −8.6 ± 12.6; all P < .001). However, a minimal important difference was achieved in 64.6% for QoL, 67.9% for BDI-II, and 53.2% and 52.8% for STAI subscales. After multivariable analysis, QoL improvements were predicted by lower baseline body mass index, pretreatment symptoms ❤ years, postoperative hydrocortisone replacement >6 months, and normal follow-up late-night salivary cortisol (LNSC). Depression improvements were predicted by symptoms ❤ years, normal follow-up LNSC, and surgical treatment. Anxiety improvements were predicted by younger age and >6 months postoperative hydrocortisone. Depression improved more gradually than QoL and anxiety.

Conclusion

Although effective treatment improves mood and QoL in CS, clinically meaningful recovery is variable and incomplete for some patients. Our findings highlight the need to limit diagnostic delay and provide comprehensive posttreatment care that includes normalization of cortisol circadian rhythm.

Endogenous Cushing’s syndrome (CS) is a rare disorder characterized by chronic cortisol excess, most commonly due to an ACTH-secreting pituitary tumor [Cushing disease (CD)], followed by a cortisol-secreting adrenal adenoma and ectopic ACTH production due to a nonpituitary tumor (1). CS is associated with multiple comorbidities including diabetes, obesity, hypertension, immune suppression, osteoporosis, and cardiovascular disease, among others (2). Apart from these, patients face a spectrum of neuropsychiatric disturbances including depression, anxiety, mania, sleep disorders, and even psychosis. These comorbidities significantly disturb quality of life (QoL) and may persist long after treatment (3-7).

As with many rare diseases, CS remains incompletely understood, and patients experience impaired disease perception, information gaps, and isolation. In this context, patient-reported outcomes (PROs) have become useful instruments to clarify these gaps and guide patient-centered care. Disease-specific tools (CushingQoL, Tuebingen CD-25) and generic mood scales (Beck Depression Inventory, State-Trait Anxiety Inventory [STAI; including State (STAI-S) and Trait (STAI-T), Hospital Anxiety and Depression Scale] have established impairments in QoL and mood both during active disease and in remission (48-11).

Although improvements are noted with treatment, recovery does not seem to be complete. Studies have reported persistently reduced QoL compared to the general population and the presence of depressive symptoms even 12 months postoperatively (49). Findings regarding anxiety are less consistent: while some studies did not support the increased prevalence of anxiety in patients with active CS compared to the general population (12), others reported higher anxiety traits among patients with CS (during active disease and in remission) (1314) with steady improvement at 6- and 12-month follow-up (15). Clinical trials with adrenal steroidogenesis inhibitors or pasireotide demonstrated that effective biochemical control can improve QoL and depression (16-18). However, it is unclear whether these improvements are clinically significant and if patients achieve normal QoL and depression scores.

The role of PROs in assessing recovery during the treatment journey of patients with CS has not been clearly established, and QoL and mood trajectories remain unclear, largely due to small samples, limited follow-up, and cross-sectional designs. Among available prospective studies using PROs in CS, only 3 (2 evaluating pasireotide and 1 osilodrostat) reported the proportion of patients who met the minimal important difference (MID), which is the score change reflecting a clinically meaningful improvement (17-19), while others have only reported statistically significant changes in mean score, an important but possibly less clinically relevant outcome (20-22). Real-world clinical management adds further complexity: postoperative glucocorticoid replacement, potential glucocorticoid-withdrawal symptoms, and 20% to 30% recurrence rates after initial surgical “cure” all suggest that, for many patients, recovery may follow a nonlinear course. To date, no clinical practice prospective study has systematically assessed QoL and mood across multiple timepoints, compared surgical and medical strategies within a single cohort, and limited inclusion to patients who achieved biochemical remission or control for at least 6 months. Therefore, the aims of this study were to evaluate changes in QoL, depression, and anxiety in a clinical practice cohort of patients with CS before and over time after biochemical control, report achievement rates of MID, and identify predictors of clinically meaningful improvement.

Methods

Study Design

This study includes prospective data from patients enrolled in an ongoing observational cohort study, which since 2017 enrolls patients with endogenous CS at Memorial Sloan Kettering Cancer Center (MSKCC) [prior to 2017, enrollment took place at Mount Sinai (2012-2017)]. In this protocol, CS patients being treated at the MSKCC Pituitary and Skull Base Tumor Center are enrolled at any point in their treatment journey and prospectively followed over time after surgical, medical, and/or radiation treatment. At each study visit, a detailed medical history and biochemical and clinical data are collected according to standard of care. Patients also complete validated psychological and QoL assessments.

The current analysis includes a cohort of 67 patients with CS: 60 with pituitary and 7 with adrenal CS. Each patient completed a baseline (active disease) visit and at least 1 follow-up visit after achieving surgical remission or endocrine control due to medical therapy.

From the total of 67 patients, we analyzed 73 distinct baseline-to-follow-up case pairs. Six patients experienced recurrence after surgery or were inadequately controlled while on medical therapy after their initial follow-up visit and underwent a subsequent change in treatment strategy. These instances were treated as separate case pairs when needed, enabling comparison of different treatment approaches. When analyzing for a single follow-up, visits were grouped by time: group 1 (G1): 6 months, group 2 (G2): 12-18 months, and group 3 (G3): 24 or more months posttreatment. Each patient contributed to 1 or multiple groups based on the number of their study visits. For patients with multiple visits receiving different treatments throughout the current study, each follow-up visit was categorized based on time since the most recent intervention to ensure that we assessed outcomes according to the duration of biochemical control. For patients who underwent surgery, the follow-up interval was calculated from the date of surgery; for those on medical therapy, it was calculated from the start of medication. In the subanalysis comparing treatment- or demographic-related score changes, the most recent available follow-up was used in each case. At each visit patients completed at least 1 of the following: Cushing QoL, Beck Depression Inventory-II (BDI-II), or STAI-S and STAI-T.

For multiple follow-up visits during remission or treatment, 28 patients were evaluated. For this subgroup, we examined their whole trajectory over time. We then stratified this subgroup by total follow-up duration (<2 years vs ≥2 years) and assessed for significant differences between these 2 categories where applicable.

For the baseline visit, ACTH-dependent pituitary and ACTH-independent adrenal Cushing’s was confirmed according to Endocrine Society guidelines (23). Surgical remission was defined as postoperative serum cortisol <5 μg/dL (<138 nmol/L) and requirement of glucocorticoid replacement, according to the Endocrine Society’s guidelines and the Pituitary Society’s recent consensus statement (2425). For patients managed medically, endocrine control was defined as normalization of 24-hour urinary free cortisol (UFC) and based on clinical review and assessment by E.B.G.

The study was approved by the institutional review board at MSKCC. All subjects gave written informed consent before participation.

Outcome Measurements

Cushing QoL

The Cushing QoL is a validated disease-specific questionnaire consisting of 12 questions on a 5-point scale ranging from “always” to “never” (for 10 questions) or “very much” to “not at all” (for 2 questions). Total score ranges from 12 to 60. This is converted to a 0 to 100 scale, with 0 indicating the worst and 100 the best QoL. It evaluates physical and psychological issues and can also be scored through these 2 distinct subscales. MID is defined as an increase of ≥10.1 (26).

BDI-II

The BDI-II is a validated 21-item patient-reported questionnaire. Patients self-rate each item on a scale from 0 to 3 based on how they were feeling during the past 2 weeks. Total score ranges from 0 (best) to 63 (worst); scores from 0 to 13 indicate no or minimal depression; 14 to 19, mild depression; 20 to 28, moderate depression; and 29 to 63, severe depression. MID is defined as a 20% reduction from baseline score (2728).

STAI

The STAI is an instrument with 2 subscales: State anxiety (STAI-S), which reflects the present moment, and Trait anxiety (STAI-T), which assesses a stable tendency toward anxiety. Both subscales consist of 20 items scored from 0 to 3. Total scores range from 0 to 60, with higher scores indicating greater anxiety. Prior studies suggest a change of 0.5× SDs—or approximately 5 to 10 points—as a reasonable threshold for MID. In our study, we defined the MID at 7 points, based on observed SD of change at 12.5 for STAI-S and 12.6 for STAI-T (29).

In this study, all score changes from baseline to follow-up were reported as positive values to uniformly represent improvement across measures. For BDI-II and STAI where higher scores indicate worse outcomes, the direction of change was inverted for consistency.

Hormone Assays

Hormone testing was performed at either the MSKCC clinical laboratory or external laboratories (Quest Diagnostics, Labcorp, Mayo Clinic Laboratories). Plasma ACTH was measured using Tosoh immunoassay [RRID:AB_2783633; normal range (NR): 7.4-64.3 pg/mL (1.6-14.2 pmol/L); MSKCC or 6 to 50 pg/mL (1.3-11.0 pmol/L); QuestDiagnostics] or electrochemiluminescence immunoassay [RRID:AB_3678556; NR: 7.2-63.3 pg/mL (1.6-13.9 pmol/L); LabCorp, Mayo Clinic Laboratories]. Serum cortisol was measured via either immunoassay [RRID:AB_2802133; NR: 4-22 µg/dL (110-607 nmol/L); QuestDiagnostics or 7-25 µg/dL (193-690 nmol/L); Mayo Clinic Laboratories], electrochemiluminescence immunoassay [RRID:AB_2802131; NR: 6.2-19.4 µg/dL; (171-535 nmol/L); LabCorp], or liquid chromatography–tandem mass spectrometry [LC-MS/MS; NR: 5-25 µg/dL (138-690 nmol/L)]. UFC was measured using LC-MS/MS [NR: 3.5-45 µg/24 hours (9.7-124 nmol/24 hours); MSKCC, Mayo Clinic Laboratories or 3.0 to 50 µg/24 hours (8.3-138 nmol/24 hours); Quest Diagnostics, LabCorp]. Late-night salivary cortisol (LNSC) was assessed via LC-MS/MS [NR: ≤ 0.09 µg/dL (2.5 nmol/L); QuestDiagnostics, LabCorp or <100 ng/dL (27.6 nmol/L); MSKCC, Mayo Clinic Laboratories]. LNSC values were analyzed categorically (normal vs abnormal), and patients were asked to provide 2 LNSC samples on separate evenings. Abnormal LNSC was defined as at least 1 value above the upper limit of normal for the assigned laboratory.

Comorbidities

Diabetes mellitus (DM) was defined by any of the following: hemoglobin A1c (HbA1c) > 6.4%, fasting blood glucose (FBG) ≥ 126 mg/dL (7.0 mmol/L), or use of at least 1 antidiabetic medication. Pre-DM was defined as HbA1c between 5.7% and 6.4% or FBG between 100 and 125 mg/dL (5.6-6.9 mmol/L). Women taking metformin for polycystic ovary syndrome were classified as nondiabetic only if their HbA1c and FBG values both before metformin initiation and at the time of CS diagnosis remained within the normal range. Hypertension was defined as systolic blood pressure ≥ 130 mmHg, diastolic blood pressure ≥ 80 mmHg, or use of any antihypertensive medication.

Statistical Analysis

Analyses were conducted using IBM SPSS for Windows (version 29.0, IBM Corp.). Data normality was assessed by the Shapiro–Wilk test. Descriptive statistics were used for demographic and clinical characteristics. Normally distributed data were compared by Student’s t-test and nonnormally distributed variables with the Mann–Whitney U-test. Paired T-tests were conducted to study mean changes from baseline to a single follow-up visit. For categorical characteristics and the MID, we calculated the achievement rates and used Pearson’s chi-square for comparisons where applicable. For patients with more than 2 follow-up visits ANOVA (repeated measures) was applied for the trajectory of each measurement over time. To identify predictors of improvement, univariable linear regression models for score change and logistic regression for MID achievement were performed using baseline visit and longest follow-up visit for each patient. Variables with P ≤ .10 or of clinical relevance were then entered into multivariable regression models—again, linear regression for score change and logistic regression for MID achievement—where each predictor was separately evaluated, adjusting for age, sex, and baseline score. Correlation analyses were performed using Pearson or Spearman correlation coefficients for data with normal or abnormal distribution, respectively. Correlation coefficients (r) were interpreted as follows: values between 0.0 and ±0.3: weak, between ±0.3 and ±0.7: moderate, and between ±0.7 and ±1.0: strong relationships. All statistical tests were 2-sided, and results were considered significant with P ≤ .05.

Results

Study Participants

From a cohort of 226 endogenous CS and silent ACTH tumor patients enrolled in our ongoing MSKCC prospective cohort study, we identified patients who had a baseline visit with active hypercortisolism, who had at least 1 follow-up visit while in surgical remission or medical control, and who had completed at least 1 of the evaluated questionnaires correctly. After excluding patients with silent ACTH tumors, those with missing data, and follow-up visits that did not meet remission criteria, we included 67 patients (56 females, 11 males) with a mean baseline age of 42.3 ± 13.1 years. Among these patients, 60 had CD and 7 had adrenal CS.

Further patient demographic information is shown in Tables 1 and 2.

 

Table 1.

Demographics and baseline characteristics

Demographic variable n = 67 patients
Age, years
 Mean (SD) 42.3 (13.1)
 Range 20-75
Sex, n (%)
 Female 56 (83.6)
CS subtype, n (%)
 CD 60 (89.6)
 Adrenal CS 7(10.4)
Race, n (%)
 White 50 (74.6)
 Black/African American 8 (11.9)
 Asian 2 (3.0)
 Other/unknown 7 (10.4)
24-hour UFC
 Mean (SD) 391.5 (1471) µg/24 hours,
1080 (4060) nmol/24 hours
 Median (IQR) 135.0 (82.7-220.0) µg/24 hours, 372 (228-607) nmol/24 hours
 Range (min-max) 29-12 346 µg/24 hours, 80-34 053 nmol/24 hours
LNSC, n (%)
 Normal 3 (4.5)
 Abnormal 59 (88.1)
 NA 5 (7.5)
Plasma ACTH
 Mean (SD) 70.7 (64.1) pg/mL, 15.6 (14.1) pmol/L
 Median (IQR) 56.0 (42.0-83.8) pg/mL, 12.3 (9.2-18.4) pmol/L
 Range (min-max) 11-416 pg/mL (2.4-91.5 pmol/L)
Prior recurrence at baseline, n (%) 16 (23.9)
Prior transsphenoidal surgery, n (%) 16 (23.9)
 1 9 (13.4)
 2 7(10.4)

Abbreviations: CD, Cushing disease; CS, Cushing’s syndrome; IQR, interquartile range; LNSC, late-night salivary cortisol; NA, not available; UFC, urinary free cortisol.

