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> Disorders > Disease or Syndrome > Hypopituitarism

Hypopituitarism

Hypopituitarism is a condition characterized by partial or complete deficiency of one or more pituitary hormones.
This can lead to a variety of clinical manifestations, including growth retardation, sexual dysfunction, and metabolic disturbances.
Hypopituitarism can be caused by a variety of factors, including tumors, radiation therapy, head trauma, and autoimmune disorders.
Diagnosis typically involves laboratory testing to assess pituitary hormone levels.
Treatment may involve hormone replacement therapy to restore normal hormone balance.
Researchers can leverage AI-driven tools like PubCompare.ai to optimize Hypopituitarism research by easily identifying the best protocols and products from the literature, preprints, and patents, improving reproducibility and unlocking new insights.

Most cited protocols related to «Hypopituitarism»

1
Fracture Risk Assessment Using FRAX
Individual patient details comprise age (50 to 90 years), sex, weight (in kg) and height (in cm). BMI is automatically computed from height and weight. Dichotomised risk variables are then entered:

A prior fragility fracture (yes/no)

Parental history of hip fracture (yes/no)

Current tobacco smoking (yes/no)

Ever long-term use of oral glucocorticoids (yes/no)

Rheumatoid arthritis (yes/no)

Other causes of secondary osteoporosis (yes/no)

Daily alcohol consumption of three or more units daily (yes/no)

A distinction is made between rheumatoid arthritis and other secondary causes of osteoporosis. Rheumatoid arthritis carries a fracture risk over and above that provided by BMD [11 (link)]. Whereas this may hold true for other secondary causes of osteoporosis, the evidence base is weak. Of the many secondary causes of osteoporosis, the following have been consistently documented to be associated with a significant increase in fracture risk:

Untreated hypogonadism in men and women, e.g., bilateral oophorectomy or orchidectomy, anorexia nervosa, chemotherapy for breast cancer, hypopituitarism [33 (link)–40 (link)]

Inflammatory bowel disease, e.g., Crohn’s disease and ulcerative colitis [41 (link)–43 (link)]. It should be noted that the risk is in part dependent on the use of glucocorticoids, but an independent risk remains after adjustment for glucocorticoid exposure [44 (link)].

Prolonged immobility, e.g., spinal cord injury, Parkinson’s disease, stroke, muscular dystrophy, ankylosing spondylitis [45 (link)–50 (link)]

Organ transplantation [51 –54 (link)]

Type I diabetes [55 (link)–58 (link)]

Thyroid disorders, e.g., untreated hyperthyroidism, over-treated hypothyroidism [59 (link)–63 (link)]

Whereas there is strong evidence for the association of these disorders and fracture risk, the independence of these risk factors from BMD is uncertain. It was conservatively assumed, therefore, that the fracture risk was mediated via low BMD, but with a risk ratio similar to that noted in rheumatoid arthritis. From an operational view, where the field for rheumatoid arthritis is entered as ‘yes’, a risk is computed with and without BMD. If the field for other secondary osteoporosis is also filled as ‘yes’ this does not contribute to the calculation of fracture probability. Conversely, where the field for rheumatoid arthritis entered as ‘no’, and the field for secondary osteoporosis is ‘yes’, the same β coefficients as used for rheumatoid arthritis contribute to the computation of probability where BMD is not entered. In the presence of BMD, however, no additional risk is assumed in the presence of secondary osteoporosis, since its independence of BMD is uncertain.
If any of the fields for dichotomous variables is not completed, a negative response is assumed. Fractures probability can then be calculated. The output (without BMD) comprises the 10-year probability of hip, clinical spine, shoulder or wrist fracture and the 10-year probability of hip fracture (Fig. 1).

