The Interaction of Insulin and Pituitary Hormone Syndromes

Schernthaner-Reiter, Marie Helene; Wolf, Peter; Vila, Greisa; Luger, Anton

doi:10.3389/fendo.2021.626427

Cited by 18 publications

(10 citation statements)

References 75 publications

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“…Hypopituitarism (including central hypogonadism), reported in 10–80% of acromegalic cases, and hyperprolactinemia (affecting more than one third of the patients) are associated with a higher rate of subfertility/infertility in acromegalic patients when compared to a non-acromegalic population of the same age, in both females and males [ 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 ]. Pituitary insufficiency and increased prolactin status are related to larger tumors; 90–95% of pituitary GH secreting tumors are macroadenomas; the additional negative role concerning fertility status is caused by secondary DM [ 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 ]. For instance, one study from 2022 on 529 acromegalic individuals identified a rate of hyperprolactinemia of 39.1% and a prevalence of hypopituitarism of 34.8%, with the gonadal axis being the most affected pituitary axis; there was a hypogonadism frequency of 29.7% [ 45 ].…”

Section: Sub/infertility Issues In Acromegalymentioning

confidence: 99%

Approach of Acromegaly during Pregnancy

et al. 2022

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Acromegaly-related sub/infertility, tidily related to suboptimal disease control (1/2 of cases), correlates with hyperprolactinemia (1/3 of patients), hypogonadotropic hypogonadism—mostly affecting the pituitary axis in hypopituitarism (10–80%), and negative effects of glucose profile (GP) anomalies (10–70%); thus, pregnancy is an exceptional event. Placental GH (Growth Hormone) increases from weeks 5–15 with a peak at week 37, stimulating liver IGF1 and inhibiting pituitary GH secreted by normal hypophysis, not by somatotropinoma. However, estrogens induce a GH resistance status, protecting the fetus form GH excess; thus a full-term, healthy pregnancy may be possible. This is a narrative review of acromegaly that approaches cardio-metabolic features (CMFs), somatotropinoma expansion (STE), management adjustment (MNA) and maternal-fetal outcomes (MFOs) during pregnancy. Based on our method (original, in extenso, English—published articles on PubMed, between January 2012 and September 2022), we identified 24 original papers—13 studies (3 to 141 acromegalic pregnancies per study), and 11 single cases reports (a total of 344 pregnancies and an additional prior unpublished report). With respect to maternal acromegaly, pregnancies are spontaneous or due to therapy for infertility (clomiphene, gonadotropins or GnRH) and, lately, assisted reproduction techniques (ARTs); there are no consistent data on pregnancies with paternal acromegaly. CMFs are the most important complications (7.7–50%), especially concerning worsening of HBP (including pre/eclampsia) and GP anomalies, including gestational diabetes mellitus (DM); the best predictor is the level of disease control at conception (IGF1), and, probably, family history of 2DM, and body mass index. STE occurs rarely (a rate of 0 to 9%); some of it symptoms are headache and visual field anomalies; it is treated with somatostatin analogues (SSAs) or alternatively dopamine agonists (DAs); lately, second trimester selective hypophysectomy has been used less, since pharmaco-therapy (PT) has proven safe. MNA: PT that, theoretically, needs to be stopped before conception—continued if there was STE or an inoperable tumor (no clear period of exposure, preferably, only first trimester). Most data are on octreotide > lanreotide, followed by DAs and pegvisomant, and there are none on pasireotide. Further follow-up is required: a prompt postpartum re-assessment of the mother’s disease; we only have a few data confirming the safety of SSAs during lactation and long-term normal growth and developmental of the newborn (a maximum of 15 years). MFO seem similar between PT+ve and PT-ve, regardless of PT duration; the additional risk is actually due to CMF. One study showed a 2-year median between hypophysectomy and pregnancy. Conclusion: Close surveillance of disease burden is required, particularly, concerning CMF; a personalized approach is useful; the level of statistical evidence is expected to expand due to recent progress in MNA and ART.

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Section: Sub/infertility Issues In Acromegalymentioning

confidence: 99%

Approach of Acromegaly during Pregnancy

et al. 2022

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“…Even subtle variations in sleep characteristics are likely to influence health via metabolic, endocrine and cardiovascular processes to adversely influence health. For instance, sleep impairments can lead to several hormonal changes that result in lower response of β cells to glucose and reduced insulin sensitivity (driven by lower glucagon‐like peptide‐1 levels) 40 . Conversely, modification of these behaviours through sleep interventions (e.g., sleep extension, sleep education or cognitive behavioural therapy for insomnia) has the potential to influence a number of health outcomes, including insulin sensitivity and energy intake 41,42 .…”

Section: Discussionmentioning

confidence: 99%

Twenty‐four‐hour physical behaviour profiles across type 2 diabetes mellitus subtypes