 

Table 2.

Baseline and follow-up data

Baseline Longest follow-up P-value
BMI (kg/m2)
 Mean (SD) 33.2 (7.6) 30.6 (8.5) <.001
 Median (IQR) 31.6 (26.8-37.3) 29.3 (25.3-34.8)
LNSC, n (%) <.001
 Normal 3 (4.5) 30 (44.7)
 Abnormal 59 (88.1) 16 (23.8)
 NA 5 (7.5) 21 (31.3)
DM, n (%) <.001
 DM 28 (41.8) 13 (19.4)
 Pre-DM 15 (22.4) 9 (13.4)
Hypertension, n (%) 55 (82.1) 35 (53.7) <.001
HbA1C (%) <.001
 Total mean (SD) 6.5 (1.8) 5.7 (0.9)
 DM/pre-DM mean (SD) 6.9 (1.8) 6.1 (1.0)
Antidiabetic medications, n (%) 20 (29.9) (22.4)
 1 12 (17.9) (13.4)
 2 1 (1.5) (3.0)
 3 3 (4.5) (1.5)
 Insulin 4 (6.0) 3 (4.5)
Antihypertensive medications, n (%) 34 (50.7) (37.3)
 1 15 (22.4) (19.4)
 2 10 (14.9) (11.9)
 ≥3 9 (13.4) 4 (6.0)
Other medications, n (%)
 Antidepressants 10 (14.9) 13 (19.4)
 Anxiolytics 12 (17.9) 12(17.9)
 Pain medications 16 (23.9) 23 (34.3)
 Sleep medications 16 (23.9) 21 (31.3)
Treatment at most recent follow-up,a n (%)
 Transsphenoidal surgery 44 (65.7)
 Medical therapy 18 (26.9)
 Bilateral adrenalectomy 3 (4.5)
 Radiation therapy 1 (1.5)
 Adrenalectomy (adrenal CS) 7 (10.4)

Abbreviations: BMI, body mass index; CS, Cushing’s syndrome; DM, diabetes mellitus; HbA1c, hemoglobin A1c; IQR, interquartile range; LNSC, late-night salivary cortisol.

a“n” refers to number of separate baseline-to-follow-up cases.

In total, there were 46 visits in G1, 31 in G2, and 24 in G3. At the most recent follow-up of each case, there were 24 visits in G1, 25 in G2, and 24 in G3.

The mean (range) duration from baseline to most recent follow-up was 28.3 (5-138) months in the overall cohort. The mean (range) follow-up duration since the most recent treatment was 6.3 (4-9) months for G1, 12.7 (10-18) months for G2, and 43.7 (23-120) months for G3. At their final follow-up visit, 44 patients (65.7%) achieved remission after transsphenoidal surgery (TSS), 18 (26.9%) were under medical control, 3 (4.5%) underwent bilateral adrenalectomy (BLA), 1 (1.5%) received radiation therapy (RT), and the 7 (10.4%) patients with adrenal CS underwent unilateral adrenalectomy (Table 2).

The following additional treatments were administered between this study’s baseline visit and longest follow-up: among the 44 patients treated with TSS at their latest follow-up, 1 underwent an additional TSS and 1 received medical therapy prior to TSS. Of the 18 medically managed patients at last follow-up, 8 (44.4%) had previously undergone TSS (3 of whom had 2 TSSs), and 2 of these 8 additionally received at least 1 different medication before switching to the 1 recorded at their last follow-up. Two (11.1%) other patients received 2 sequential medications before the final 1 at follow-up, and 1 (5.6%) patient was on a block-and-replace regimen with hydrocortisone (HC) after 2 TSSs and BLA. The complete treatment journey of patients on medical therapy, before and after entering the study, is shown in Fig. 1. Among the patients who underwent BLA at last follow-up, 1 had 2 prior TSSs, 1 had a sin1 gle prior TSS and received medical therapy and had 2 TSSs and received medical therapy. The patient treated with RT had 2 prior TSSs and received medical therapy.

 

Treatment journey of the 18 patients on medical therapy at their longest follow-up. Each row represents the longitudinal treatment course of each patient before and/or after entering the study. Multiple boxes indicating medical therapy within the same patient represent different medications administered over time. Segments outlined in bold represent the follow-up period analyzed in the current cohort, from this' study baseline to the longest available follow-up.

Figure 1.

Treatment journey of the 18 patients on medical therapy at their longest follow-up. Each row represents the longitudinal treatment course of each patient before and/or after entering the study. Multiple boxes indicating medical therapy within the same patient represent different medications administered over time. Segments outlined in bold represent the follow-up period analyzed in the current cohort, from this’ study baseline to the longest available follow-up.

Abbreviations: CT, clinical trial; Keto, ketoconazole; Levo, levoketoconazole; Mety, metyrapone; Mife, mifepristone; Osilo, osilodrostat; Pasi, pasireotide.

Sixteen patients presented with recurrent disease; an additional 9 patients (13.4%) developed recurrent or persistent disease after surgery. HC replacement was administered at 21 of the longest available follow-up visits [6 due to ongoing hypopituitarism or adrenal insufficiency (AI) and 15 for temporary postoperative AI], with another 9 cases receiving replacement at intermediate follow-up visits.

All 18 patients on medical therapy at their longest follow-up received adrenal steroidogenesis inhibitors: osilodrostat (8 patients, 44.4%), metyrapone (6 patients, 33.3%), and ketoconazole (4 patients, 22.2%).

Comorbid Conditions

As shown in Table 2, mean body mass index (BMI) at baseline was 33.2 ± 7.6 kg/m2. Twenty-eight (41.8%) patients presented with DM, 15 (22.4%) with prediabetes, and 24 (35.8%) without DM. Fifty-five of 67 patients (82.1%) had hypertension at baseline. At the longest follow-up, mean BMI decreased to 30.6 ± 8.5 kg/m² (P < .001), and mean HbA1c decreased to 5.7 ± 0.9% (P < .001). Thirteen patients (19.4%) continued to have DM, and 9 patients (13.4%) had prediabetes. Hypertension was present in 35 patients (53.7%), of whom 25 (71.4%) were receiving at least 1 antihypertensive medication.

LNSC levels remained abnormal in 16 patients (23.8%), although LNSC data were not available for 21 patients (31.3%). Of those, LNSC testing was not considered clinically indicated in some cases, such as patients on HC replacement for postoperative AI (n = 10) or patients with adrenal CS status postadrenalectomy (n = 3). The remaining 8 patients with missing LNSC data were on medical therapy (n = 4) or status post-TSS (n = 4).

Cushing QoL

Sixty-five patients (71 baseline to follow-up case pairs) completed the CushingQoL assessment. In the overall cohort, treatment resulted in significant improvements in mean QoL scores at all follow-up time points: mean change in G1 was 16.6 ± 18.6 (P < .001); G2, 19.1 ± 19.4 (P < .001); and G3, 16.6 ± 27.1 (P = .009) (Table 3Fig. 2A). For longest available follow-up for each case, overall mean improvement was 18.2 ± 20.9 points (P < .001).

 

Score trajectory for (A) Cushing QoL, (B) BDI-II, (C) STAI-State, and (D) STAI-Trait in the overall cohort based on duration of follow-up, including patients with 2 follow-up visits. Significant improvements in mean scores were observed in all assessments and all follow-up time points except in group 3 STAI-State, noted with a gray line. Group 1: 6 months posttreatment, group 2: 12 to 18 months posttreatment, group 3: ≥ 24 months posttreatment.

Figure 2.

Score trajectory for (A) Cushing QoL, (B) BDI-II, (C) STAI-State, and (D) STAI-Trait in the overall cohort based on duration of follow-up, including patients with 2 follow-up visits. Significant improvements in mean scores were observed in all assessments and all follow-up time points except in group 3 STAI-State, noted with a gray line. Group 1: 6 months posttreatment, group 2: 12 to 18 months posttreatment, group 3: ≥ 24 months posttreatment.

Abbreviations: BDI-II, Beck Depression Inventory-II; QoL, quality of life; STAI, State-Trait Anxiety Inventory.

 

Table 3.

Cushing QoL scores at baseline, follow-up visit, and mean score change in each time-based group for total cohort, patients who had TSS and patients on medical therapy

Category Subgroup n Baseline mean Follow-up visit mean Mean change SD (change) P-value
Total cohort Longest follow-up 71 42.4 60.6 18.2 20.9 <.001
Group 1 45 40.6 57.2 16.6 18.6 <.001
Group 2 30 43.5 62.6 19.1 19.4 <.001
Group 3 23 41.2 57.9 16.6 27.1 .009
TSS Longest follow-up 42 40.0 59.9 20.0 18.5 <.001
Group 1 29 40.2 57.0 16.8 19.1 <.001
Group 2 21 41.4 61.9 20.4 15.8 <.001
Group 3 9 29.0 48.7 19.7 24.9 .045
Medical therapy Longest follow-up 19 46.3 58.4 12.1 26.2 .059
Group 1 9 44.6 56.7 12.1 18.5 .086
Group 2 7 40.9 57.1 16.3 31.4 .219
Group 3 10 56.0 62.0 6.0 27.9 .513

Abbreviations: QoL, quality of life; TSS, transsphenoidal surgery.

In the subanalysis by treatment strategy, 42 patients who completed the Cushing QoL achieved surgical remission and 19 patients were controlled on medical therapy. In the surgical cohort, improvement in scores were noted across all time groups with a mean score increase of 20.0 ± 18.5 points from baseline to the longest available follow-up (P < .001) (Figs. 3A and 4A). Among these patients, 15 had 2 follow-up visits; between them the mean score further increased by 9.6 ± 14.8 points, indicating significant QoL improvement >6 months postsurgery (P  = .025). In contrast, patients under medical control at follow-up showed a mean improvement of 12.1 ± 26.2 points from baseline to the longest follow-up, which did not reach statistical significance (n = 19; P  = .059) (Table 3Figs. 3A and 4A).

 

Mean score change in total cohort, patients after transsphenoidal surgery, and patients on medical therapy based on duration of follow-up: (A) Cushing QoL, (B) BDI-II, (C) STAI-State, (D) STAI-Trait. Direct comparison between the 2 treatment modalities was performed only in the longest available follow-up visit for each patient. Caps represent SEM. Only P-values ≤ .05 are displayed.

Figure 3.

Mean score change in total cohort, patients after transsphenoidal surgery, and patients on medical therapy based on duration of follow-up: (A) Cushing QoL, (B) BDI-II, (C) STAI-State, (D) STAI-Trait. Direct comparison between the 2 treatment modalities was performed only in the longest available follow-up visit for each patient. Caps represent SEM. Only P-values ≤ .05 are displayed.

Abbreviations: BDI-II, Beck Depression Inventory-II; QoL, quality of life; STAI, State-Trait Anxiety Inventory.

 

Mean scores at baseline and longest follow-up in total cohort, patients after transsphenoidal surgery, and patients on medical therapy for (A) Cushing QoL, (B) BDI-II, (C) STAI-State, (D) STAI-Trait. Caps represent SEM. Asterisks (*) indicate significant change from baseline to follow-up (P ≤ .05) and brackets significant differences between the 2 treatment modalities at the longest follow-up visit (P ≤ .05).

Figure 4.

Mean scores at baseline and longest follow-up in total cohort, patients after transsphenoidal surgery, and patients on medical therapy for (A) Cushing QoL, (B) BDI-II, (C) STAI-State, (D) STAI-Trait. Caps represent SEM. Asterisks (*) indicate significant change from baseline to follow-up (P ≤ .05) and brackets significant differences between the 2 treatment modalities at the longest follow-up visit (P ≤ .05).

Abbreviations: BDI-II, Beck Depression Inventory-II; QoL, quality of life; STAI, State-Trait Anxiety Inventory.

MID achievement and predictors of improvement

In the overall cohort, CushingQoL MID was achieved in 42 of the 65 patients (64.6%) (Fig. 5). When stratified by follow-up duration, MID achievement rates were 60.8% in G1 (n = 45), 70.0% in G2 (n = 30), and 60.9% (n = 23) in G3.

 

MID achievement rates for all patient-reported outcomes at most recent follow-up.

Figure 5.

MID achievement rates for all patient-reported outcomes at most recent follow-up.

Abbreviations: MID, minimal important difference.

Males (n = 11) improved more than female patients (n = 54) (27.8 ± 13.0 vs 15.5 ± 21.9; P  = .020) and achieved the MID more frequently (90.9% vs 59.3%; P  = .045). Even though they presented with lower baseline scores compared to females (33.2 ± 16.3 vs 44.3 ± 20.7), that difference was not significant (P  = .117).

Score change differed by BMI category, using as cut-off the baseline mean of our cohort (≤33.2 vs >33.2 kg/m²): patients with lower BMI (n = 34) improved considerably more than those with higher BMI (n = 31) (median score change: 26 vs 11; P = .023). Likewise, MID achievement was more common in the low-BMI group (76.5% vs 51.6%; P = .036).

Patients presenting with recurrent disease at baseline (n = 16) reported better baseline QoL than those with primary disease (n = 49) (51.6 ± 19.5 vs 39.5 ± 20.9; P = .046), and their mean improvement following treatment was smaller (7.2 ± 21.0 vs 21.0 ± 19.8; P = .022). Only 43.8% of recurrent cases achieved the MID compared to 71.4% of primary cases (P = .044).

Patients reporting symptom duration ≥3 years prior to diagnosis (n = 29) were less likely to achieve the MID compared to those with shorter symptom duration (n = 35) (48.3% vs 66.7%; P = .008).