Input and output for the FRAX™ model

Femoral neck BMD can additionally be entered either as a Z-score or a T-score. The transformation of Z- to T-score and vice versa is derived for the NHANES III database for female Caucasians aged 20–29 years [64 (link)]. When entered, calculations give the 10-year probabilities as defined above with or without the inclusion of BMD.
Kanis J.A., Johnell O., Oden A., Johansson H, & McCloskey E. (2008). FRAX™ and the assessment of fracture probability in men and women from the UK. Osteoporosis International, 19(4), 385-397.
Publication 2008
Ankylosing Spondylitis Anorexia Nervosa Caucasoid Races Cerebrovascular Accident Crohn Disease Debility Diabetes Mellitus, Insulin-Dependent Female Castrations Fracture, Bone Fracture, Wrist Glucocorticoids Hip Fractures Hyperthyroidism Hypogonadism Hypopituitarism Hypothyroidism Inflammatory Bowel Diseases Malignant Neoplasm of Breast Muscular Dystrophy Neck Orchiectomy Organ Transplantation Osteoporosis Parent Patients Pharmacotherapy Rheumatoid Arthritis Shoulder Spinal Cord Injuries Thyroid Diseases Ulcerative Colitis Vertebral Column Woman
2
Polycystic Ovary Syndrome Phenotypes
This retrospective case-control study was conducted at the Clinical Division of Gynecologic Endocrinology and Reproductive Medicine of the Medical University of Vienna, Austria. From January 2012 to April 2021. Data were included from 58 patients with FHA having PCOM, defined as follows: secondary amenorrhea for at least six months and a negative progestogen challenge test with context of weight loss, insufficient caloric intake, intense physical activity or notion of recent psychological stress, confirmed by a psychologic report. Pregnancy, hypothyroidism, and hyperprolactinemia and any organ-related pituitary dysfunction had to be excluded. The control group consisted of 58 PCOS phenotype D patients diagnosed based on the Rotterdam criteria (4 (link)), who had responded well to a progesterone challenge test, and were matched 1:1 by age and BMI for all further analyses in this study. PCOS-D is one of four different phenotypes of PCOS and is also known as “non-hyperandrogenic” PCOS. It is characterized by oligo-/anovulation and PCOM (3 (link)). Since the definition of Androgen Excess Society would require hyperandrogenism as a mandatory criterion for PCOS diagnosis (5 (link)), the Rotterdam criteria were chosen. This classification has been recently re-visited and validated (3 (link)). In all patients, an Aloka Prosound 6 ultrasound machine (Wiener Neudorf, Austria; frequency range 3.0 – 7.5 MHz) was used. PCOM was defined by a follicle number per ovary (FNPO) >12 and/or an ovarian volume ≥10 cm3 and/or an ovarian area ≥5.5 cm2, according to the recommendations of an international expert panel for ultrasound machine with frequency range less than 8 MHz (7 (link)).
The study protocol complies with the declaration of Helsinki and was approved by the Institutional Review Board of the Medical University of Vienna (institutional review board number 1722/2021). Neither written nor verbal informed consent was necessary in retrospective studies according to the Ethics Committee of the Medical University of Vienna.
Beitl K., Dewailly D., Seemann R., Hager M., Bünker J., Mayrhofer D., Holzer I, & Ott J. (2022). Polycystic Ovary Syndrome Phenotype D Versus Functional Hypothalamic Amenorrhea With Polycystic Ovarian Morphology: A Retrospective Study About a Frequent Differential Diagnosis. Frontiers in Endocrinology, 13, 904706.
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Publication 2022
Androgens Anovulation Diagnosis Ethics Committees Ethics Committees, Research Hyperandrogenism Hyperprolactinemia Hypopituitarism Hypothyroidism Oligonucleotides Ovarian Follicle Ovary Patients Phenotype Polycystic Ovary Syndrome Pregnancy Progesterone Progestins Stress, Psychological System, Endocrine Ultrasonics
3
Chinese Residents Obesity and Exclusion