Henson,

Tziannou,

Rowlands

et al. 2024

Diabetes Obesity Metabolism

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AimTo investigate how 24‐h physical behaviours differ across type 2 diabetes (T2DM) subtypes.Materials and MethodsWe included participants living with T2DM, enrolled as part of an ongoing observational study. Participants wore an accelerometer for 7 days to quantify physical behaviours across 24 h. We used routinely collected clinical data (age at onset of diabetes, glycated haemoglobin level, homeostatic model assessment index of beta‐cell function, homeostatic model assessment index of insulin resistance, body mass index) to replicate four previously identified subtypes (insulin‐deficient diabetes [INS‐D], insulin‐resistant diabetes [INS‐R], obesity‐related diabetes [OB] and age‐related diabetes [AGE]), via k‐means clustering. Differences in physical behaviours across the diabetes subtypes were assessed using generalized linear models, with the AGE cluster as the reference.ResultsA total of 564 participants were included in this analysis (mean age 63.6 ± 8.4 years, 37.6% female, mean age at diagnosis 53.1 ± 10.0 years). The proportions in each cluster were as follows: INS‐D: n = 35, 6.2%; INS‐R: n = 88, 15.6%; OB: n = 166, 29.4%; and AGE: n = 275, 48.8%. Compared to the AGE cluster, the OB cluster had a shorter sleep duration (−0.3 h; 95% confidence interval [CI] −0.5, −0.1), lower sleep efficiency (−2%; 95% CI −3, −1), lower total physical activity (−2.9 mg; 95% CI −4.3, −1.6) and less time in moderate‐to‐vigorous physical activity (−6.6 min; 95% CI −11.4, −1.7), alongside greater sleep variability (17.9 min; 95% CI 8.2, 27.7) and longer sedentary time (31.9 min; 95% CI 10.5, 53.2). Movement intensity during the most active continuous 10 and 30 min of the day was also lower in the OB cluster.ConclusionsIn individuals living with T2DM, the OB subtype had the lowest levels of physical activity and least favourable sleep profiles. Such behaviours may be suitable targets for personalized therapeutic lifestyle interventions.

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“…Mitochondrial Rho proteins MIRO regulate mitochondrial trafficking and local energy turnover in neurons, affecting the neurodegeneration process [72]. These Rho proteins regulate hormone secretion in PAs [45] and maintain glucose homeostasis [73], which is altered in both acromegaly and Cushing's individuals [74]. MAPK signalling cascade regulates biological processes such as stress, proliferation, apoptosis and immune defence [44].…”

Section: Discussionmentioning

confidence: 99%

Evaluation of autoantibody signatures in pituitary adenoma patients using human proteome arrays

Banerjee

Ray

Barpanda

et al. 2022

Proteomics Clinical Apps

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Purpose To identify the specific diagnostic biomarkers related to pituitary adenomas (PAs), we performed serological antibody profiles for three types of PAs, namely Acromegaly, Cushing's and Nonfunctional Pituitary Adenomas (NFPAs), using the human proteome (HuProt) microarray. This is the first study describing the serum autoantibody profile of PAs. Experimental Design We performed serological autoantibody profiling of four healthy controls, four Acromegaly, three Cushing's and three NFPAs patient samples to obtain their autoantibody profiles, which were used for studying expression, interaction and altered biological pathways. Further, significant autoantibodies of PAs were compared with data available for glioma, meningioma and AAgAtlas for their specificity. Results Autoantibody profile of PAs led to the identification of differentially expressed significant proteins such as AKNAD1 (AT‐Hook Transcription Factor [AKNA] Domain Containing 1), NINJ1 (Nerve injury‐induced protein 1), L3HYPDH (Trans‐3‐hydroxy‐L‐proline dehydratase), RHOG (Rho‐related GTP‐binding protein) and PTP4A1 (Protein Tyrosine Phosphatase Type IVA 1) in Acromegaly. Protein ABR (Active breakpoint cluster region‐related protein), ST6GALNAC6 (ST6 N‐acetylgalactosaminide alpha‐2, 6‐sialyltransferase 6), NOL3 (Nucleolar protein 3), ANXA8 (Annexin A8) and POLR2H (RNA polymerase II, I and III subunit H) showed an antigenic response in Cushing's patient's serum samples. Protein dipeptidyl peptidase 3 (DPP3) and reticulon‐4 (RTN4) exhibited a very high antigenic response in NFPA patients. These proteins hold promise as potential autoantibody biomarkers in PAs.

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The Interaction of Insulin and Pituitary Hormone Syndromes

Cited by 18 publications

References 75 publications

Approach of Acromegaly during Pregnancy

Approach of Acromegaly during Pregnancy

Twenty‐four‐hour physical behaviour profiles across type 2 diabetes mellitus subtypes

Evaluation of autoantibody signatures in pituitary adenoma patients using human proteome arrays

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