Patients with at least 1 abnormal LNSC (n = 15) value at follow-up were less likely to meet MID compared to those with normal LNSC values (n = 28) (33.3% vs 75.0%; P = .008). Similarly, patients requiring HC replacement (after their first TSS or unilateral adrenalectomy for adrenal CS) for >6 months (n = 22) were more likely to achieve MID than those requiring ≤6 months (n = 30) (81.8% vs 50.0%; P = .019).

MID achievement rates between the TSS and medical-therapy groups differed (71.4% vs 47.4%) but did not reach significance (P = .070).

Baseline 24 hours UFC was inversely correlated with baseline CushingQoL score (ρ = −0.3; P = .035), indicating a relationship between biochemical and symptomatic disease severity.

BDI-II

Fifty-six patients (60 case pairs) were included in this subgroup. In the overall cohort, improvements in BDI-II score were seen at all follow-up time points: mean change in G1 was 4.7 ± 9.2 (P = .004); in G2, 7.7 ± 7.3 (P  < .001); and in G3, 7.6 ± 10.6 (P = .008). In the overall cohort, mean improvement from baseline to the longest follow-up was 6.8 ± 8.6 points (P  < .001) (Table 4Fig. 2B). Of note, a significant 7.3-point improvement was noted between follow-up G1 (6 months) and follow-up G2 (12 months) (n = 11, P = .025), indicating continued improvement in depressive symptoms over time after treatment.

 

Table 4.

BDI-II scores at baseline, follow-up visit, and mean score change in each time-based group for total cohort, patients who had TSS and patients on medical therapy

Category Subgroup n Baseline mean Follow-up visit mean Mean change SD (change) P-value
Total cohort Longest follow-up 60 15.7 8.9 6.8 8.6 <.001
Group 1 37 17.0 12.2 4.7 9.2 .004
Group 2 26 15.2 7.5 7.7 7.3 <.001
Group 3 18 15.9 8.3 7.6 10.6 .008
TSS Longest follow-up 32 17.1 8.2 8.8 8.1 <.001
Group 1 22 18.6 13.6 5.0 10.9 .043
Group 2 17 14.7 6.7 8.0 8.1 <.001
Group 3 6 20.5 8.3 12.2 4.7 .001
Medical therapy Longest follow-up 18 14.4 11.0 3.4 9.9 .159
Group 1 8 14.6 11.0 3.6 6.7 .171
Group 2 6 18.3 10.8 7.5 7.1 .049
Group 3 9 11.8 8.8 3.0 13.3 .517

Abbreviations: BDI-II, Beck Depression Inventory-II; TSS, transsphenoidal surgery.

Among the 32 patients who underwent TSS, improvements were noted across all follow-up time groups, with mean scores decreasing from 17.1 ± 10.9 to 8.2 ± 7.0 at the longest follow-up (P  < .001). In contrast, the 18 patients treated medically did not experience a significant change (P = .159). Improvement following TSS was significantly greater than with medical therapy at longest follow-up for each case (8.8 ± 8.1 vs 3.4 ± 9.9; P = .043) (Figs. 3B and 4B).

MID achievement and improvement predictors

Thirty-eight patients (67.9%) achieved MID by their longest follow-up (Fig. 5). Twenty-nine (51.8%) patients had baseline scores ≥14 points, indicating mild or moderate depression, and 23 (79.3%) of these patients met the MID. By follow-up duration, overall MID achievement rates were 56.8% in G1 (n = 37), 76.9% in G2 (n = 26), and 72.2% in G3 (n = 18).

By treatment approach, MID was met by 75.0% of patients who had TSS (n = 32) and 38.9% of patients on medication (n = 18) (P = .012). All patients who underwent BLA (n = 4) or RT (n = 1) and 5 out of 6 patients treated for adrenal CS achieved MID.

Patients with recurrent and primary disease did not differ in terms of baseline score (P = .267). However, those with recurrent disease were less likely to achieve MID (42.9% vs 76.2%; n = 14 vs 75.6%; n = 42, P = .021).

Symptom duration prior to diagnosis was inversely correlated with BDI-II score change (ρ = −0.33, P = .016). Patients experiencing symptoms for ≥3 years (n = 24) exhibited lower MID achievement rates compared to those with shorter symptom duration (n = 31) (50.0% vs 83.9%; P = .007).

Patients with normal LNSC at follow-up had higher MID achievement rates (81.5%; n = 27 vs 45.5%; n = 11, P = .026).

STAI

STAI-S

Fifty-six patients (60 case pairs) completed the STAI-State questionnaire. All follow-up time groups exhibited improvements, although in G3 the score decrease did not reach significance. In the overall cohort, mean scores declined from 44.8 ± 14.0 to 35.3 ± 11.2 at the longest follow-up (P < .001) (Table 5).

 

Table 5.

STAI scores at baseline, follow-up visit, and mean score change in total cohort, patients who had TSS and patients on medical therapy

Outcome Category Subgroup n Baseline mean Follow-up visit mean Mean change SD (change) P-value
STAI-State Total cohort Longest follow-up 60 44.8 35.3 9.6 12.5 <.001
Group 1 40 45.9 36.6 9.3 12.3 <.001
Group 2 25 46.2 35.3 10.8 10.8 <.001
Group 3 17 42.4 36.1 6.3 13.8 .078
TSS Longest follow-up 33 44.4 34.3 10.1 12.3 <.001
Group 1 24 44.4 35.8 8.6 11.9 .002
Group 2 16 43.7 33.9 9.8 11.9 .005
Group 3 7 46.0 37.9 8.1 12.1 .126
Medical therapy Longest follow-up 17 47.2 37.4 9.8 14.7 .014
Group 1 9 50.9 37.2 13.7 13.7 .017
Group 2 5 56.4 39.8 16.6 8.4 .012
Group 3 8 36.3 34.6 2.0 14.9 .715
STAI-Trait Total cohort Longest follow-up 58 46.0 37.3 8.6 12.6 <.001
Group 1 36 47.9 40.3 7.6 12.0 <.001
Group 2 26 45.7 36.0 9.6 10.9 <.001
Group 3 16 46.7 36.9 9.8 13.2 .010
TSS Longest follow-up 31 47.5 36.7 10.7 12.2 <.001
Group 1 22 47.9 40.6 7.3 11.5 .008
Group 2 16 46.3 35.9 10.4 11.4 .002
Group 3 6 54.0 37.8 16.2 7.5 .003
Medical therapy Longest follow-up 18 45.1 38.8 6.2 13.4 .065
Group 1 8 49.5 39.8 9.8 14.0 .089
Group 2 6 47.5 36.2 11.3 10.9 .052
Group 3 8 39.3 37.5 1.8 12.7 .709

Abbreviations: STAI, State-Trait Anxiety Inventory; TSS, transsphenoidal surgery.

By treatment modality, state anxiety improved in both the TSS group (10.1 ± 12.3; n = 33; P < .001) and patients on medical therapy (9.8 ± 14.7; n = 17; P = .014) (Figs. 3C and 4C).

MID achievement and improvement predictors

Overall, 30 of 56 (53.5%) patients achieved MID in STAI-State at their longest follow-up visit (Fig. 5). By follow-up duration, MID achievement rates were 52.5% in G1 (n = 40), 56.1% in G2 (n = 25), and 64.7% in G3 (n = 17).

A negative correlation was observed between STAI-S score change and baseline age (ρ = −0.3, P = .029). Patients >40 years old at baseline (n = 29), improved less than younger patients (n = 27) [median score change: 5 vs 13 (P = .017)] and were less likely to meet the MID, with results approaching statistical significance (41.4% vs 66.7%, P = .058).

STAI-T

Fifty-three patients (58 case pairs) were evaluated. In the overall cohort, mean score change from baseline to longest follow-up was 8.6 ± 12.6 points (P < .001). In time-based subgroups the following score reductions were noted: G1: 7.6 ± 12.0 (P < .001), G2: 9.6 ± 10.9 (P < .001), G3: 9.8 ± 13.2 (P = .010) (Fig. 2D). Among patients treated with TSS (n = 31), significant improvement was seen in every subgroup. Patients receiving medical therapy (n = 18) showed numerical but not statistically significant improvement (P = .065) (Table 5Figs. 3D and 4D).

MID achievement and improvement predictors

STAI-Trait MID was achieved by 28 (52.8%) patients at the longest follow-up (Fig. 5). By follow-up duration, MID achievement rates were 44.4% in G1, 53.8% in G2, and 68.8% in G3.

Patients ≤40 years at baseline (n = 26) improved more than those aged >40 years (n = 27), with results approaching significance [median score change: 14 vs 4 (P = .060)].

Patients with ≥2 Follow-up Visits

Twenty-eight patients had multiple follow-up visits; we stratified by follow-up duration (<2 years vs ≥2 years) [Table S1 (30)].

Cushing QoL

Significant improvements were noted in all groups with pairwise comparisons revealing higher scores in both first and second follow-up, with the mean score changing by 14.9 (P = .002) and 21.5 (P < .001) points, respectively, in total cohort.

BDI-II

Although the overall trajectory demonstrated significant improvement, pairwise comparisons showed no significant changes between baseline and first follow-up. Improvement was noted between baseline and the second follow-up visit (P < .001) and between the 2 treated visits (P = .021) (Table 6).

 

Table 6.

BDI-II mean scores and pairwise comparisons in patients with 2 follow-up visits

Comparison Mean score A Mean score B Mean difference P-value
Baseline vs follow-up 1 16.9 13.0 4.846 .200
Baseline vs follow-up 2 16.9 7.1 9.731 <.001
Follow-up 1 vs follow-up 2 13.0 7.1 4.885 .021

Abbreviations: BDI-II, Beck Depression Inventory-II.

STAI-S

Overall, the mean score decreased from 45.9 ± 13.0 at baseline to 38.3 ± 12.4 at the first follow-up and to 36.1 ± 10.9 at the second follow-up (P = .005). In cases with follow-up ≥2 years (n = 13), the score trajectory did not change significantly from baseline (P = .187). In contrast, patients with total follow-up <2 years (n = 11) exhibited significant improvement (P = .008).

STAI-T

Overall, the mean score decreased from 49.2 ± 9.0 at baseline to 39.8 ± 11.6 at first follow-up and further to 36.4 ± 10.5 at second follow-up (P < .001). Significant improvement noted from baseline to both follow-up visits in both subgroups (P < .001).

Regression Analyses for Predictors of Change

In all measurements, after controlling for age and sex, baseline score was an independent predictor of greater change (P < .001) (Table 7). Patients with more impaired QoL, or severe depression and anxiety at baseline, had more room for improvement.

 

Table 7.

Predictors of mean score change from baseline to most recent follow-up of each patient in univariable and multivariable linear regression analysis

Outcome Parameter Univariable analysis Multivariable analysis
Estimate SE P-value Estimate SE P-value
Cushing QoL score change Baseline score −0.50 0.11 <.001 −0.47 0.11 <.001
Baseline age −0.05 0.20 .797 −0.04 0.19 .825
Male sex 12.11 6.83 .081 7.49 6.68 .267
Baseline age ≤40 (vs >40) −3.43 5.23 .515 −4.90 4.89 .321
Normal LNSC (vs abnormal) −19.98 6.4 .004 −19.39 5.26 .001
HC replacement >6 months (vs ≤6 months) 10.06 5.90 .095 12.35 4.96 .016
Primary disease at baseline (vs recurrent) −13.19 5.86 .028 −6.63 5.60 .241
Baseline BMI ≤33.2 kg/m2 (vs >33.2 kg/m2) −8.72 5.1 .095 −6.53 4.71 .171
Symptom duration ❤ years (vs ≥3 years) −4.60 5.25 .384 −4.55 4.70 .337
Treatment (TSS vs medical therapy) −7.87 5.8 .185 −4.23 5.41 .473
BDI-II score change Baseline score 0.57 0.09 <.001 0.58 0.09 <.001
Baseline age −0.08 0.09 .402 0.02 0.08 .797
Male sex −0.59 3.07 .848 0.80 2.53 .752
Baseline age ≤40 (vs >40) −3.96 4.82 .429 −0.52 2.02 .800
Normal LNSC (vs abnormal) −3.01 3.06 .332 −3.27 1.87 .090
HC replacement >6 months (vs ≤6 months) 0.06 2.577 .980 2.33 1.90 .226
Primary disease at baseline (vs recurrent) −4.76 2.63 .076 −2.66 2.17 .224
Baseline BMI ≤33.2 kg/m2 vs >33.2 kg/m2 −3.79 2.29 .104 −1.41 1.90 .462
Symptom duration ❤ years (vs ≥3 years) −5.61 2.23 .015 −3.49 1.78 .055
Treatment (TSS vs medical therapy) −5.46 2.60 .041 −3.94 2.02 .057
STAI-State score change Baseline score 0.57 0.09 <.001 0.56 0.09 <.001
Baseline age −0.22 0.13 .104 −0.11 0.12 .338
Male sex −5.70 4.37 .197 −4.39 3.69 .239
Baseline age ≤40 (vs >40) −5.94 3.30 .078 −3.75 2.73 .175
Normal LNSC (vs abnormal) −2.15 3.95 .589 −4.47 2.89 .131
HC replacement >6 months (vs ≤6 months) 0.72 3.45 .836 4.42 2.81 .123
Primary disease at baseline (vs recurrent) 2.41 3.91 .743 2.14 2.91 .465
Baseline BMI ≤33.2 kg/m2 (vs >33.2 kg/m2) −2.36 3.38 .488 −0.93 2.56 .716
Symptom duration ❤ years (vs ≥3 years) −5.67 3.33 .095 −3.26 2.46 .192
Treatment (TSS vs medical therapy) −1.50 3.91 .970 −2.77 2.97 .355
STAI-Trait score change Baseline score 0.58 0.11 <.001 0.56 0.12 <.001
Baseline age −0.20 0.13 .128 −0.07 0.11 .562
Male sex −3.09 4.57 .502 −0.83 4.13 .841
Baseline age ≤40 (vs >40) −5.45 3.36 .111 −2.55 3.03 .405
Normal LNSC (vs abnormal) −6.52 4.23 .133 −6.74 3.44 .059
HC replacement >6 months (vs ≤6 months) 4.63 3.52 .195 7.11 2.87 .018
Primary disease at baseline (vs recurrent) −2.07 3.90 .597 −0.34 3.42 .921
Baseline BMI ≤33.2 kg/m2 (vs >33.2 kg/m2) −4.95 3.38 .150 −2.59 3.00 .393
Symptom duration ❤ years (vs ≥3 years) −5.78 3.37 .093 −4.35 2.80 .127
Treatment (TSS vs medical therapy) −4.49 3.74 .236 −3.39 3.11 .281

Each predictor in multivariable analysis was separately evaluated, adjusting for baseline age, sex, and baseline score. In models exploring baseline age <40 years as a categorical variable, continuous baseline age was not included in the multivariable model. Statistically significant results (P ≤ .05) are indicated in bold.