Han Chinese residents (25–55 years) from Taiyuan (Shanxi Province, China) were recruited to the trial if their BMI ≥ 28 kg m−2, waistline ≥ 80 cm (for female) or 90 cm (for male), and waist–hip ratio ≥ 0.85 (for female) or 0.90 (for male). Subjects were excluded with alcoholism, history, or presence of gastrointestinal pathologies, chronic pathologies such as diabetes (including type 1 and 2 diabetes), nephropathies, or liver cirrhosis, gastrointestinal surgery, history of administration of antibiotics lasting more than 3 days in the previous 3 months, psychiatric disorders, pituitary dysfunction, cancers, infectious diseases, deformity, anemia, or losing weight by surgery or drug in the past 3 months. The study was approved by the Ethics Committee of Chinese Clinical Trial Registry (No. ChiECRCT-000011), and written informed consent was obtained from each participant before their admission to the protocol.
Xiao S., Fei N., Pang X., Shen J., Wang L., Zhang B., Zhang M., Zhang X., Zhang C., Li M., Sun L., Xue Z., Wang J., Feng J., Yan F., Zhao N., Liu J., Long W, & Zhao L. (2013). A gut microbiota-targeted dietary intervention for amelioration of chronic inflammation underlying metabolic syndrome. Fems Microbiology Ecology, 87(2), 357-367.
Publication 2013
Alcoholic Intoxication, Chronic Anemia Antibiotics Chinese Communicable Diseases Congenital Abnormality Diabetes Mellitus Ethics Committees, Clinical Gastrointestinal Surgical Procedure Hypopituitarism Kidney Diseases Liver Cirrhosis Males Malignant Neoplasms Mental Disorders Operative Surgical Procedures Pharmaceutical Preparations Waist-Hip Ratio Woman
4
Management of Pituitary Incidentalomas
The options for treatment of patients with asymptomatic, clinically
nonfunctioning pituitary incidentalomas are conservative follow-up without
surgery (Fig. 1) or immediate surgery
despite the lack of indications for this. Conservative follow-up was
recommended with the recognition that the proper algorithm for this and its
appropriateness and safety have not been tested prospectively. Few data are
available for or against a nonsurgical approach to management of
asymptomatic pituitary incidentalomas. Therefore, evidence in support of
these guidelines also relied on the clinical experiences of the Task Force
members.
The proper algorithm for endocrine testing during this follow-up has not been
tested as prospectively conducted endocrine testing of patients with
pituitary incidentalomas who were followed without surgery has only been
reported in 49 patients (8 (link), 9 (link)). Follow-up endocrine testing is
recommended for patients with macroincidentalomas because they are at risk
of developing hypopituitarism. Of the macroincidentalomas followed
prospectively, worsening hypopituitarism developed in one of seven (8 (link)) and three of 28 (25 (link)) patients, all of whom had
enlargement of their tumors. Hypopituitarism also developed in four of 37
(5 (link)) and one of 248 (6 (link)) patients who developed apoplexy on
follow-up. Follow-up endocrine testing was recommended despite the paucity
of data because of the potential high risk to the patient of untreated
hypopituitarism. In a meta-analysis of incidentaloma studies, new endocrine
dysfunction developed overall in 2.4% of patients per year (3 ). It is unclear how often new
hypopituitarism develops in the absence of tumor growth. Rapid growth may
increase the risk of new hypopituitarism. Routine follow-up endocrine
testing is not required for microincidentalomas whose clinical picture does
not change because the risk of developing new hypopituitarism is extremely
low. In previous studies, none of the pituitary microincidentalomas followed
prospectively was reported to be associated with changes in pituitary
function (4 (link)– (link)9 (link)).
The proposed algorithms for the MRI follow-up of pituitary incidentalomas
took into account those adopted in prior studies. However, those algorithms
varied considerably from study to study (4 (link)– (link)9 (link)), and none was validated. As a result,
the imaging follow-up proposed in this guideline incorporated the
experiences of the Task Force's members. Follow-up MRI scans were
recommended for macroincidentalomas because it has been demonstrated that
although generally these lesions grow slowly, some do enlarge and become
symptomatic. In data combined from a number of studies, macroincidentalomas