Abbreviations: BDI-II, Beck Depression Inventory-II; BMI, body mass index; HC, hydrocortisone; LNSC, late-night salivary cortisol; QoL, quality of life; STAI, State-Trait Anxiety Inventory; TSS, transsphenoidal surgery.

Cushing QoL

Normal LNSC at follow-up and >6 months of postoperative HC replacement were predictors of QoL score improvement and MID achievement even after adjustment for baseline score, age, and sex. Lower baseline BMI and male sex, although significant in univariable analysis, were no longer significant in the multivariable linear model. However, a BMI < 33.2 kg/m² (P = .034) and symptom duration ❤ years prior to diagnosis (P = .005) remained statistically significant predictors of reaching the MID in the multivariable logistic model (Table 8Fig. 6). To determine if treatment modality modified the effect of LNSC, we built a model including baseline QoL score, age, sex, follow-up LNSC, and treatment type (TSS vs medical therapy). In this multivariable model, normal LNSC remained a significant predictor of improvement (P = .023).

 

MID achievement predictors after multivariable analysis for (A) Cushing QoL, (B) BDI-II, (C) STAI-State. Each predictor was analyzed in a separate logistic regression model after adjustment for baseline score, age, and sex. Predictors for trait anxiety are not shown, as a longer duration of postoperative HC replacement was a significant predictor only in the linear multivariable regression model.

Figure 6.

MID achievement predictors after multivariable analysis for (A) Cushing QoL, (B) BDI-II, (C) STAI-State. Each predictor was analyzed in a separate logistic regression model after adjustment for baseline score, age, and sex. Predictors for trait anxiety are not shown, as a longer duration of postoperative HC replacement was a significant predictor only in the linear multivariable regression model.

Abbreviations: BDI-II, Beck Depression Inventory-II; HC, hydrocortisone; LNSC, late-night salivary cortisol; QoL, quality of life; STAI, State-Trait Anxiety Inventory; TSS, transsphenoidal surgery.

 

Table 8.

Predictors of MID achievement from baseline to most recent follow-up of each patient in univariable and multivariable logistic regression models

Outcome Parameter Univariable analysis Multivariable analysis
Estimate SE P-value Estimate SE P-value
Cushing QoL MID achievement Baseline score 0.94 0.02 <.001 0.94 0.02 <.001
Baseline age 1.01 0.02 .548 1.02 0.03 .410
Male sex 6.89 1.09 .076 3.82 1.16 .249
Baseline age ≤40 (vs >40) 1.01 0.52 .987 1.27 0.62 .704
Normal LNSC (vs abnormal) 6.00 0.70 .011 22.82 1.17 .007
HC replacement >6 months (vs ≤6 months) 4.50 0.66 .023 14.49 0.99 .007
Primary disease at baseline (vs recurrent) 3.21 0.60 .050 1.78 0.68 .400
Baseline BMI ≤33.2 kg/m2 (vs >33.2 kg/m2) 3.05 0.54 .039 4.33 0.69 .034
Symptom duration ❤ years (vs ≥3 years) 4.29 0.56 .010 9.07 0.78 .005
Treatment (TSS vs medical therapy) 2.79 0.57 .074 2.36 0.68 .209
BDI-II MID achievement Baseline score 1.08 0.04 .064 1.08 0.04 .042
Baseline age 1.02 0.02 .510 1.01 0.03 .613
Male sex 5.28 1.10 .130 5.76 1.14 .126
Baseline age ≤40 (vs >40) 1.11 0.57 .854 1.05 0.63 .937
Normal LNSC (vs abnormal) 5.28 0.78 .033 14.86 1.25 .030
HC replacement >6 months (vs ≤6 months) 2.00 0.65 .288 2.32 0.71 .236
Primary disease at baseline (vs recurrent) 4.27 0.65 .026 2.67 0.71 .165
Baseline BMI ≤33.2 kg/m2 (vs >33.2 kg/m2) 1.94 0.58 .255 1.55 0.66 .504
Symptom duration < 3 years (vs ≥3 years) 5.20 0.64 .010 5.74 0.70 .012
Treatment (TSS vs medical therapy) 4.71 0.63 .014 4.19 0.69 .039
STAI-State MID achievement Baseline score 1.17 0.04 <.001 1.19 0.05 <.001
Baseline age 0.97 0.02 .241 0.96 0.03 .261
Male sex 1.95 0.71 .347 3.17 1.00 .249
Baseline age ≤40 (vs >40) 2.83 0.56 .061 5.87 0.89 .048
Normal LNSC (vs abnormal) 2.02 0.73 .337 2.41 1.04 .396
HC replacement >6 months (vs ≤6 months) 0.94 0.59 .943 2.66 0.97 .313
Primary disease at baseline (vs recurrent) 1.21 0.62 .757 2.15 0.92 .408
Baseline BMI ≤33.2 kg/m2 (vs >33.2 kg/m2) 2.05 0.54 .189 1.57 0.82 .584
Symptom duration < 3 years (vs ≥3 years) 1.39 0.55 .52 0.98 0.77 .980
Treatment (TSS vs medical therapy) 1.95 0.62 .279 1.44 0.78 .634
STAI-Trait MID achievement Baseline score 1.17 0.05 <.001 1.17 0.05 <.001
Baseline age 0.98 0.02 .295 0.97 0.03 .342
Male sex 2.33 0.75 .257 4.16 1.02 .161
Baseline age ≤40 (vs >40) 2.12 0.56 .175 2.32 0.76 .265
Normal LNSC (vs abnormal) 1.78 0.71 .416 1.48 0.96 .686
HC replacement >6 months (vs ≤6 months) 1.58 0.60 .450 4.21 0.95 .130
Primary disease at baseline (vs recurrent) 2.45 0.61 .138 2.06 0.90 .421
Baseline BMI ≤33.2 kg/m2 (vs >33.2 kg/m2) 1.98 0.54 .202 1.11 0.79 .891
Symptom duration < 3 years (vs ≥3 years) 1.09 0.53 .866 0.99 0.71 .984
Treatment (TSS vs medical therapy) 1.39 0.60 .585 1.18 0.82 .839

Each predictor in multivariable analysis was separately evaluated, adjusting for baseline age, sex and baseline score. In models exploring baseline age <40 years as a categorical variable, continuous baseline age was not included in the multivariable model. Statistically significant results (P ≤ .05) are indicated in bold.

Abbreviations: BDI-II, Beck Depression Inventory-II; BMI, body mass index; HC, hydrocortisone; LNSC, late-night salivary cortisol; MID, minimal important difference; QoL, quality of life; STAI, State-Trait Anxiety Inventory; TSS, transsphenoidal surgery.

BDI-II

Symptom duration ❤ years (P = .012), normal LNSC at follow-up (P = .030), and TSS (P = .039) instead of medical therapy (for CD) were statistically significant predictors of MID achievement in the multivariable logistic models even after adjusting for age, sex, and baseline score (Table 8Fig. 6).

STAI-S

In the multivariable logistic model adjusted for sex and baseline score, age <40 predicted higher odds of MID achievement (P = .041) (Table 8Fig. 6).

STAI-T

After adjustments for sex and baseline score, age group <40 was no longer a predictor of improvement. Although nonsignificant in univariable screening, duration of postoperative HC replacement >6 months emerged as a significant predictor of score change, though not MID achievement, after adjusting for age, sex, and baseline score (Tables 7 and 8).

Discussion

In a clinical practice cohort of patients with CS followed prospectively before and over time up to 11.5 years after surgical remission and/or biochemical control from medical treatment, we identified significant improvements in mean QoL, depression, and anxiety scores in the overall cohort, but only half of patients achieved clinically meaningful improvements in anxiety, as assessed by MID, and about two-thirds of the cohort achieved clinically meaningful improvements in QoL and depression at their most recent follow-up. When assessed by treatment strategy, surgery resulted in statistically significant improvements in all 3 measures, whereas medical therapy resulted in statistically significant improvements in state anxiety but not QoL or depression. These findings may be impacted by the smaller cohort size of the medically treated patients and more complex treatment journeys in the medically vs surgically treated patients. Overall, in this cohort of treated, biochemically controlled patients, several predictors of improvements were identified, including age, baseline BMI, duration of symptoms prior to treatment, duration of HC requirement after surgery, and LNSC normalization with treatment.

PRO studies in CS have shown that patients with CS are at risk for mood disorders and impaired QoL at diagnosis and that improvement posttreatment is often partial, delayed, or inconsistent, even after biochemical remission (3-12). The most recent prospective study confirmed persistent deficits in QoL and depressive symptoms up to 1 year postsurgery, with mean BDI-II scores remaining in the clinically significant range (9). As for anxiety, a prospective study reported high baseline anxiety in patients with CD, and, although it improved after surgery, a proportion continued to experience anxiety up to 1 year posttreatment (14). Neuroimaging supports a biological basis for these symptoms, with brain abnormalities (hippocampal atrophy, cortical thinning, white matter damage) seen after biochemical cure possibly explaining the long-term emotional and cognitive deficits in some patients (1215). As for previously reported predictors of improvement, male sex, lower BMI at follow-up (43132), LNSC normalization (17), and shorter duration of cortisol exposure (3233) emerged as independent predictors of better QoL. Persistent hormone deficits or arginine vasopressin deficiency were related to worse depression (9) while increased age and male sex predicted less anxiety (31). While some studies suggest that hypopituitarism and HC replacement are associated with poorer outcomes (1134), others found no significant difference (35). Limitations of these studies include the cross-sectional design (431-36), small cohort sizes (9), and lack of long-term follow-up >12 months (37), especially in the setting of clinical trials (17).

In our study, QoL, depression, and anxiety improved following treatment, but the patterns varied by domain and follow-up duration.

As for QoL, interestingly, patients with recurrent disease showed better baseline QoL scores than those with primary disease, possibly due to posttreatment surveillance, resulting in earlier diagnosis at recurrence vs initial presentation. Although patients on medical therapy showed a trend toward improvement with treatment, results did not reach significance, potentially due to sample size or the increased (better) baseline scores in patients with recurrent disease and thus those receiving medical treatment. Most patients on medical therapy had persistent or recurrent disease and have experienced longer, more complex treatment journeys (as depicted in Fig. 1) compared to those in surgical remission, which also may impact QoL and mood outcomes. Notably, in patients with 2 follow-up visits, QoL continued to significantly improve 6 months posttreatment in those treated surgically but not in the total cohort.

Multivariable analysis revealed several predictors of QoL improvement after treatment. LNSC normalization was independently associated with approximately 20 times higher odds of achieving the MID, indicating the clinical importance of recovery of cortisol circadian rhythm for treated CS patients and the need for further work to identify medical therapies and regimens that can facilitate this. Postoperative HC replacement for more than 6 months after surgery (indicating a longer hypothalamic-pituitary-adrenal axis recovery) was also associated with greater QoL improvement. This finding complements prior work showing an association between duration of postoperative HC replacement and long-term remission (3839). Lower baseline BMI and shorter symptom duration were predictive of MID achievement, though not of mean score change.

As for depression, patients with 2 follow-ups had a distinct pattern: no significant change between baseline and first follow-up but significant improvement between the 2 follow-up visits. This suggests that depression may take longer to improve, with more evident change >6 months after biochemical control, which contrasts prior work suggesting that anxiety takes longer than depression to improve (14). The delayed trajectory could reflect the structural brain changes seen in CS even in remission, which are partially reversible (1240). Our data showed that symptom duration > 3 years prior to diagnosis reduced MID achievement, consistent with the literature linking diagnostic delay to persistent depression (33). A normal follow-up LNSC was associated with approximately 15 times higher odds of achieving the MID after adjustment, again emphasizing the need to attempt LNSC normalization while on medical therapy (917).

As for anxiety, to date, no prospective study has assessed anxiety longitudinally using STAI, the gold standard for measuring and differentiating between trait and state anxiety (29). Our results confirm that anxiety improves after treatment; however, state and trait show different patterns. State anxiety was the only domain overall to improve significantly in the medical therapy group, while trait anxiety showed only a trend. Although age <40 predicted greater anxiety improvements in both, this remained significant only for state anxiety after adjustment in the logistic model. Trait anxiety improvements were predicted by longer postoperative HC replacement in the linear multivariable model, again suggesting that a shorter recovery time of the HPA axis may be an early indicator for identifying patients who require a closer follow-up. A normal LNSC at follow-up approached significance in the multivariable linear model, suggesting the importance of circadian rhythm recovery in trait anxiety improvement as well.

Across all measures, we found no baseline or outcome differences between pituitary and adrenal CS or between those on or off HC replacement at their last follow-up. Of note, our cohort was predominantly CD patients, and the small number of adrenal CS patients may limit the ability to detect a difference in the 2 cohorts.

Overall, discrepancies between mean change and MID achievement, as reflected in the linear and logistic models, respectively, highlight the importance of reporting both metrics when available, as they may capture different but clinically useful predictors.

We also observed differences between score change and MID achievement across different time groups within the same questionnaire. In STAI-State, G2 (12-18 months since most recent treatment) had greater score reductions than G3 (24 months or more posttreatment)—though change in G3 was nearly significant. However, a higher proportion of patients in G3 achieved MID. Looking at our data, G3 had the highest SD of mean change, indicating greater heterogeneity in treatment response, likely due to broader range of follow-up duration or higher medical therapy rates among patients: 45.5% (n = 10) in G3 vs 22.6% (n = 6) in G2% and 20% (n = 8) in G1. This variability in state anxiety is reflected in the subgroup of patients with 2 follow-up visits: those followed for >2 years showed no significant improvement, while those with <2 years did. Differential responses to long-term medical therapy, higher rates of loss to follow-up among postsurgical patients, or the negative impact of time on state anxiety symptoms may explain this. For BDI-II we used a percentage-based MID, which likely contributed to greater alignment with mean changes, and accounted for individual variability and baseline severity, factors especially relevant when applying generic tools in disease-specific contexts.

Of note, in the cohort overall, the mean follow-up score was within the normal range for depression (<14 for BDI-II) and anxiety (<40 for STAI) (41). This is an encouraging finding that, on average, patients with treated CS may have rates of depression and anxiety that are not clinically significant. Nevertheless, as shown in Table 2, rates of antidepressant, anxiolytic, pain, and sleep medication use did not decrease with treatment but instead were stable or increased numerically, although they were not statistically significant. Similarly, case-control studies have reported higher depression and anxiety levels in patients with CS in remission when compared to healthy controls, even if the mean scores were within the normal range for both groups (1542). Whether this difference is clinically significant still remains inconclusive. Taken together, these results emphasize the importance of multidisciplinary pituitary centers that integrate formal psychological services, including psychiatric care and social work support, to monitor and promote long-term mental health in this population.

Inclusion of both surgically and medically treated patients may be considered a limitation to the study, since it introduces heterogeneity in the cohort. However, including patients undergoing a range of treatments allows for analysis of CS cohorts as seen in a real-world practice rather than a controlled clinical trial setting, thus providing clinically valuable information. Another limitation of the study is the use of clinically available, rather than centralized, hormone assays, again introducing variability in our data. As this cohort included patients treated at our center, their endocrine testing followed standard of care, which did not include sending samples to a centralized laboratory. The use of antidepressants in a minority of patients could potentially affect depression scores. However, this is an unavoidable reality in patients with CS, and their use was stable over time (14.9% at baseline vs 19.4% at follow-up, P = .49). Given our prospective study design, which captured each patient’s change relative to their own baseline, and adjustment for baseline scores in multivariable models, any confounding is likely limited.

Despite these limitations, our data contribute to the literature as the largest clinical practice cohort to date that prospectively characterizes QoL and mood disturbances in CS patients, before and over time after achieving biochemical control. By incorporating 3 longitudinal time points, we identified that the greatest improvements occur within the first 6 months for QoL and anxiety, while depression improves more gradually beyond that point. Another strength of our approach is the use of score change and MID as outcomes when exploring potential predictors of improvement and not remission score per se, enabling more precise tracking of each patient’s progress and supporting an individualized approach by accounting for baseline severity.

In summary, this prospective analysis of mood and Qol in a clinical practice cohort of patients with CS showed that effective treatment of hypercortisolism improves depression, anxiety, and QoL, but one-third to one-half of patients do not experience clinically meaningful improvements in these measures. We identified predictors of improvement that highlight the need for early detection of CS and treatment strategies that allow for recovery of cortisol circadian rhythm. Psychological recovery in CS is heterogeneous, domain-specific, and not always aligned with biochemical normalization. Our findings support a model of care that extends beyond endocrine remission, integrating psychosocial follow-up and individualized treatment.

Acknowledgments

We would like to thank the people with Cushing’s syndrome who contributed their valuable time to this research.

Funding

This research was funded by the National Institutes of Health/National Cancer Institute Support Grant P30 CA008748.

https://academic.oup.com/jcem/advance-article/doi/10.1210/clinem/dgaf598/8307075?login=false

Challenges of Cushing’s Syndrome and Bariatric Surgery

Abstract

Cushing’s disease (CD), caused by an adrenocorticotropic hormone-secreting pituitary adenoma, is challenging to diagnose, especially in obese patients post-bariatric surgery.

This report discusses a misdiagnosed case of CD in a 42-year-old obese male with hypertension. CD was suspected only after surgery, confirmed by magnetic resonance imaging (MRI) showing a pituitary macroadenoma.

Despite transsphenoidal surgery and ketoconazole therapy, the patient suffered liver failure and died.

Among 20 CD reviewed cases in the literature, 65% were misdiagnosed. MRI and immunohistochemistry confirmed tumors, with 55% achieving remission post-surgery. Screening for CD before bariatric surgery may prevent mismanagement and complications.

Obesity-Related Hemodynamic Alterations in Patients with Cushing’s Disease

Abstract

Background: Cushing’s disease (CD) is associated with a specific form of metabolic syndrome that includes visceral obesity, which may affect cardiovascular hemodynamics by stimulating hypercortisolism-related metabolic activity. The purpose of this study was to evaluate the relationship between obesity and the hemodynamic profile of patients with CD.
Methods: This prospective clinical study involved a hemodynamic status assessment of 54 patients newly diagnosed with CD with no significant comorbidities (mean age of 41 years). The assessments included impedance cardiography (ICG) to assess such parameters as stroke index (SI), cardiac index (CI), velocity index (VI), acceleration index (ACI), Heather index (HI), systemic vascular resistance index (SVRI), and total arterial compliance index (TACI) as well as applanation tonometry to assess such parameters as central pulse pressure (CPP) and augmentation index (AI). These assessments were complemented by echocardiography to assess cardiac structure and function.
Results: Compared with CD patients without obesity, individuals with CD and obesity (defined as a body mass index ≥ 30 kg/m2) exhibited significantly lower values of ICG parameters characterizing the pumping function of the heart (VI: 37.0 ± 9.5 vs. 47.2 ± 14.3 × 1*1000−1*s−1, p = 0.006; ACI: 58.7 ± 23.5 vs. 76.0 ± 23.5 × 1/100/s2, p = 0.005; HI: 11.1 ± 3.5 vs. 14.6 ± 5.5 × Ohm/s2, p = 0.01), whereas echocardiography in obese patients showed larger heart chamber sizes and a higher left ventricular mass index. No significant intergroup differences in blood pressure, heart rate, LVEF, GLS, TACI, CPP, or AI were noted.
Conclusions: Hemodynamic changes associated with obesity already occur at an early stage of CD and manifest via significantly lower values of the ICG parameters illustrating the heart’s function as a pump, despite the normal function of the left ventricle in echocardiography.

Graphical Abstract

1. Introduction

Cushing’s disease (CD), caused by a pituitary neuroendocrine tumor, leads to a specific type of metabolic syndrome that includes hypertension, obesity, impaired glucose metabolism, and dyslipidemia [1,2,3]. Chronic hypercortisolemia in patients with CD results in the excessive accumulation of visceral fat due to abnormal adipokine production [4]. Visceral obesity plays an important role in hypercortisolism-induced metabolic abnormalities and increased activity of the renin–angiotensin–aldosterone system activity in patients with CD [1,2,3,4,5]. Visceral obesity in patients with CD not only contributes to metabolic syndrome, but it is also an independent risk factor for cardiovascular disease [1,3,6,7]. Importantly, the structure and function of adipose tissue in patients with CD differ from those of healthy individuals [1,8,9]. The various hypercortisolism-induced metabolic abnormalities occurring in obese patients with CD may affect cardiovascular hemodynamics. There are no data on the effect of obesity on the hemodynamic profile of patients with CD and also few data are known on the association between obesity and hemodynamic disturbances in people without CD [10,11]. It was shown that the hemodynamic profile of a person with obesity is characterized by increased cardiac output and thoracic fluid content and decreased vascular resistance in comparison with these parameters in healthy individuals [12].
More studies are needed to enhance our understanding of the pathophysiology of CD-related obesity as a modifiable cardiovascular risk factor, in order to develop effective preventive and therapeutic strategies. Unfortunately, subclinical consequences of hypercortisolism in newly diagnosed patients with early CD, particularly with comorbid obesity, may be undetectable with standard methods. Therefore, novel and easy-to-use diagnostic methods would be of additive value to the standard methods of assessing cardiovascular structure and function in patients with CD. A detailed evaluation of the nature of obesity in patients with CD by innovative noninvasive diagnostic methods, such as impedance cardiography (ICG), applanation tonometry (AT), and echocardiographic assessment of global longitudinal strain (GLS), may provide additional data on cardiovascular hemodynamics, particularly the heart’s pumping function, preload, and afterload [13,14,15,16,17,18]. Our previous studies demonstrated the usefulness of ICG in identifying subclinical cardiovascular complications in patients with CD [19,20].
The purpose of this analysis was to assess the relationship between obesity and the hemodynamic profile of patients newly diagnosed with CD with no significant comorbidities.

2. Materials and Methods

2.1. Study Population

This was a prospective observational cohort study involving a comprehensive assessment of 54 patients (mean age of 41 years) newly diagnosed with CD with no significant comorbidities (although 64.8% were diagnosed with hypertension). These patients were admitted to the Military Institute of Medicine—National Research Institute between 2016 and 2021 in order to undergo a thorough cardiovascular assessment prior to transsphenoidal pituitary neuroendocrine tumor resection surgery.
This study was approved by the ethics committee at the Military Institute of Medicine—National Research Institute (approval No. 76/WIM/2016) and compliant with the Declaration of Helsinki and Good Clinical Practice guidelines. Each patient received detailed information on the purpose of this study and signed an informed consent form. This study was financed by the Polish Ministry of Research and Higher Education/Military Institute of Medicine—National Research Institute in Warsaw (grant No. 453/WIM).

2.2. Inclusion Criteria

The diagnosis of CD was established based on the presence of the typical (clinical and hormonal) evidence of hypercortisolism with no adrenocorticotropic hormone (ACTH) response to corticotropin-releasing hormone (CRH) stimulation, which meets the current guidelines for the diagnosis and treatment of CD [21,22,23]. Physical examination findings consistent with the signs and symptoms of CD, including central obesity with the characteristic altered body fat distribution (a moon face and a short, thick neck); muscle atrophy in the torso and limbs; purplish stretch marks on the abdomen, hips, and thighs; thinned skin; ecchymoses; signs and symptoms of hyperandrogenism; bone pain; frequent infections; erectile dysfunction in men; and secondary amenorrhea and infertility in women. Hormone test results included elevated 24 h urinary free cortisol levels, increased morning serum cortisol levels, altered circadian rhythmicity of ACTH and cortisol secretion, elevated or detectable morning serum ACTH, and a lack of overnight serum cortisol suppression to <1.8 mg/dL during a low-dose dexamethasone suppression test (1 mg or 2 mg of dexamethasone administered at midnight). In order to ensure a pituitary etiology of CD, all patients underwent a two-day high-dose (2 mg every 6 h = a total of 8 mg) dexamethasone suppression test (HDDST), which was expected to show low serum cortisol or a >50% decrease in urinary-free cortisol levels. Moreover, each patient was shown to have no ACTH secretion response to a CRH stimulation test (with 100 μg intravenous CRH), and the presence of a pituitary neuroendocrine tumor was confirmed via contrast magnetic resonance imaging of the pituitary. Patients with inconclusive hormone tests or imaging studies additionally underwent bilateral inferior petrosal sinus sampling (used to determine ACTH levels in the venous blood before and after CRH stimulation) [21,22,23].

2.3. Exclusion Criteria

The following comorbidities, which might considerably affect hemodynamic profiles, constituted our study exclusion criteria: (1) heart failure with mildly reduced or reduced left ventricular ejection fraction (LVEF) (i.e., LVEF of <50%); (2) cardiomyopathy; (3) clinically significant valvular heart disease or arrhythmia; (4) coronary artery disease, including a history of acute coronary syndrome; (5) a poor acoustic window on echocardiography; (6) a history of pulmonary embolism; (7) a history of a stroke or transient ischemic attack; (8) renal failure (estimated glomerular filtration rate < 60 mL/min/1.73 m2); (9) peripheral vascular disease and polyneuropathy; (10) chronic obstructive pulmonary disease; (11) respiratory failure (decreased partial pressure of arterial oxygen [PaO2] < 60 mmHg and/or increased partial pressure of carbon dioxide [PaCO2] > 45 mmHg); (12) a history of head trauma; (13) pregnancy; (14) age < 18 years; (15) no written informed consent.

2.4. Additional Hormone Tests

Due to the fact that hypercortisolemia inhibits gonadotropin release, hormone testing was expanded to include follicle-stimulating hormone and luteinizing hormone levels. The patients also had their serum thyroid-stimulating hormone levels tested to determine possible hypothyroidism, associated with reduced CRH and thyroid-stimulating hormone secretion and hypercortisolism-induced alterations in thyroid function. The patients with CD included in this study were not receiving any medications affecting the hypothalamus–pituitary–adrenal axis. None of the female patients with CD were pregnant at the time of the study or had given birth within the previous five years.

2.5. Laboratory Tests

In order to detect possible metabolic conditions, such as impaired fasting glucose, type 2 diabetes mellitus, or dyslipidemia, all patients underwent fasting blood tests from venous blood samples collected in the morning (at 6:00 a.m.). The tests evaluated the levels of fasting glucose, creatinine, eGFR, total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and triglycerides, as well as a complete blood count.

2.6. Anamnesis and Physical Examination

The patients were thoroughly evaluated for cardiovascular risk factors, cardiovascular signs and symptoms, a family history of cardiovascular disease, comorbidities, prescription medications and other drugs, and smoking.
The body mass index (BMI) was calculated, and obesity was determined based on the International Diabetes Federation and European Society of Cardiology guidelines, which define it as a BMI of ≥30 kg/m2 [24,25]. In the study, patients were divided into two groups: patients with CD and obesity (defined as high body mass index ≥ 30 kg/m2) and patients with CD without obesity (defined as normal BMI < 30 kg/m2).
Physical examination included the resting heart rate (HR), systolic and diastolic blood pressure, and anthropometric parameters.
Office blood pressure measurements were taken by a trained nurse in seated patients in the morning, after a 5 min rest. The blood pressure monitor used was Omron M4 Plus (Omron Healthcare Co. Ltd., Kyoto, Japan), which meets the European Society of Cardiology criteria [26].