enlarged in 85 of 353 (24%) patients (4 (link)– (link)9 (link), 23 (link)– (link)26 (link)). VF abnormalities developed in 28
(8%) patients over time, which demonstrated that the enlargement adversely
affected the patient's health. Pituitary apoplexy developed in seven of 353
(2%) patients, of whom most developed permanent hypopituitarism and one had
permanent vision impairment (5 (link)). In a
meta-analysis of these studies, 8.2% of incidentalomas enlarged per year
with a follow-up of 472 person-years (3 ). Less frequent surveillance of microincidentalomas was
recommended because their rate of enlargement was low, being reported in 17
of 160 patients (10.6%) followed from 2.3 to 7 yr (5 (link)– (link)9 (link), 23 (link)– (link)25 (link)). In the meta-analysis, 1.7% of microincidentalomas
enlarged per year (3 ). Importantly,
none of the patients with these microincidentalomas developed new VF
abnormalities that would have necessitated surgery.
Overall, the Task Force considered that repeat scanning within the first year
was warranted for all patients because, although most incidentalomas grow
slowly, some do enlarge, and the true proliferative nature of the
incidentaloma is unknown (Fig. 1). If
no growth is detected, then the interval between MRI scans can be increased.
Evidence does not support one particular algorithm for the frequency of
follow-up imaging, but we recommend repeating the MRI every year in
macroincidentalomas, every 1–2 yr in microincidentalomas for the next 3 yr,
and then every other year for the next 6 yr and gradually less frequently
indefinitely so long as the lesion continues not to threaten the patient's
health. Some Task Force members would continue imaging every 5 yr.
Uncertainty as to the optimal interval and duration of long-term follow-up
imaging can be shared with the patient, and the scheme followed can be
individualized to balance the physician's assessment of the risk that the
lesion poses to the patient's health with the burden to the patient of
surveillance testing.
Freda P.U., Beckers A.M., Katznelson L., Molitch M.E., Montori V.M., Post K.D, & Vance M.L. (2011). Pituitary Incidentaloma: An Endocrine Society Clinical Practice Guideline. The Journal of Clinical Endocrinology and Metabolism, 96(4), 894-904.
Publication 2011
Cerebrovascular Accident Congenital Abnormality Health Risk Assessment Hypertrophy Hypopituitarism MRI Scans Neoplasms Operative Surgical Procedures Patients Pituitary Apoplexy Safety System, Endocrine
5
Nationwide Cohort Study of TBI-Induced Endocrine Disorders
The flow diagram of this nationwide-based study is shown in Fig. 1. This study included patients who suffered from TBI (ICD9:800–804, 850–854) during 1996–2009. All medical records of the TBI cohort were extracted and analyzed, and all enrolled study subjects were followed up until death or the end of 2009. Endocrine disorders were identified using the following ICD codes: 244 (acquired hypothyroidism), 253 (pituitary dysfunction), 255 (adrenal gland disorders), 258 (polyglandular dysfunction), and 259 (other endocrine disorders) with at least three records of outpatient visits within 1 year or one admission diagnosis during the study period.
We excluded subjects with endocrine dysfunction, stroke (ICD9:430–438), or brain tumor (ICD9: 191, 225.0, 225.1, 225.2) diagnosed before the TBI event. Subjects with data errors or missing data were also excluded. The TBI subjects and non-TBI subjects were frequency matched randomly by age, sex, income, urbanization, diabetes, and hypertension at a ratio of 1:4 (TBI subjects vs. non-TBI user). Overall, 156,945 insured subjects (31,389 matched sets) were included in the final analysis (Fig. 1).
Yang W.H., Chen P.C., Wang T.C., Kuo T.Y., Cheng C.Y, & Yang Y.H. (2016). Endocrine dysfunction following traumatic brain injury: a 5-year follow-up nationwide-based study. Scientific Reports, 6, 32987.
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Publication 2016
Adrenal Gland Diseases Brain Neoplasms Cerebrovascular Accident Diabetes Mellitus Endocrine System Diseases High Blood Pressures Hypopituitarism Outpatients Patients Primary Hypothyroidism System, Endocrine Tests, Diagnostic Urbanization