2.7. Echocardiography

Two-dimensional echocardiography included standard parasternal, apical, and subcostal views with a 2.5 MHz transducer (VIVID E95, GE Medical System, Wauwatosa, WI, USA) in accordance with the American Society of Echocardiography (ASE) and the European Association of Cardiovascular Imaging (EACVI) guidelines [27]. The parasternal long-axis view was used to measure the left ventricular end-diastolic diameter (LVEDd), right ventricular end-diastolic diameter (RVEDd), interventricular septal thickness, and left atrial (LA) diameter. Linear 2-dimensional left ventricular measurements were used to calculate the left ventricular mass index (LVMI), which is the left ventricular mass divided by the body surface area (LVMI cut-off values of >115 g/m2 for men and >95 g/m2 for women meet ASE and EACVI criteria for the diagnosis of left ventricular hypertrophy). The LVEF was calculated with the biplane Simpson method, based on 2-dimensional views of the left ventricle during systole and diastole in four- and two-chamber apical views. The ascending aortic diameter, valvular structure and function, and pericardium were assessed. The patients were assessed for left ventricular diastolic dysfunction according to current guidelines. Pulse wave Doppler in an apical four-chamber view aligned with mitral valve tips was used to visualize mitral inflow, including the early passive blood inflow (E) and the later atrial (A) contribution to the mitral inflow, E/A ratio, and early mitral inflow deceleration time. Apical four-chamber views were used to determine the septal and lateral early diastolic mitral annular velocities (e′ avg), and the E/e′ avg ratio was calculated [27,28].
Global longitudinal strain (GLS) was assessed via electrocardiography-gated automated function imaging in two-, three-, and four-chamber views. The rates of >60 frames per second were used for optimal speckle-tracking strain assessment. Patients with a poor acoustic window were excluded from the study. Semiautomated endocardial border detection was initiated by manually selecting two points identifying the mitral annulus and one point at the apex. Segmental and whole-chamber strain was assessed. The results have been presented in the form of a “bull’s eye” graph. The data were analyzed for four-, three-, and two-chamber views, and average GLS was calculated [29].

2.8. Impedance Cardiography

Based on the phenomenon of impedance variability in individual body segments associated with regional arterial blood flow, ICG is a noninvasive tool for assessing cardiovascular hemodynamics. ICG assessments were conducted by a trained nurse with a Niccomo device (Medis, Ilmenau, Germany) in patients who had been resting for 10 min in a supine position. ICG data were recorded during a 10 min assessment and processed with dedicated software (Niccomo Software, Medis). We analyzed the mean values of the following hemodynamic parameters reflecting the pumping function of the heart: (1) stroke volume (SV [mL]) and stroke index (SI [mL/m2]), based on the following formula: SV = VEPT × (dZmax/Z0) × LVET, where VEPT is tissue volume calculated from body weight, height, and patient sex, Z0 is the initial thoracic impedance, dZmax is the maximum change in thoracic impedance, and LVET is the left ventricular ejection time; (2) cardiac output (CO [mL] = SV × HR), and cardiac index (CI [mL*m−2*min−1]); (3) velocity index (VI [1*1000−1*s−1]); (4) acceleration index (ACI [1/100/s2], which is the peak acceleration of blood flow in the aorta; and (5) Heather index (HI [Ohm/s2] = dZmax × TRC, where TRC the time interval between the R-peak in the electrocardiogram and the C-point on the impedance wave). We also conducted a detailed analysis of the following afterload parameters: (1) systemic vascular resistance (SVR [dyn*s*cm−5]) together with SVR index (SVRI [dyn*s*cm−5*m2]) and (2) total arterial compliance (TAC) and TAC index (TACI [mL/mmHg] = SV/pulse pressure [mL/mmHg*m2]). Preload was assessed based on thoracic fluid content (TFC [1/kOhm], based on the formula TFC = 1000/Z0, where Z0 is the initial thoracic impedance [30,31,32].

2.9. Applanation Tonometry

Applanation tonometry is a novel method of indirectly illustrating arterial pressure waveform in the aorta and arterial stiffness, which reflect left ventricular afterload. AT parameters were assessed noninvasively with a SphygmoCor system (AtCor Medical, Sydney, NSW, Australia). The measurements were taken in supine patients by a qualified nurse immediately after ICG. Radial artery pressure curves were recorded via AT with a micromanometer (Millar Instruments, Houston, TX, USA) strapped onto the left wrist. We selected high-quality recordings for our analysis. Radial pulse was calibrated against the latest brachial systolic and diastolic blood pressure measurement with an oscillometric module of the Niccomo device. SphygmoCor software (version 9.0; AtCor Medical Inc. Pty Ltd., Sydney, NSW, Australia) was used to process the arterial waveform and generate an appropriate aortic blood pressure curve from the radial pulse curve. The analyzed waveforms were composed of the pulse wave generated by the aorta and were augmented by an overlapping reflected wave. Our analyses yielded the following parameters: central systolic blood pressure; central diastolic blood pressure; central pulse pressure (CPP); augmentation pressure, which is the absolute increase in aortic systolic pressure (directly generated by left ventricular contraction) resulting from the reflection wave; and the augmentation index, calculated as AP × 100/CPP, which is a quotient of the augmentation pressure and the blood pressure in the aorta [33].

2.10. Statistical Analysis

For the statistical analysis of the results, we used MS Office Excel 2023 and Statistica 12.0 (StatSof Inc., Tulsa, OK, USA). Data distribution and normality were assessed visually on histograms and with the use of the Kolmogorov–Smirnov test. Continuous variables were expressed as mean ± standard deviation (SD) or median (interquartile range, IQR), and categorical variables were expressed as absolute and relative (percentage) values. In order to evaluate differences between the subgroups of CD patients with and without comorbid obesity, we used Student’s t-test for normally distributed data, and the Mann–Whitney U test for non-normally distributed data. A comparative analysis with the use of the Mann–Whitney U test was conducted on the data from patients stratified into two subgroups: patients with CD and obesity (BMI ≥ 30 kg/m2, n = 22) and patients with CD without obesity (BMI < 30 kg/m2, n = 32). The relationship between selected indices of cardiovascular function and obesity (represented as BMI) was analyzed separately for each one in a multivariable regression model, adjusting for age and hypertension as potential covariates related to hemodynamics. The threshold of statistical significance was adopted at p < 0.05.

3. Results

3.1. Baseline Characteristics

Nearly half of the patients with CD were found to be obese (n = 22, 40.7%). Overall, 20 of the 54 patients with Cushing’s disease (37%) were diagnosed with type 2 diabetes mellitus, 5 (9.3%) had prediabetes, and 29 (46.3%) had normal glucose tolerance. Of the patients with Cushing’s disease and type 2 diabetes, 14 received metformin, 5 received metformin with insulin, and 1 received insulin.
The mean age, HR, hemoglobin, creatinine, and sex distribution were similar in the subgroup with and without obesity (Table 1).
Table 1. Clinical, echocardiographic, hemodynamic, and applanation tonometry variables in patients with Cushing’s disease (CD) and with or without obesity.

3.2. Echocardiographic Assessment

Patients with CD and obesity (BMI ≥ 30 kg/m2) showed larger dimensions of heart chambers and ascending aorta (RVEDd, p < 0.001; LVEDd, p = 0.028; LA diameter, p < 0.001; aortic arch, p = 0.005) and higher rates of left ventricular mass index (LVMI, p = 0.028). We observed no significant differences between the subgroups in terms of the systolic (LVEF or GLS) or diastolic function of the left ventricle (Table 1).

3.3. ICG and AT Assessment

The most noticeable differences in ICG were observed for parameters of the left ventricular function as a pump. In obese individuals, VI (p = 0.006), ACI (p = 0.005), and HI (p = 0.012) were lower, whereas the systolic time ratio (STR) was higher (p = 0.038) than those in non-obese individuals, with SI and CI comparable in both subgroups. We observed no significant differences in afterload (TACI, SVRI, CPP, or augmentation index) or preload (TFC) parameters (Table 1).

3.4. Correlation Analysis

Analyzing the relationships between BMI and ICG hemodynamic parameters, we observed significant correlations, independent of sex and hypertension, between BMI and CI (R = 0.46; p < 0.001), SI (R = 0.29; p = 0.043), SVRI (−0.31; 0.028), and VI (R = −0.37; p = 0.0006)—see Table 2.
Table 2. Correlations between hemodynamic parameters assessed with impedance cardiography and body mass index, adjusted for sex and hypertension in multivariable regression models.

4. Discussion

The results of our study revealed a relationship between obesity and hemodynamic profile assessed via ICG in patients newly diagnosed with active CD. The use of novel diagnostic modalities demonstrated that excessive fat accumulation in young and middle-aged patients with CD, already at the early stages of the disease, is associated with some hemodynamic changes in the cardiovascular system, which—at that stage—may still be undetectable in routine assessments. These findings support the need for the early detection of subclinical heart dysfunction in patients with CD to enable early treatment and help prevent cardiovascular complications [1,34,35,36].
Occurring in 25%–100% of patients with CD, visceral obesity is one of the most common components of metabolic syndrome, often being the first sign of the disease. The duration of hypercortisolism correlates with obesity development [1,7,37,38], with chronic excessive cortisol levels being responsible for the abnormal distribution of adipose tissue [39]. The mechanisms behind this phenomenon may be due to the tissue overexpression of the 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1), which affects the pattern of excessive fat distribution in the torso, face, and neck [1,6]. Visceral obesity found in patients with CD is not only a component of metabolic syndrome but is in itself associated with increased metabolic activity, which makes it an independent cardiovascular risk factor, leading to the development of cardiovascular disease [1,4,9]. The tendency to accumulate visceral fat in patients with CD is also associated with abnormal adipokine production [4,6,40,41].
Our study included patients newly diagnosed with active CD with no clinically significant cardiovascular disease. Males were underrepresented in both subgroups. The proportion of patients with hypertension was 64.8%, which is comparable with that reported by other authors [38,42,43,44] and similarly distributed between subgroups. However, the patients in our study presented well-controlled hypertension (mean blood pressure was 126/83 mmHg), usually with one or two medications. Considering both sex and hypertension as potential confounders, these variables were included in regression models evaluating correlations between hemodynamics and BMI.
Similar to reports by other authors, our study showed higher SV and CO values in obese patients with CD; however, the respective indexed values (SI and CI) were comparable in obese and non-obese patients [12,45]. A more detailed ICG assessment demonstrated significant impairment of the pumping function of the heart as evidenced by lower HI, VI, and ACI values, and a higher STR value. The analysis of correlations revealed the independence of age and sex interrelation between some hemodynamic indices (CI, SI, SVRI, VI) and BMI. The paradox of the positive relation of obesity with volume indices of left ventricular function (CI and SI), which is negative with the marker of both its outflow and myocardial contractility (VI) encourages further studies investigating the (patho)physiological background of this phenomenon.
These findings were detected despite the lack of echocardiographic evidence of left ventricular systolic or diastolic dysfunction.
Moreover, our study showed larger heart chamber diameters and significantly higher LVMI in patients with CD and obesity, which is consistent with numerous earlier reports by other authors [46,47,48]. Nonetheless, it seems that in this case, increased heart chamber size and left ventricular hypertrophy should not be considered as only secondary to an increase in body weight. Hypercortisolism in patients with CD worsens the structural and functional condition of the heart muscle and may lead to myocardial fibrosis [48]. This results in myocardial remodeling associated with concentric left ventricular hypertrophy, which may impair left ventricular hemodynamic function, subsequently leading to myocardial dysfunction and symptomatic heart failure [49,50,51]. The effective treatment of patients with CD has been shown to normalize their serum cortisol levels and ultimately stop myocardial remodeling [47]. Therefore, the ICG-evidenced impaired pumping function of the heart may result from myocardial remodeling associated with complex metabolic and neuroendocrine changes in obese patients with CD [52]. These findings are consistent with previous reports on the adverse effect of obesity on left ventricular contractility [53,54,55,56].
The potential mechanisms underlying the results of our study remain to be elucidated. An interesting perspective is represented by the cross-talk between glucocorticoid (GR) and mineralocorticoid receptors (MR) and their impact on metabolic syndrome. Excessive activation of the MR in extra-renal tissues by aldosterone or glucocorticoids depending on the expression of 11beta-hydroxysteroid dehydrogenase type 2 has been shown to be associated with the development of vascular dysfunction and metabolic abnormalities, leading to obesity and metabolic syndrome. High concentrations of aldosterone may also activate the transcriptional function of the GR. These mechanisms result in an interaction between GR and MR in the regulation of adipogenesis [57].
The novelty of our approach is due to the use of noninvasive tools (ICG, AT) for hemodynamic assessment of the cardiovascular system in patients with CD to detect subclinical changes associated with obesity. On the one hand, our findings support earlier observations in other patient groups; on the other hand, they cast a new light on the relationship between obesity and an impaired hemodynamic profile in CD, which may result in the early development of cardiovascular complications.

4.1. Clinical Implications

We determined that a dysfunctional pumping action of the heart is the key marker of impaired cardiovascular hemodynamics in obese patients newly diagnosed with CD. The use of noninvasive diagnostic methods in this study revealed a complex relationship between obesity-related hemodynamic changes and the efficiency of left ventricular contractions. An early assessment of a patient’s hemodynamic profile may help detect subclinical cardiovascular dysfunction. Such a personalized approach may facilitate early therapeutic intervention and monitoring of treatment effectiveness focused on preventing myocardial remodeling and heart dysfunction.

4.2. Limitations

One limitation of our study was the small sample size. This was a result of the relatively low incidence of pituitary neuroendocrine tumors secreting ACTH. The exclusion of patients with clinically significant comorbidities further diminished the study population. However, this helped to eliminate the effect of additional factors on hemodynamic profiles. The patients assessed in our study were mostly young and middle-aged individuals with CD; therefore, our conclusions should not be extrapolated to older subjects. Although we conducted neither cardiac stress tests nor coronary angiography to exclude asymptomatic ischemic heart disease, other thorough assessments showed no physical, electrocardiographic, or echocardiographic evidence suggesting myocardial ischemia. Another potential limitation of our study is the fact that some patients had hypertension; however, it was well controlled with medications. The hemodynamic assessments involved the use of noninvasive methods as an alternative to the more expensive and less readily available invasive techniques. Nonetheless, we acknowledge the fact that noninvasive measurements can only provide indirect measurements and depend on the patient’s condition, which may vary over time.

5. Conclusions

The results of our study support the usefulness of ICG in diagnosing early heart dysfunction associated with obesity in patients with CD. Asymptomatic impairment of the heart’s pumping function seems to be the earliest clinical sign of cardiovascular hemodynamic abnormalities, which at this stage are still undetectable with standard echocardiography. Individual hemodynamic profile assessment with novel noninvasive diagnostic methods encourages further studies on cardiovascular system function in obese individuals with CD and on the use of personalized therapies, which aim at preventing adverse cardiovascular events.

Author Contributions

Conceptualization, A.J. and P.K.; methodology, A.J., P.K., G.G., B.U.-Ż., P.W. and G.Z.; software, P.K.; validation, A.J., P.K., B.U.-Ż., P.W. and G.Z.; formal analysis, P.K., P.W., G.G. and G.Z.; investigation, A.J., P.K., B.U.-Ż., P.W. and G.Z.; resources, A.J., P.K., B.U.-Ż., P.W. and G.Z.; data curation, A.J., P.K., B.U.-Ż., P.W., G.Z., A.K., R.W. and M.B.; writing—original draft preparation, A.J. and P.K.; writing—review and editing, G.G., B.U.-Ż., P.W. and G.Z.; visualization, A.J.; supervision, G.G. and G.Z.; project administration, G.Z.; funding acquisition, G.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Polish Ministry of Research and Higher Education/Military Institute of Medicine—National Research Institute in Warsaw (grant No. 453/WIM).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and Good Clinical Practice guidelines and approved by the Bioethics Committee at the Military Institute of Medicine—National Research Institute in Warsaw, Poland (approval No. 76/WIM/2016; 21 December 2016).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

Acknowledgments

We would like to thank the medical personnel of the Military Institute of Medicine—National Research Institute in Warsaw for the provided patient care.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Pivonello, R.; Isidori, A.M.; De Martino, M.C.; Newell-Price, J.; Biller, B.M.; Colao, A. Complications of Cushing’s syndrome: State of the art. Lancet Diabetes Endocrinol. 2016, 4, 611–629. [Google Scholar] [CrossRef]
  2. Dekkers, O.M.; Horváth-Puhó, E.; Jørgensen, J.O.; Cannegieter, S.C.; Ehrenstein, V.; Vandenbroucke, J.P.; Pereira, A.M.; Sørensen, H.T. Multisystem morbidity and mortality in Cushing’s syndrome: A cohort study. J. Clin. Endocrinol. Metab. 2013, 98, 2277–2284. [Google Scholar] [CrossRef] [PubMed]
  3. Pivonello, R.; Faggiano, A.; Lombardi, G.; Colao, A. The metabolic syndrome and cardiovascular risk in Cushing’s syndrome. Endocrinol. Metab. Clin. N. Am. 2005, 34, 327–339. [Google Scholar] [CrossRef] [PubMed]
  4. Pivonello, R.; De Leo, M.; Vitale, P.; Cozzolino, A.; Simeoli, C.; De Martino, M.C.; Lombardi, G.; Colao, A. Pathophysiology of diabetes mellitus in Cushing’s syndrome. Neuroendocrinology 2010, 1, 77–81. [Google Scholar] [CrossRef]
  5. Davy, K.P.; Hall, J.E. Obesity and hypertension: Two epidemics or one? Am. J. Physiol. Regul. Integr. Comp. Physiol. 2004, 286, R803–R813. [Google Scholar] [CrossRef]
  6. Lee, M.J.; Pramyothin, P.; Karastergiou, K.; Fried, S.K. Deconstructing the roles of glucocorticoids in adipose tissue biology and the development of central obesity. Biochim. Biophys. Acta 2014, 1842, 473–481. [Google Scholar] [CrossRef] [PubMed]
  7. Mancini, T.; Kola, B.; Mantero, F.; Boscaro, M.; Arnaldi, G. High cardiovascular risk in patients with Cushing’s syndrome according to 1999 WHO/ISH guidelines. Clin. Endocrinol. 2004, 61, 768–777. [Google Scholar] [CrossRef]
  8. Bujalska, I.J.; Kumar, S.; Stewart, P.M. Does central obesity reflect “Cushing’s disease of the omentum”? Lancet 1997, 349, 1210–1213. [Google Scholar] [CrossRef]
  9. Galton, D.J.; Wilson, J.P. Lipogenesis in adipose tissue of patients with obesity and Cushing’s disease. Clin. Sci. 1972, 43, 17P. [Google Scholar] [CrossRef]
  10. Koch, R.; Sharma, A.M. Obesity and cardiovascular hemodynamic function. Curr. Hypertens. Rep. 1999, 1, 127–130. [Google Scholar] [CrossRef]
  11. Raison, J. Conséquences cardiovasculaires de l’obésité associée à l’hypertension artérielle [Cardiovascular consequences of obesity associated with arterial hypertension]. Presse Med. 1992, 21, 1522–1525. [Google Scholar]
  12. de Simone, G.; Devereux, R.B.; Kizer, J.R.; Chinali, M.; Bella, J.N.; Oberman, A.; Kitzman, D.W.; Hopkins, P.N.; Rao, D.C.; Arnett, D.K. Body composition and fat distribution influence systemic hemodynamics in the absence of obesity: The HyperGEN Study. Am. J. Clin. Nutr. 2005, 81, 757–761. [Google Scholar] [CrossRef] [PubMed]
  13. Krzesiński, P.; Gielerak, G.; Kowal, J. Kardiografia impedancyjna—Nowoczesne narzedzie terapii monitorowanej chorób układu krazenia [Impedance cardiography—A modern tool for monitoring therapy of cardiovascular diseases]. Kardiol. Pol. 2009, 67, 65–71. (In Polish) [Google Scholar]
  14. El-Dawlatly, A.; Mansour, E.; Al-Shaer, A.A.; Al-Dohayan, A.; Samarkandi, A.; Abdulkarim, A.; Alshehri, H.; Faden, A. Impedance cardiography: Noninvasive assessment of hemodynamics and thoracic fluid content during bariatric surgery. Obes. Surg. 2005, 15, 655–658. [Google Scholar] [CrossRef] [PubMed]
  15. Eikås, J.G.; Gerdts, E.; Halland, H.; Midtbø, H.; Cramariuc, D.; Kringeland, E. Arterial Stiffness in Overweight and Obesity: Association with Sex, Age, and Blood Pressure. High Blood Press Cardiovasc. Prev. 2023, 30, 435–443. [Google Scholar] [CrossRef] [PubMed]
  16. Abomandour, H.G.; Elnagar, A.M.; Aboleineen, M.W.; Shehata, I.E. Subclinical Impairment of Left Ventricular Function assessed by Speckle Tracking in Type 2 Diabetic Obese and Non-Obese Patients: Case Control Study. J. Cardiovasc. Echogr. 2022, 32, 95–106. [Google Scholar] [CrossRef]
  17. Galderisi, M.; Lomoriello, V.S.; Santoro, A.; Esposito, R.; Olibet, M.; Raia, R.; Di Minno, M.N.; Guerra, G.; Mele, D.; Lombardi, G. Differences of myocardial systolic deformation and correlates of diastolic function in competitive rowers and young hypertensives: A speckle-tracking echocardiography study. J. Am. Soc. Echocardiogr. 2010, 23, 1190–1198. [Google Scholar] [CrossRef]
  18. Kalam, K.; Otahal, P.; Marwick, T.H. Prognostic implications of global LV dysfunction: A systematic review and meta-analysis of global longitudinal strain and ejection fraction. Heart 2014, 100, 1673–1680. [Google Scholar] [CrossRef]
  19. Jurek, A.; Krzesiński, P.; Gielerak, G.; Witek, P.; Zieliński, G.; Kazimierczak, A.; Wierzbowski, R.; Banak, M.; Uziębło-Życzkowska, B. Cushing’s Disease: Assessment of Early Cardiovascular Hemodynamic Dysfunction With Impedance Cardiography. Front. Endocrinol. 2021, 12, 751743. [Google Scholar] [CrossRef]
  20. Jurek, A.; Krzesiński, P.; Uziębło-Życzkowska, B.; Witek, P.; Zieliński, G.; Kazimierczak, A.; Wierzbowski, R.; Banak, M.; Gielerak, G. The patient’s sex determines the hemodynamic profile in patients with Cushing disease. Front. Endocrinol. 2023, 14, 1270455. [Google Scholar] [CrossRef]
  21. Fleseriu, M.; Auchus, R.; Bancos, I.; Ben-Shlomo, A.; Bertherat, J.; Biermasz, N.R.; Boguszewski, C.L.; Bronstein, M.D.; Buchfelder, M.; Carmichael, J.D.; et al. Consensus on diagnosis and management of Cushing’s disease: A guideline update. Lancet Diabetes Endocrinol. 2021, 9, 847–875. [Google Scholar] [CrossRef] [PubMed]
  22. Nieman, L.K.; Biller, B.M.; Findling, J.W.; Newell-Price, J.; Savage, M.O.; Stewart, P.M.; Montori, V.M. The diagnosis of Cushing’s syndrome: An Endocrine Society Clinical Practice Guideline. J. Clin. Endocrinol. Metab. 2008, 93, 1526–1540. [Google Scholar] [CrossRef]
  23. Ceccato, F.; Boscaro, M. Cushing’s syndrome: Screening and diagnosis. High. Blood Press. Cardiovasc. Prev. 2016, 23, 209–215. [Google Scholar] [CrossRef]
  24. Alberti, K.G.; Eckel, R.H.; Grundy, S.M.; Zimmet, P.Z.; Cleeman, J.I.; Donato, K.A.; Fruchart, J.C.; James, W.P.; Loria, C.M.; Smith, S.C., Jr.; et al. Harmonizing the metabolic syndrome: A joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 2009, 120, 1640–1645. [Google Scholar] [CrossRef]
  25. Visseren, F.L.J.; Mach, F.; Smulders, Y.M.; Carballo, D.; Koskinas, K.C.; Bäck, M.; Benetos, A.; Biffi, A.; Boavida, J.M.; Capodanno, D.; et al. 2021 ESC Guidelines on cardiovascular disease prevention in clinical practice. Eur. Heart J. 2021, 42, 3227–3337. [Google Scholar] [CrossRef] [PubMed]
  26. Williams, B.; Mancia, G.; Spiering, W.; Agabiti Rosei, E.; Azizi, M.; Burnier, M.; Clement, D.L.; Coca, A.; de Simone, G.; Dominiczak, A.; et al. 2018 ESC/ESH Guidelines for the management of arterial hypertension. Eur. Heart J. 2018, 39, 3021–3104. [Google Scholar] [CrossRef]
  27. Lang, R.M.; Badano, L.P.; Mor-Avi, V.; Afilalo, J.; Armstrong, A.; Ernande, L.; Flachskampf, F.A.; Foster, E.; Goldstein, S.A.; Kuznetsova, T.; et al. Recommendations for cardiac chamber quantification by echocardiography in adults: An update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. J. Am. Soc. Echocardiogr. 2015, 28, 1–39.e14. [Google Scholar] [CrossRef]
  28. Nagueh, S.F.; Smiseth, O.A.; Appleton, C.P.; Byrd, B.F.; Dokainish, H.; Edvardsen, T.; Flachskampf, F.A.; Gillebert, T.C.; Klein, A.L.; Lancellotti, P.; et al. Recommendations for the evaluation of left ventricular diastolic function by echocardiography: An update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. J. Am. Soc. Echocardiogr. 2016, 29, 277–314. [Google Scholar] [CrossRef]
  29. Voigt, J.U.; Pedrizzetti, G.; Lysyansky, P.; Marwick, T.H.; Houle, H.; Baumann, R.; Pedri, S.; Ito, Y.; Abe, Y.; Metz, S.; et al. Definitions for a common standard for 2D speckle tracking echocardiography: Consensus document of the EACVI/ASE/Industry Task Force to standardize deformation imaging. J. Am. Soc. Echocardiogr. 2015, 28, 183–193. [Google Scholar] [CrossRef] [PubMed]
  30. Bhalla, V.; Isakson, S.; Bhalla, M.A.; Lin, J.P.; Clopton, P.; Gardetto, N.; Maisel, A.S. Diagnostic Ability of B-Type Natriuretic Peptide and Impedance Cardiography: Testing to Identify Left Ventricular Dysfunction in Hypertensive Patients. Am. J. Hypertens. 2005, 18, 73S–81S. [Google Scholar] [CrossRef]
  31. Parrott, C.W.; Burnham, K.M.; Quale, C.; Lewis, D.L. Comparison of Changes in Ejection Fraction to Changes in Impedance Cardiography Cardiac Index and Systolic Time Ratio. Congest. Heart Fail. 2004, 10, 11–13. [Google Scholar] [CrossRef]
  32. Packer, M.; Abraham, W.T.; Mehra, M.R.; Yancy, C.W.; Lawless, C.E.; Mitchell, J.E.; Smart, F.W.; Bijou, R.; O’Connor, C.M.; Massie, B.M.; et al. Utility of Impedance Cardiography for the Identification of Short-Term Risk of Clinical Decompensation in Stable Patients with Chronic Heart Failure. J. Am. Coll. Cardiol. 2006, 47, 2245–2252. [Google Scholar] [CrossRef]
  33. Pauca, A.L.; O’Rourke, M.F.; Kon, N.D. Prospective evaluation of a method for estimating ascending aortic pressure from the radial artery pressure waveform. Hypertension 2001, 38, 932–937. [Google Scholar] [CrossRef] [PubMed]
  34. Coulden, A.; Hamblin, R.; Wass, J.; Karavitaki, N. Cardiovascular health and mortality in Cushing’s disease. Pituitary 2022, 25, 750–753. [Google Scholar] [CrossRef] [PubMed]
  35. Clayton, R.N. Cardiovascular complications of Cushings syndrome: Impact on morbidity and mortality. J. Neuroendocrinol. 2022, 34, e13175. [Google Scholar] [CrossRef] [PubMed]
  36. De Leo, M.; Pivonello, R.; Auriemma, R.S.; Cozzolino, A.; Vitale, P.; Simeoli, C.; De Martino, M.C.; Lombardi, G.; Colao, A. Cardiovascular disease in Cushing’s syndrome: Heart versus vasculature. Neuroendocrinology 2010, 1, 50–54. [Google Scholar] [CrossRef]
  37. Faggiano, A.; Pivonello, R.; Spiezia, S.; De Martino, M.C.; Filippella, M.; Di Somma, C.; Lombardi, G.; Colao, A. Cardiovascular risk factors and common carotid artery caliber and stiff ness in patients with Cushing’s disease during active disease and 1 year after disease remission. J. Clin. Endocrinol. Metab. 2003, 88, 2527–2533. [Google Scholar] [CrossRef] [PubMed]
  38. Witek, P.; Zieliński, G.; Szamotulska, K.; Witek, J.; Zgliczyński, W. Complications of Cushing’s disease—Prospective evaluation and clinical characteristics. Do they affect the efficacy of surgical treatment? Endokrynol. Pol. 2012, 63, 277–285. [Google Scholar] [PubMed]
  39. Geer, E.B.; Shen, W.; Gallagher, D.; Punyanitya, M.; Looker, H.C.; Post, K.D.; Freda, P.U. MRI assessment of lean and adipose tissue distribution in female patients with Cushing’s disease. Clin. Endocrinol. 2010, 73, 469–475. [Google Scholar] [CrossRef] [PubMed]
  40. Wang, M. The role of glucocorticoid action in the pathophysiology of the metabolic syndrome. Nutr. Metab. 2005, 2, 3. [Google Scholar] [CrossRef]
  41. Tataranni, P.A.; Larson, D.E.; Snitker, S.; Young, J.B.; Flatt, J.P.; Ravussin, E. Eff ects of glucocorticoids on energy metabolism and food intake in humans. Am. J. Physiol. 1996, 271, E317–E325. [Google Scholar] [PubMed]
  42. Isidori, A.M.; Graziadio, C.; Paragliola, R.M.; Cozzolino, A.; Ambrogio, A.G.; Colao, A.; Corsello, S.M.; Pivonello, R. ABC Study Group. The hypertension of Cushing’s syndrome: Controversies in the pathophysiology and focus on cardiovascular complications. J. Hypertens. 2015, 33, 44–60. [Google Scholar] [CrossRef]
  43. Dekkers, O.M.; Biermasz, N.R.; Pereira, A.M.; Roelfsema, F.; van Aken, M.O.; Voormolen, J.H.; Romijn, J.A. Mortality in patients treated for Cushing’s disease is increased, compared with patients treated for nonfunctioning pituitary macroadenoma. J. Clin. Endocrinol. Metab. 2007, 92, 976–981. [Google Scholar] [CrossRef] [PubMed]
  44. Lambert, J.K.; Goldberg, L.; Fayngold, S.; Kostadinov, J.; Post, K.D.; Geer, E.B. Predictors of mortality and long-term outcomes in treated Cushing’s disease: A study of 346 patients. J. Clin. Endocrinol. Metab. 2013, 98, 1022–1030. [Google Scholar] [CrossRef]
  45. Abel, E.D.; Litwin, S.E.; Sweeney, G. Cardiac remodeling in obesity. Physiol. Rev. 2008, 88, 389–419. [Google Scholar] [CrossRef]
  46. Hey, T.M.; Dahl, J.S.; Brix, T.H.; Søndergaard, E.V. Biventricular hypertrophy and heart failure as initial presentation of Cushing’s disease. BMJ Case Rep. 2013, bcr2013201307. [Google Scholar] [CrossRef] [PubMed]
  47. Toja, P.M.; Branzi, G.; Ciambellotti, F.; Radaelli, P.; De Martin, M.; Lonati, L.M.; Scacchi, M.; Parati, G.; Cavagnini, F.; Cavagnini, F. Clinical relevance of cardiac structure and function abnormalities in patients with Cushing’s syndrome before and after cure. Clin. Endocrinol. 2012, 76, 332–338. [Google Scholar] [CrossRef]
  48. You, K.H.; Marsan, N.A.; Delgado, V.; Biermasz, N.R.; Holman, E.R.; Smit, J.W.; Feelders, R.A.; Bax, J.J.; Pereira, A.M. Increased myocardial fibrosis and left ventricular dysfunction in Cushing’s syndrome. Eur. J. Endocrinol. 2012, 166, 27–34. [Google Scholar] [CrossRef]
  49. Baykan, M.; Erem, C.; Gedikli, O.; Hacihasanoglu, A.; Erdogan, T.; Kocak, M.; Kaplan, S.; Kiriş, A.; Orem, C.; Celik, S. Assessment of left ventricular diastolic function and Tei index by tissue Doppler imaging in patients with Cushing’s Syndrome. Echocardiography 2008, 25, 182–190. [Google Scholar] [CrossRef]
  50. Ainscough, J.F.; Drinkhill, M.J.; Sedo, A.; Turner, N.A.; Brooke, D.A.; Balmforth, A.J.; Ball, S.G. Angiotensin II type-1 receptor activation in the adult heart causes blood pressure-independent hypertrophy and cardiac dysfunction. Cardiovasc. Res. 2009, 81, 592–600. [Google Scholar] [CrossRef]
  51. Uziębło-Życzkowska, B.; Krzesinński, P.; Witek, P.; Zielinński, G.; Jurek, A.; Gielerak, G.; Skrobowski, A. Cushing’s Disease: Subclinical Left Ventricular Systolic and Diastolic Dysfunction Revealed by Speckle Tracking Echocardiography and Tissue Doppler Imaging. Front. Endocrinol. 2017, 8, 222. [Google Scholar] [CrossRef]
  52. Eschalier, R.; Rossignol, P.; Kearney-Schwartz, A.; Adamopoulos, C.; Karatzidou, K.; Fay, R.; Mandry, D.; Marie, P.Y.; Zannad, F. Features of cardiac remodeling, associated with blood pressure and fibrosis biomarkers, are frequent in subjects with abdominal obesity. Hypertension 2014, 63, 740–746. [Google Scholar] [CrossRef]
  53. Krzesiński, P.; Stańczyk, A.; Piotrowicz, K.; Gielerak, G.; Uziębło-Zyczkowska, B.; Skrobowski, A. Abdominal obesity and hypertension: A double burden to the heart. Hypertens. Res. 2016, 39, 349–355. [Google Scholar] [CrossRef] [PubMed]
  54. Wong, C.Y.; O’Moore-Sullivan, T.; Leano, R.; Byrne, N.; Beller, E.; Marwick, T.H. Alterations of left ventricular myocardial characteristics associated with obesity. Circulation 2004, 110, 3081–3087. [Google Scholar] [CrossRef] [PubMed]
  55. Parrinello, G.; Licata, A.; Colomba, D.; Di Chiara, T.; Argano, C.; Bologna, P.; Corrao, S.; Avellone, G.; Scaglione, R.; Licata, G. Left ventricular filling abnormalities and obesity-associated hypertension: Relationship with overproduction of circulating transforming growth factor beta1. J. Hum. Hypertens. 2005, 19, 543–550. [Google Scholar] [CrossRef] [PubMed]
  56. Wang, Q.; Gao, Y.; Tan, K.; Li, P. Subclinical impairment of left ventricular function in diabetic patients with or without obesity: A study based on three-dimensional speckle tracking echocardiography. Herz 2014, 40, 260–268. [Google Scholar] [CrossRef]
  57. Feraco, A.; Marzolla, V.; Scuteri, A.; Armani, A.; Caprio, M. Mineralocorticoid Receptors in Metabolic Syndrome: From Physiology to Disease. Trends Endocrinol. Metab. 2020, 31, 205–217. [Google Scholar] [CrossRef]
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ACTH-independent Cushing’s syndrome due to bilateral adrenocortical adenoma