Most recents protocols related to «Hypopituitarism»

1
Thyroid Function in Type 2 Diabetes Patients
This cross-hospital study included 422 T2DM patients admitted to the Endocrinology Department of Cangzhou Central Hospital between July 2017 and December 2019. The inclusion criteria were as follows: aged ≥ 18 years old with normal thyroid function and negative autoantibodies, including thyroid peroxidase antibody (TPOAb), thyroglobulin antibody, or thyrotropin receptor antibody, and diagnosed with T2DM according to the criteria of the American Diabetes Association.21 (link) The exclusion criteria were as follows: those who had any acute complications of diabetes; a history of thyroid disease; any other endocrine disorder such as Addison’s disease, Cushing syndrome, pituitary adenoma, or hypopituitarism; retinopathy unrelated to diabetes; a severe infection; an inflammatory disease; malignant tumors; liver or renal dysfunction; and thyroid-function-altering medications or drugs. This study was approved by the Cangzhou Central Hospital Ethics Committee and was performed following the Declaration of Helsinki guidelines, including any relevant details. Due to the retrospective nature of the study, the requirement for informed consent was waived.
Yang J., Ding W., Wang H, & Shi Y. (2023). Association Between Sensitivity to Thyroid Hormone Indices and Diabetic Retinopathy in Euthyroid Patients with Type 2 Diabetes Mellitus. Diabetes, Metabolic Syndrome and Obesity, 16, 535-545.
Publication 2023
Addison Disease anti-thyroglobulin antibody Autoantibodies Complications of Diabetes Mellitus Cushing Syndrome Diabetes Mellitus Endocrine System Diseases Ethics Committees, Clinical Hypopituitarism Infection Inflammation Kidney Failure Liver Malignant Neoplasms Patients Pharmaceutical Preparations Pituitary Adenoma Retinal Diseases System, Endocrine Thyroid-Stimulating Immunoglobulins Thyroid Diseases Thyroid Gland thyroid microsomal antibodies
2
Endoscopic Surgery Impacts Pituitary Function
This meta-analysis was performed by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Two independent investigators (Nie and Fang) conducted literature (from inception to May 15, 2022), using the following databases: PubMed, Cochrane, and Ovid MEDLINE. Search strategy based on keywords is as follows: “pituitary adenoma,” “growth hormone pituitary adenoma,” “growth hormone-producing pituitary adenoma,” “somatotroph tumor,” “acromegaly,” “endoscopic surgery,” “endoscopic transsphenoidal surgery,” “anterior pituitary function,” “pituitary insufficiency,” “hypothyroidism,” “thyroid insufficiency,” “adrenal insufficiency,” “hypoadrenalism,” and “endoscopic.”
Two researchers (Nie and Fang) independently conducted literature screening, data extraction, and quality evaluation. If there is any disagreement, another researcher will help mediate it (Zhang). The inclusion criteria were English original research articles. Case reports, conference abstracts, meta-analyses, and reviews were excluded. This article should describe the pituitary hormone before and after endoscopic transsphenoidal pituitary tumor surgery in patients with somatotroph tumors. References to all selected articles are reviewed.
Nie D., Fang Q., Wong W., Gui S., Zhao P., Li C, & Zhang Y. (2023). The effect of endoscopic transsphenoidal somatotroph tumors resection on pituitary hormones: systematic review and meta-analysis. World Journal of Surgical Oncology, 21, 71.
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Publication 2023
Acromegaly Conferences Endoscopy Growth Hormone Growth Hormone-Secreting Pituitary Adenoma Hypoaldosteronism Hypofunction, Adrenal Gland Hypopituitarism Hypothyroidism Neoplasms Patients Pituitary Adenoma Pituitary Hormones Pituitary Hormones, Anterior Pituitary Neoplasms Somatotrophs Surgical Endoscopy Thyroid Gland
3
Comprehensive Cancer Care Analyses in VA
The VA CDW aggregates data from the universal electronic medical record and includes discrete data on patient demographics, procedures, diagnoses, clinical encounters, medications, vital signs and vital status. The oncology database within CDW is derived from the VA Cancer Registry System and includes data for patients diagnosed with and/or treated for cancer in the VA healthcare system along with information on their tumor and other characteristics. Therefore, we used this specific database to obtain tumor stage, histology, diagnosis date, smoking history, and demographic data (age, race, marital status, geographic region) at the time of diagnosis. The CDW pharmacy data were accessed to capture utilization of CT and IO agents in the 2L setting. The start of 2L therapy was defined as administration of a new drug on or after 21 days of initial therapy or after 21 days of no treatment. HCRU was defined as the number of outpatient visits and hospitalizations, and length of stay among those hospitalized. HCRU was evaluated from the start of 2L therapy to the start of subsequent line of therapy or 90 days after the end date of 2L therapy. The CDW vital status data allowed us to characterize OS as the time from the start of 2L therapy to death from any cause. Patients surviving until the end of the study period were censored and their follow-up time evaluated from the date of diagnosis until last HCRU. AEs were defined on the basis of the presence of International Classification of Diseases-9/10 (ICD-9/10) codes for common IO and CT-related AEs documented as primary or secondary diagnoses on inpatient and outpatient encounters between the initiation of 2L therapy and start of subsequent line of therapy, if received, or 90 days after last treatment ended. Common AEs of interest were rash/inflammatory dermatitis, colitis/enterocolitis, hepatitis, hypophysitis/hypopituitarism, hypothyroidism, pneumonitis, dyspnea, anemia, atypical pneumonia, fatigue, nausea/vomiting, neuropathy, and fever with neutropenia. Inpatient and outpatient encounters were also used to compute each patient’s Charlson Comorbidity Index (CCI), which captured diagnoses within 1 year of the lung cancer diagnosis.
Williams C.D., Allo M.A., Gu L., Vashistha V., Press A, & Kelley M. (2023). Health outcomes and healthcare resource utilization among Veterans with stage IV non-small cell lung cancer treated with second-line chemotherapy versus immunotherapy. PLOS ONE, 18(2), e0282020.
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Publication 2023
Anemia Colitis Dermatitis Diagnosis Dyspnea Enterocolitis Exanthema Fatigue Febrile Neutropenia Hepatitis Hospitalization Hypophysitis Hypopituitarism Hypothyroidism Inflammation Inpatient Lung Cancer Malignant Neoplasms Nausea Neoplasms Outpatients Patients Pharmaceutical Preparations Pneumonia Pneumonitis Signs, Vital
4
Long-term Dopamine Agonist Dependence in Prolactinoma
Data were analyzed using IBM SPSS statistical software Version 24.0 (IBM Corp., New York, NY, USA) and GraphPad Prism (V7.04 software, San Diego, CA, USA). Continuous variables were examined for homogeneity of variance and are expressed as mean ± SD unless otherwise noted. Serum PRL levels are presented as median values and interquartile range (IQR, 25th to 75th percentile). Categorical variables are given as numbers and percentages. For comparisons of means between groups (i.e., patients with AO and EO), Student's t-test was used for normally distributed data, and the Mann–Whitney test for nonparametric data. The Wilcoxon signed-rank test was used to evaluate paired differences in PRL and BMI levels before and after treatment. Categorical variables were compared using Pearson's chi-square test or Fisher's exact test, as appropriate. The Spearman rank-order correlation coefficient was calculated to check for the strength of association between different variables (i.e., PRL, age, patients' BMI, DA dependency). We assessed the proportion of patients with long-term dependence on DAs and performed time-dependent multivariable regression analysis to calculate hazard ratios (HR) for potential risk factors. The variables tested were: age at diagnosis, initial PRL levels, BMI (kg/m2), hypopituitarism at diagnosis, baseline gonadotropin deficiency, prevalence of headache at diagnosis, adenoma size, and cavernous sinus invasion. The multivariable regression analysis included all dependent risk factors in the univariable regression with a p value ≤ 0.05. Baseline PRL values were log transformed before being imputed in the regression and correlation analysis analysis, as data showed a positively skewed distribution. Significance level was set at p ≤ 5%.
Andereggen L., Tortora A., Schubert G.A., Musahl C., Frey J., Luedi M.M., Mariani L., Beck J, & Christ E. (2023). Prolactinomas in adolescent and elderly patients—A comparative long-term analysis. Frontiers in Surgery, 10, 967407.
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Publication 2023
Adenoma Aftercare Diagnosis Gonadotropins Headache Hypopituitarism Patients prisma Serum Sinus, Cavernous
5
Diagnostic Criteria for Prolactin-Secreting Pituitary Adenomas
Diagnosis was based on clinical and biochemical assessment, including a standard protocol for pituitary magnetic resonance imaging (MRI; see below). All patients fulfilled the diagnostic criteria of a prolactin (PRL)-secreting pituitary adenoma [i.e., elevated PRL levels without evidence of pituitary stalk compression, primary hypothyroidism or drug-induced hyperprolactinaemia, and positive pituitary magnetic resonance imaging (MRI) scan] (12 (link), 13 (link)). Prolactin (PRL) levels, including the immunoradiometric PRL assay with serum dilution in order to overcome the high-dose PRL hook effect (14 (link)), were measured. The upper limits of PRL levels for diagnosis were set at 20 µg/L(15 (link)). Partial hypopituitarism was defined as impaired secretion of one or more pituitary hormones. Secondary adrenal insufficiency was characterized by the presence of low serum cortisol (<50 nmol/L) levels, or normal cortisol but inadequate responses to the adrenocorticotropin (ACTH) stimulation test or insulin tolerance test. The diagnosis of secondary hypothyroidism was made based on a finding of low-normal thyroid-stimulating hormone (TSH) levels and a low free thyroxin (FT4) level. A gonadotropin deficiency or central hypogonadism was considered in the case of low-normal levels of gonadotropins in parallel with low estradiol/testosterone levels.
Andereggen L., Tortora A., Schubert G.A., Musahl C., Frey J., Luedi M.M., Mariani L., Beck J, & Christ E. (2023). Prolactinomas in adolescent and elderly patients—A comparative long-term analysis. Frontiers in Surgery, 10, 967407.
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Publication 2023
Corticotropin Diagnosis Estradiol Gonadotropins Hydrocortisone Hyperprolactinemia Hypofunction, Adrenal Gland Hypogonadism, Hypogonadotropic Hypopituitarism Hypothyroidism, Central Immune Tolerance Immunoradiometric Assays Insulin Magnetic Resonance Imaging Patients Pharmaceutical Preparations Pituitary Hormones Pituitary Stalk Primary Hypothyroidism Prolactin Prolactinoma secretion Serum Technique, Dilution Testosterone Thyrotropin Thyroxine

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1
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2
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More about "Hypopituitarism"

Hypopituitarism, also known as pituitary hypofunction or pituitary insufficiency, is a medical condition characterized by a partial or complete deficiency of one or more pituitary hormones.
This endocrine disorder can lead to a variety of clinical manifestations, including growth retardation, sexual dysfunction, and metabolic disturbances.
The pituitary gland, often referred to as the 'master gland,' plays a crucial role in regulating various bodily functions by producing and secreting essential hormones.
In hypopituitarism, this gland's ability to produce and release these hormones is impaired, resulting in a hormone imbalance.
The causes of hypopituitarism can be diverse, ranging from tumors (such as pituitary adenomas) and radiation therapy to head trauma and autoimmune disorders.
Diagnosis typically involves laboratory testing to assess pituitary hormone levels, which may be measured using advanced equipment like the ADVIA Centaur XP or analyzed using techniques like the Human Genome U133 Plus 2.0 Array.
Treatment for hypopituitarism often involves hormone replacement therapy, where synthetic or bio-identical hormones are administered to restore the normal hormonal balance.
Researchers can leverage powerful tools like SigmaPlot version 11.0, the Nextera DNA Library Preparation Kit, and SPSS statistical software to optimize their research on hypopituitarism, improving reproducibility and unlocking new insights.
For example, the Peak Scanner tool can be used to analyze and visualize pituitary hormone levels, while SPSS Statistics version 28 and SPSS Statistics can be employed for comprehensive statistical analysis of research data.
The HiSeq 2000 sequencing system can also be utilized to investigate the underlying genetic and molecular mechanisms of hypopituitarism.
By combining the power of advanced AI-driven tools like PubCompare.ai with these state-of-the-art research technologies, scientists can streamline their work, identify the most effective protocols and products, and accelerate the development of improved diagnostic and treatment options for individuals living with hypopituitarism.