https://doi.org/10.1016/j.radcr.2021.07.093

 

Abstract

The chronic excess of glucocorticoids results in Cushing’s syndrome. Cushing’s syndrome presents with a variety of signs and symptoms including: central obesity, proximal muscle weakness, fatigue striae, poor wound healing, amenorrhea, and others.

ACTH independent Cushing’s syndrome is usually due to unilateral adenoma. A rare cause of it is bilateral adrenal adenomas.

In this paper we report a case of a 43-year-old woman with Cushing’s syndrome due to bilateral adrenal adenoma.

Read the case report at https://www.sciencedirect.com/science/article/pii/S1930043321005690

Hypopituitarism and COVID-19 – exploring a possible bidirectional relationship?

As of September 1, 2021, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is the virus responsible for the coronavirus disease 2019 (COVID-19), has infected over 219 million and caused the deaths of over 4.5 million worldwide. Although COVID-19 has been traditionally associated with its ability to cause varied symptoms resembling acute respiratory distress syndrome (ARDS), emerging scientific evidence has demonstrated that SARS-CoV-2 causes much more damage beyond its effects on the upper respiratory tract.

To this end, in a recent study published in Reviews in Endocrine and Metabolic Disorders, the researchers discuss the extra-pulmonary manifestations of COVID-19.

Risk factors for severe COVID-19

It is now a well-known fact that the likelihood of people falling severely ill or dying from COVID-19 is increases if these individuals are obese, or have certain comorbidities like diabetes mellitus (DM), vitamin D deficiency, and vertebral fractures (VFs).

Any abnormality in the pituitary gland may lead to metabolic disorders, impaired immunity, and a host of other conditions that also make the body susceptible to infections. Since such conditions are common in patients with COVID-19 as well, it has been hypothesized that there might be a relationship between COVID-19 and pituitary gland disorders.

On the other hand, researchers have also observed that COVID-19 causes increased severity of pituitary-related disorders, and even pituitary apoplexy, which is a condition defined as internal bleeding or impaired blood supply in the pituitary gland. A group of Italian researchers has reviewed this bidirectional relationship between the pituitary gland abnormalities and COVID-19 in their study recently published in Reviews in Endocrine and Metabolic Disorders.

The link between pituitary gland abnormalities and COVID19

The pituitary gland releases hormones that regulate and control some of the most important functions of the body like growth, metabolism, energy levels, bone health, mood swings, vision, reproduction, and immunity, to name a few. The inability of the pituitary gland to release one or more of these hormones is known as ‘hypopituitarism.’  Factors responsible for hypopituitarism include traumatic brain injury, pituitary adenomas (tumors), genetic mutations, as well as infiltrative and infectious diseases.

Hypopituitarism can lead to severe cases of DM, growth hormone deficiency (GHD), abnormal lipid profile, obesity, arterial hypertension, and immune dysfunctions. Interestingly, similar consequences of COVID-19 have also been reported.

SARS-CoV-2 infects the human body by binding to a special class of receptors known as the angiotensin-converting enzyme 2 (ACE2) receptors. These receptors are located in the endothelial linings of most organs like the brain, heart, lungs, kidneys, intestine, liver, and pancreas, among others. The main function of the ACE2 receptors is binding to specific target molecules to maintain the renin-angiotensin system that is crucial for regulating dilation of blood vessels, as well as maintain blood glucose levels, the immune system, and homeostasis.

Therefore, SARS-CoV-2 binding to these ACE2 receptors facilitates the entry of this virus into all the organs that have these receptors, thus leading to the ability of SARS-CoV-2 to cause widespread damage in the body. Upon entry into the pancreas, for example, SARS-CoV-2 can inhibit ß-cells function, which worsens hyperglycemia and increases the risk for acute diabetic complications.

Similarly, the presence of ACE2 receptors in brain tissues may cause invasion into the pituitary gland and lead to pituitary apoplexy. The entry of SARS-CoV-2 into the brain can also cause neurological damage in infected patients, which may account for some of the common neurological complaints of COVID-19 including headaches, confusion, dysgeusia, anosmia, nausea, and vomiting.

Study findings

Hypopituitarism leading to metabolic syndrome has been scientifically linked to higher mortality in COVID-19 patients. In fact, the presence of a single metabolic syndrome component has been observed to double the risk of death by COVID-19. This risk was even higher among patients with DM and hypertension.

There was also an increased incidence of VFs in COVID-19 patients with hypopituitarism. Hence, patients with DM, obesity, hypertension, and chronic inflammatory disease, are all at an increased risk of poor outcomes and death in COVID-19.

Arterial hypertension is a common finding in adults with GHD, which is another consequence of hypopituitarism. Hypopituitarism also causes adrenal insufficiency, a condition that is primarily managed with glucocorticoids and hormonal replacement therapies.

Notably, patients with COVID-19 are often treated for prolonged periods with high-dose exogenous glucocorticoids, which is a class of steroids that suppress some activities of the immune system. This treatment approach may result in suppression of the hypothalamic-pituitary–adrenal axis that can lead to adrenal insufficiency.

Hypogonadism is another aspect of pituitary insufficiency that predisposes patients, especially males, to COVID-19. Evidence shows that males with hypogonadism were more frequently affected by metabolic syndrome.

Pituitary apoplexy, albeit rare, has also been linked to COVID-19, especially in patients with pituitary adenomas and those who are being treated with anticoagulant therapy. This may be because the pituitary gland becomes overstimulated during an infectious disease, which may increase pituitary blood demand and lead to sudden infarction precipitating acute apoplexy.

This phenomenon has also been shown in patients suffering from infectious diseases that cause hemorrhagic fevers. Taken together, pituitary apoplexy complicates treatment and management procedures in COVID-19 patients.

Despite the use of steroids in COVID-19 patients, there have been no contraindications for vaccination in such patients. However, those on extensive hormonal therapies need constant monitoring for best results.

Implications

The pituitary gland acts like a double-edged sword for COVID-19. On one end, hypopituitarism predisposes patients to metabolic disorders like DM, obesity, and VFs, all of which are known risk factors for COVID-19.

On the other hand, COVID-19 may cause direct or indirect damage to the pituitary glands by entering the brain and inducing unfavorable vascular events – though evidence on this remains lesser in comparison to that of hypopituitarism. Ultimately, the researchers of the current study conclude that managing patients with hormonal insufficiencies optimally with steroids is likely to improve outcomes in severe COVID-19.

Journal reference: