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Adiponectin

Adiponectin is a protein hormone secreted by adipose tissue that plays a crucial role in regulating glucose and lipid metabolism.
It has been linked to various health conditions, including diabetes, obesity, and cardiovascular disease.
PubCompare.ai's AI-driven platform offers efficient research protocols to optimizie Adiponectin studies, helping researchers locate the best protocols from literature, pre-prints, and patents using intelligent comparisons.
Thier powerful tools can enhance research outcomes and advance the understanding of Adiponectin's physiological and pathological functions.

Most cited protocols related to «Adiponectin»

1
Cardiometabolic Traits and BF% Associations
Cited 38 times
Cardiometabolic consortia. To explore the relationship between
BF% and an array of cardiometabolic traits and diseases, the
association results for the 12 GWS-index SNPs were requested from seven primary
cardiometabolic genetic consortia: the LEPgen consortium (circulating leptin,
Kilpeläinen et al., in preparation), VATGen consortium27 (link), GIANT (BMI, height and WHRadjBMI)19 (link)20 (link)26 (link), GLGC (HDL-C, LDL-C, TG, TC)28 (link),
MAGIC29 (link), DIAGRAM (T2D)30 (link) and CARDIoGRAMplusC4D
(CAD)31 (link). On the basis of known correlations among these
cardiometabolic traits, we considered circulating leptin levels, abdominal
adipose tissue storage, height, WHRadjBMI, plasma lipid levels,
plasma glycemic traits, T2D and CAD as eight independent trait groups. In
addition, the associations for these 12 SNPs were also looked up in four
consortia that examined phenotypes more distantly related to BF%:
ADIPOGen (BMI-adjusted adiponectin)64 (link), ReproGen (age at
menarche)24 (link), liver enzyme meta-analysis65 (link) and
CRP meta-analysis38 (link). For certain GWAS-index SNPs, we also did
specific lookups: rs6857 association in liver fat storage, rs3761445
associations in cutaneous nevi and melanoma risk meta-analysis42 (link)43 (link)44 (link), early growth genetics (birth weight32 (link)
and pubertal height33 (link)), insulin-like growth factor 1
meta-analysis (Teumer et al. under review) and CHARGE testosterone
meta-analysis66 (link), and rs9906944 associations in tooth
development meta-analysis35 (link) and Early Growth Genetics Consortium
(birth weight32 (link) and pubertal height33 (link)).
NHGRI GWAS catalogue lookups. We manually curated and searched the
National Human Genome Research Institute (NHGRI) GWAS Catalogue ( www.genome.gov/gwastudies)
for previously reported associations for SNPs within 500 kb and
r2>0.7 (1000 Genomes Pilot1 EUR
population based on SNAP: http://www.broadinstitute.org/mpg/snap/ldsearch.php) with each of
the 12 GWS-index SNPs. All previously reported associations that reached
P<5 × 10−8 were retained
(Supplementary Table 11).
Lu Y., Day F.R., Gustafsson S., Buchkovich M.L., Na J., Bataille V., Cousminer D.L., Dastani Z., Drong A.W., Esko T., Evans D.M., Falchi M., Feitosa M.F., Ferreira T., Hedman Å.K., Haring R., Hysi P.G., Iles M.M., Justice A.E., Kanoni S., Lagou V., Li R., Li X., Locke A., Lu C., Mägi R., Perry J.R., Pers T.H., Qi Q., Sanna M., Schmidt E.M., Scott W.R., Shungin D., Teumer A., Vinkhuyzen A.A., Walker R.W., Westra H.J., Zhang M., Zhang W., Zhao J.H., Zhu Z., Afzal U., Ahluwalia T.S., Bakker S.J., Bellis C., Bonnefond A., Borodulin K., Buchman A.S., Cederholm T., Choh A.C., Choi H.J., Curran J.E., de Groot L.C., De Jager P.L., Dhonukshe-Rutten R.A., Enneman A.W., Eury E., Evans D.S., Forsen T., Friedrich N., Fumeron F., Garcia M.E., Gärtner S., Han B.G., Havulinna A.S., Hayward C., Hernandez D., Hillege H., Ittermann T., Kent J.W., Kolcic I., Laatikainen T., Lahti J., Leach I.M., Lee C.G., Lee J.Y., Liu T., Liu Y., Lobbens S., Loh M., Lyytikäinen L.P., Medina-Gomez C., Michaëlsson K., Nalls M.A., Nielson C.M., Oozageer L., Pascoe L., Paternoster L., Polašek O., Ripatti S., Sarzynski M.A., Shin C.S., Narančić N.S., Spira D., Srikanth P., Steinhagen-Thiessen E., Sung Y.J., Swart K.M., Taittonen L., Tanaka T., Tikkanen E., van der Velde N., van Schoor N.M., Verweij N., Wright A.F., Yu L., Zmuda J.M., Eklund N., Forrester T., Grarup N., Jackson A.U., Kristiansson K., Kuulasmaa T., Kuusisto J., Lichtner P., Luan J., Mahajan A., Männistö S., Palmer C.D., Ried J.S., Scott R.A., Stancáková A., Wagner P.J., Demirkan A., Döring A., Gudnason V., Kiel D.P., Kühnel B., Mangino M., Mcknight B., Menni C., O'Connell J.R., Oostra B.A., Shuldiner A.R., Song K., Vandenput L., van Duijn C.M., Vollenweider P., White C.C., Boehnke M., Boettcher Y., Cooper R.S., Forouhi N.G., Gieger C., Grallert H., Hingorani A., Jørgensen T., Jousilahti P., Kivimaki M., Kumari M., Laakso M., Langenberg C., Linneberg A., Luke A., Mckenzie C.A., Palotie A., Pedersen O., Peters A., Strauch K., Tayo B.O., Wareham N.J., Bennett D.A., Bertram L., Blangero J., Blüher M., Bouchard C., Campbell H., Cho N.H., Cummings S.R., Czerwinski S.A., Demuth I., Eckardt R., Eriksson J.G., Ferrucci L., Franco O.H., Froguel P., Gansevoort R.T., Hansen T., Harris T.B., Hastie N., Heliövaara M., Hofman A., Jordan J.M., Jula A., Kähönen M., Kajantie E., Knekt P.B., Koskinen S., Kovacs P., Lehtimäki T., Lind L., Liu Y., Orwoll E.S., Osmond C., Perola M., Pérusse L., Raitakari O.T., Rankinen T., Rao D.C., Rice T.K., Rivadeneira F., Rudan I., Salomaa V., Sørensen T.I., Stumvoll M., Tönjes A., Towne B., Tranah G.J., Tremblay A., Uitterlinden A.G., van der Harst P., Vartiainen E., Viikari J.S., Vitart V., Vohl M.C., Völzke H., Walker M., Wallaschofski H., Wild S., Wilson J.F., Yengo L., Bishop D.T., Borecki I.B., Chambers J.C., Cupples L.A., Dehghan A., Deloukas P., Fatemifar G., Fox C., Furey T.S., Franke L., Han J., Hunter D.J., Karjalainen J., Karpe F., Kaplan R.C., Kooner J.S., McCarthy M.I., Murabito J.M., Morris A.P., Bishop J.A., North K.E., Ohlsson C., Ong K.K., Prokopenko I., Richards J.B., Schadt E.E., Spector T.D., Widén E., Willer C.J., Yang J., Ingelsson E., Mohlke K.L., Hirschhorn J.N., Pospisilik J.A., Zillikens M.C., Lindgren C., Kilpeläinen T.O, & Loos R.J. (2016). New loci for body fat percentage reveal link between adiposity and cardiometabolic disease risk. Nature Communications, 7, 10495.
Full text: Click here
Publication 2016
Adiponectin Childbirth Enzymes Fatty Liver Genome Genome, Human Genome-Wide Association Study Gigantism Leptin Lipids Liver Melanoma Nevus, Intradermal Phenotype Plasma Puberty Single Nucleotide Polymorphism Somatomedins Tissues
2
Maternal and Infant Health Cohort
Cited 22 times
START has been granted ethical approval locally from the Research Ethics Board, Hamilton Health Sciences/McMaster Health Sciences (REB#: 10-640) and in India, Institutional Ethics Review Board Reference #: 114/2010). In both countries, pregnant mothers are recruited during their antenatal visits (1st or 2nd trimester) to their primary care practitioner or obstetrician. The study is described by the study personnel to the pregnant mothers and consent for participation is obtained. Information concerning medical and pregnancy history, health status, health behaviors, and socioeconomic status is obtained by questionnaires. Anthropometric measurements (height, weight, skinfold thickness), blood pressure, urine sample, and a fasting blood sample for glucose, insulin, micronutrients (i.e. vitamin B12, RBC folate, plasma homocysteine, methylmalonic acid MMA), lipids and a buffy coat for future DNA extraction will be collected, and processed using a standardized protocol at 24-28 weeks of gestation. Mothers who are not known to have diabetes will undergo a 75 oral glucose tolerance test between 24-28 weeks gestation. The results of an ultrasound performed between 18-24 weeks to assess for congenital anomalies and for precise determination of gestational age will be collected from each pregnant mother. At the time of delivery, details of the delivery, birth outcomes for the mother and baby will be collected, and a cord blood sample for DNA, glucose, insulin, lipids and additional aliquots for future analysis of adiponectin, and leptin will be taken from each baby. The placenta will be weighed, and where possible a biopsy of the placenta will be collected and stored in RNAlater for future analysis of RNA and methylation patterns. In addition, the infant’s anthropometry including birth weight, triceps and sub-scapular skin fold thickness, length, abdominal, head, and arm circumference will be measured by a trained research assistant.
Anand S.S., Vasudevan A., Gupta M., Morrison K., Kurpad A., Teo K.K, & Srinivasan K. (2013). Rationale and design of South Asian Birth Cohort (START): a Canada-India collaborative study. BMC Public Health, 13, 79.
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Publication 2013
Abdomen Adiponectin Biopsy Birth Birth Weight Blood Glucose Blood Pressure Cobalamins Congenital Abnormality Diabetes Mellitus DNA, A-Form Folate Gestational Age Glucose Head Homocysteine Infant Insulin Leptin Lipids Methylation Methylmalonic Acid Micronutrients Mothers Obstetric Delivery Obstetrician Oral Glucose Tolerance Test Placenta Plasma Pregnancy Primary Health Care Scapula Skinfold Thickness Ultrasonography Umbilical Cord Blood Urine
3
Avon Longitudinal Study of Parents and Children
Cited 17 times
The Avon Longitudinal Study of Parents and Children (ALSPAC) is a prospective population-based birth cohort study that recruited 14,541 pregnant women resident in Avon, UK with expected dates of delivery 1st April 1991 to 31st December 1992 (http://www.alspac.bris.ac.uk.).13 (link) There were 13,678 mother-offspring pairs from singleton live births who survived to at least one year of age; only singleton pregnancies are considered in this paper. We further restricted analyses in this paper to women with term deliveries (between 37-44weeks gestation): N = 12,447. Of these women 11,702 (94%) gave consent for abstraction of data from their obstetric records. 6,668 (57%) offspring of these 11,702 women attended the 9-year follow-up clinic. Of the 6,668 mother-offspring eligible pairs, complete data on GWG, offspring anthropometry, blood pressure and potential confounders were available for 5,154 (77% of attendees; 41% of 12,447 total). In addition, 3,457 (52% of attendees; 28% of total) had complete data on offspring blood assays.
Six trained research midwives abstracted data from obstetric medical records. There was no between-midwife variation in mean values of abstracted data and repeat data entry checks demonstrated error rates consistently < 1%. Obstetric data abstractions included every measurement of weight entered into the medical records and the corresponding gestational age and date. To allocate women to IOM categories (box 1) we used weight measurements from the obstetric notes and subtracted the first from the last weight measurement in pregnancy to derive absolute weight gain. Pre-pregnancy BMI was based on the predicted pre-pregnancy weight using multilevel models (see below) and maternal report of height.
Maternal age, parity, mode of delivery (caesarean section / vaginal delivery) and the child’s sex were obtained from the obstetric records. Based on questionnaire responses, the highest parental occupation was used to allocate the children to family social class groups (classes I (professional / managerial) to V (unskilled manual workers)). Maternal smoking in pregnancy, categorised as - never smoked; smoked before pregnancy or in the first trimester and then stopped; smoked throughout pregnancy – was obtained from questionnaire responses.
Offspring weight and height were measured in light clothing, without shoes. Weight was measured to the nearest 0.1kg using Tanita scales. Height was measured to the nearest 0.1cm using a Harpenden stadiometer. WC was measured to the nearest 1mm at the mid-point between the lower ribs and the pelvic bone with a flexible tape and with the child breathing normally. Fat mass was assessed using dual energy X-ray densitometry (DXA). We examined BMI, WC and fat mass as continuously measured variables. We also examined binary outcomes of overweight/obese (BMI) and abdominally obese (WC) using age- and sex-specific thresholds for both child BMI (International Obesity Task Force) 14 (link) and WC (>=90th percentile15 (link) based on WC percentile curves derived for British children16 (link)).
Blood pressure was measured using a Dinamap 9301 Vital Signs Monitor with the child rested and seated and their arm supported at chest level on a table. Two readings of systolic and diastolic blood pressure (SBP and DBP) were recorded and the mean of each was used. Non-fasting blood samples were taken using standard procedures with samples immediately spun and frozen at −80°C. The measurements were assayed in plasma in 2008 after a median of 7.5 years in storage with no previous freeze-thaw cycles during this period. Lipids (total cholesterol, triglycerides and HDL-C) were performed by modification of the standard Lipid Research Clinics Protocol using enzymatic reagents for lipid determinations. Apolipoprotein (apo) A1 and apoB were measured by immunoturbidimetric assays (Hitachi/Roche). Leptin was measured by an in house ELISA validated against commercial methods. Adiponectin and high sensitivity IL-6 were measured by ELISA (R&D systems) and CRP was measured by automated particle-enhanced immunoturbidimetric assay (Roche UK, Welwyn Garden City, UK). All assay coefficients of variation were <5%. Non-HDLc was calculated as total cholesterol minus HDLc.
All pregnancy weight measurements (median number of repeat measurements per woman: 10,range: 1, 17) were used to develop a linear spline multilevel model (with two levels: woman and measurement occasion) relating weight (outcome) to gestational age (exposure). Full details of this statistical modelling are provided in supplementary web-material. High levels of agreement were found between estimated and observed weights (Web-table1 and Web-figure2). We scaled maternal pre-pregnancy weight and gestational weight change to be clinically meaningful – examining the variation in offspring outcomes per additional 1kg of maternal weight at conception and per 400g gain per week of gestation for GWG.2 Sensitivity analyses were conducted in which we repeated analyses including only those women who had at least 9 measurements of gestational weight.
Associations of offspring outcomes with the IOM categories and with the estimates of maternal pre-pregnancy weight and early-, mid- and late-pregnancy GWG were undertaken using linear regression. We explored the linearity of the relationships between all outcomes and the exposures using fractional polynomials. Where there was evidence of non-linearity, we used spline models to approximate the relationship. In the basic model we adjusted for offspring gender and age at the time of outcome measurement and for all models with fat mass for height and height-squared. We considered the following potential confounders: pre-pregnancy weight and GWG in the previous period (for the multilevel model exposures only), gestational age (for IOM categories only, since this is taken account of in the multilevel models), maternal age, parity, pregnancy smoking, social class, and mode of delivery. In order to examine whether effects were mediated by birthweight we adjusted for it and for non-adiposity outcomes we also examined potential mediation by adiposity. Triglycerides, leptin, CRP and IL-6 were log transformed in order to normalize their distributions. The resultant regression coefficients were exponentiated to give a ratio of geometric means per change in exposure. Results are presented jointly for mothers of female and male offspring as associations were all very similar in both genders.
Fraser A., Tilling K., Macdonald-Wallis C., Sattar N., Brion M.J., Benfield L., Ness A., Deanfield J., Hingorani A., Nelson S.M., Smith G.D, & Lawlor D.A. (2010). Association of maternal weight gain in pregnancy with offspring obesity and metabolic and vascular traits in childhood. Circulation, 121(23), 2557-2564.
Publication 2010
Adiponectin APOB protein, human Apolipoprotein A-I Biological Assay Birth Cohort Birth Weight BLOOD Blood Pressure Cesarean Section Chest Child Cholesterol Conception Densitometry, X-Ray Diastole Enzyme-Linked Immunosorbent Assay Enzymes Females Freezing Gestational Age Hip Bone Hypersensitivity Hypoalphalipoproteinemia, Familial Immunoturbidimetric Assay Leptin Light Lipids Midwife Mothers Obesity Obstetric Delivery Parent Plasma Pregnancy Pregnant Women Ribs Signs, Vital Systolic Pressure Triglycerides Vagina Woman Workers
4
Genetics of Premature Coronary Artery Disease
Cited 14 times
The study complies with the Declaration of Helsinki and was approved by the Ethics Committee of the Instituto Nacional de Cardiología Ignacio Chávez (INCICH). All participants provided written informed consent. The study included 1162 patients with premature CAD and 873 healthy controls belonging to the Genetics of Atherosclerotic Disease (GEA) Mexican Study. Premature CAD was defined as history of myocardial infarction, angioplasty, revascularization surgery, or coronary stenosis > 50% on angiography, diagnosed before age of 55 in men and before age of 65 in women. Controls were apparently healthy asymptomatic individuals without family history of premature CAD, recruited from blood bank donors and through brochures posted in Social Service centers. Chest and abdomen computed tomographies were performed using a 64-channel multidetector helical computed tomography system (Somatom Sensation, Siemens) and interpreted by experienced radiologists. Scans were read to assess and quantify the following: (1) coronary artery calcification (CAC) score using the Agatston method [20 (link)] and (2) total adipose tissue (TAT) and subcutaneous and visceral adipose tissue areas (SAT and VAT) as described by Kvist et al. [21 (link)]. For the present study, the control group only included individuals with CAC = 0, who were nondiabetic, and with normal glucose levels (n = 873). In the whole sample, the demographic, clinical, anthropometric, and biochemical parameters and cardiovascular risk factors were evaluated and defined as previously described [22 –24 (link)]. Briefly, hypercholesterolemia was defined as total cholesterol (TC) levels ≥ 200 mg/dL. Hypertension was defined as systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg or the use of oral antihypertensive therapy. Type 2 diabetes mellitus (T2DM) was defined with a fasting glucose ≥ 126 mg/dL and was also considered when participants reported glucose-lowering treatment or a physician diagnosis of T2DM. Obesity was defined as body mass index (BMI) ≥ 30 kg/m2. Hypoalphalipoproteinemia, hypertriglyceridemia, and metabolic syndrome (MS) were defined using the criteria from the American Heart Association, National Heart, Lung, and Blood Institute Scientific Statement [25 (link)], except for central obesity that was considered when waist circumference was 90 cm in men and 80 cm in women [26 (link)]. Hyperuricemia was considered with a serum uric acid > 6.0 mg/dL and >7.0 mg/dL for women and men, respectively [27 (link)]. Insulin resistance was estimated using the homeostasis model assessment of insulin resistance (HOMA-IR). The presence of insulin resistance was considered when the HOMA-IR values were ≥75th percentile (3.66 in women and 3.38 in men). Hyperinsulinemia was defined when insulin concentration was ≥75th percentile (16.97 μIU/mL in women and 15.20 μIU/mL in men). Hypoadiponectinemia was defined when adiponectin concentration was ≤25th percentile (8.67 μg/mL in women and 5.30 μg/mL in men). Increased VAT was defined as VAT ≥ 75th percentile (122.0 cm2 in women and 151.5 cm2 in men) and increased SAT as SAT ≥ 75th percentile (335.5 cm2 in women and 221.7 cm2 in men). Elevated alanine aminotransferase (ALT) was defined as ALT activity ≥ 75th percentile (21.0 IU/L in women and 24.5 IU/L in men). Elevated aspartate aminotransferase (AST) was defined as AST activity ≥ 75th percentile (25 IU/L in women and 28 IU/L in men) and elevated gamma glutamyltransferase (GGT) was defined as GGT ≥ 75th percentile (21.0 IU/L in women and 27.5 IU/L in men). These cutoff points were obtained from a GEA study sample of 131 men and 185 women without obesity and with normal values of blood pressure, fasting glucose, and lipids.
All GEA participants are unrelated and of self-reported Mexican-Mestizo ancestry (three generations). In order to establish the ethnical characteristics of the studied groups, we analyzed 265 ancestry informative markers (AIMs). Using the ADMIXTURE software, the Caucasian, Amerindian, and African backgrounds were determined. Similar background in premature CAD patients and healthy controls was found (P > 0.05). Patients showed 55.8% of Amerindian ancestry, 34.3% of Caucasian ancestry, and 9.8% of African ancestry, whereas controls showed 54.0% of Amerindian ancestry, 35.8% of Caucasian ancestry, and 10.1% of African ancestry.
Posadas-Sánchez R., Pérez-Hernández N., Angeles-Martínez J., López-Bautista F., Villarreal-Molina T., Rodríguez-Pérez J.M., Fragoso J.M., Posadas-Romero C, & Vargas-Alarcón G. (2017). Interleukin 35 Polymorphisms Are Associated with Decreased Risk of Premature Coronary Artery Disease, Metabolic Parameters, and IL-35 Levels: The Genetics of Atherosclerotic Disease (GEA) Study. Mediators of Inflammation, 2017, 6012795.
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Publication 2017
Abdomen Adiponectin Angiography Angioplasty Antihypertensive Agents Artery, Coronary Aspartate Transaminase BLOOD Blood Pressure Calcinosis Chest Cholesterol Coronary Stenosis D-Alanine Transaminase Diabetes Mellitus, Non-Insulin-Dependent Diagnosis Donor, Blood Ethics Committees gamma-Glutamyl Transpeptidase Glucose Heart Hereditary Diseases High Blood Pressures Homeostasis Hypercholesterolemia Hyperinsulinism Hypertriglyceridemia Hyperuricemia Hypoadiponectinemia Hypoalphalipoproteinemias Index, Body Mass Insulin Insulin Resistance Lipids Lung Metabolic Syndrome X Multiple Endocrine Neoplasia Type 2b Myocardial Infarction Negroid Races Obesity Operative Surgical Procedures Patients Physicians Premature Birth Pressure, Diastolic Radiologist Radionuclide Imaging Serum Subcutaneous Fat Systolic Pressure Tissue, Adipose Tomography, Spiral Computed Uric Acid Waist Circumference White Person Woman X-Ray Computed Tomography
5
Metabolic Syndrome Severity Scoring
Cited 14 times
Components of the metabolic syndrome were measured using similar approaches for both cohorts as previously described [16 , 17 (link)]. The metabolic syndrome was defined using the criteria established by the ATP-III, i.e. the presence of three or more of the following criteria: elevated WC (≥102 cm for men, ≥88 cm for women), elevated fasting triacylglycerol (≥1.69 mmol/l [150 mg/dl]), reduced HDL-cholesterol (<1.04 mmol/l [40 mg/dl] for men, <1.29 mmol/l [50 mg/dl] for women), elevated BP (≥130 mmHg systolic or ≥85 mmHg diastolic, or drug treatment for hypertension) and elevated fasting blood glucose (≥5.55 mmol/l [100 mg/dl]) [4 (link)].
Continuous metabolic syndrome severity z scores at baseline were calculated for participants using sex- and race-based formulas. As described elsewhere [7 (link), 8 (link)], these scores were derived using a confirmatory factor analysis approach for the five traditional metabolic syndrome components (WC, triacylglycerol, HDL-cholesterol, systolic BP, fasting glucose) to determine the weighted contribution of each component to a latent metabolic syndrome ’factor’ on a sex- and race/ethnicity-specific basis. Confirmatory factor analysis was performed among adults aged 20–64 years from the National Health and Nutrition Examination Survey with categorisation into six subgroups based on sex and race/ethnicity (non-Hispanic white, non-Hispanic black, Hispanic). For each of these six population subgroups, loading coefficients for the five metabolic syndrome components were transformed into a single metabolic syndrome factor and used to generate equations to calculate a standardised metabolic syndrome severity score for each subgroup (http://mets.health-outcomes-policy.ufl.edu/calculator/, accessed 20 March 2017). The resulting metabolic syndrome severity scores are z scores (normally distributed and ranging from theoretical negative to positive infinity with mean=0 and SD=1) of relative metabolic syndrome severity on a sex- and race/ethnicity-specific basis. These scores correlate strongly with other markers of risk of the metabolic syndrome [18 (link)], including high-sensitivity C-reactive protein (hsCRP), uric acid and the homeostasis model of insulin resistance [8 (link)], with adiponectin [19 (link)] and with long-term risk of CVD [10 , 12 ] and diabetes [11 (link)].
Gurka M.J., Golden S.H., Musani S.K., Sims M., Vishnu A., Guo Y., Cardel M., Pearson T.A, & DeBoer M.D. (2017). Independent associations between a metabolic syndrome severity score and future diabetes by sex and race: the Atherosclerosis Risk In Communities Study and Jackson Heart Study. Diabetologia, 60(7), 1261-1270.
Publication 2017
Adiponectin Adult Blood Glucose C Reactive Protein Diabetes Mellitus Diastole Diet, Formula Ethnicity factor A Glucose High Blood Pressures High Density Lipoprotein Cholesterol Hispanics Homeostasis Insulin Resistance Metabolic Syndrome X Pharmaceutical Preparations Systole Systolic Pressure Triglycerides Uric Acid Woman

Most recents protocols related to «Adiponectin»

1
Conditional Knockout of IP3R1 in Mice
All mice used in this study were C57BL/6 background. To obtain mice with a conditional knockout allele of Ip3r1, exon 3 was selected as conditional knockout (cKO) region and flanked by loxp sites (referred to as floxed) using gene targeting in C57BL/6 embryonic stem (ES) cells and built by Cyagen Biosciences (Guangzhou, China). The procedure of knockout mice generation in this study was similar to that described previously [41 (link)], including construction of targeting vector, electroporation of ES cells, G418 selection, identification of homologous recombined ES cells, and generation of Ip3r1f/f mice. To generate mice lacking IP3R1 selectively in skeletal muscle, IP3R1-floxed mice were crossed with Myf5-cre mice (007893, Jackson Laboratory) expressing recombinase under the control of Myf5 promoter. Mice used for experiments were Ip3r1f/f (wild type, WT) and Myf5-cre+/-Ip3r1f/f (Ip3r1MKO) mice. To generate mice lacking IP3R1 selectively in adipocytes, we crossed IP3R1-floxed mice with Adipoq-cre mice expressing recombinase under the control of Adiponectin promoter. Adipoq-cre mice were purchased from the Jackson Laboratory (J010803). Mice used for experiments were Ip3r1f/f (WT) and Adipoq-cre+/−Ip3r1f/f (Ip3r1FKO) mice.
Zhang X., Wang L., Wang Y., He L., Xu D., Yan E., Guo J., Ma C., Zhang P, & Yin J. (2023). Lack of adipocyte IP3R1 reduces diet-induced obesity and greatly improves whole-body glucose homeostasis. Cell Death Discovery, 9, 87.
Full text: Click here
Publication 2023
Adipocytes Adiponectin Alleles antibiotic G 418 Cloning Vectors Electroporation Embryonic Stem Cells Exons ITPR1 protein, human Mice, House Mice, Knockout Mice, Laboratory Recombinase Skeletal Muscles
2
Mendelian Randomization Analysis of Glycemic Traits and Myopia
We employed a two-sample MR design to investigate the effect of exposures on myopia outcome using genome-wide association study (GWAS) summary statistics of independent studies. A total of six glycemic traits—adiponectin, body mass index (BMI), fasting blood glucose, fasting insulin, hemoglobin A1c (HbA1c), and proinsulin levels—were used as exposures, and myopia was defined as the outcome. The MR design relies on three assumptions (Fig. 1).13 (link) First, the genetic instruments must be robustly associated with the alleged biomarker of interest. Second, the genetic instruments must be associated with the outcome only via the exposure and not via a different biological pathway independent of the exposure. Third, the genetic instruments must not be associated with any confounders of the exposure–outcome relationship. Multiple testing was performed using the Bonferroni correction, and the significant P value was <8.3 × 10−3 (= 0.05/6).
Li F.F., Zhu M.C., Shao Y.L., Lu F., Yi Q.Y, & Huang X.F. (2023). Causal Relationships Between Glycemic Traits and Myopia. Investigative Ophthalmology & Visual Science, 64(3), 7.
Publication 2023
Adiponectin Biological Markers Biopharmaceuticals Blood Glucose Genome-Wide Association Study Hemoglobin A, Glycosylated Index, Body Mass Insulin Myopia Proinsulin Reproduction
3
Mendelian Randomization Analysis of Glycemic Traits and Myopia
Cited 2 times
MR analysis between the exposures (adiponectin, BMI, fasting blood glucose, fasting insulin, HbA1c, and proinsulin levels) and myopia was performed using the TwoSampleMR v0.5.5 R package (R Foundation for Statistical Computing, Vienna, Austria).22 (link) The following standards were applied in the selection of genetic instruments for each glycemic trait: (1) P < 5 × 10−8 for each glycemic trait; (2) linkage disequilibrium r2 < 0.001; and (3) linkage disequilibrium distance > 10,000 kb. In this study, the inverse-variance weighted (IVW) method23 (link) was the main method used to estimate associations between glycemic traits and myopia. For sensitivity analysis, three additional approaches based on the TwoSampleMR R package were used, including MR-Egger regression,24 (link) weighted median method,25 (link) and weighted mode method,22 (link) which allowed for the presence of horizontal pleiotropy. Therefore, MR results were considered meaningful if the IVW method identified an association (P < 0.0083) and all four MR methods had effects in the same direction.
To further assess the robustness of these identified associations, the impact was assessed for potential horizontal pleiotropy; comprehensive sensitivity using the MR pleiotropy residual sum and outlier (MR-PRESSO) test,26 (link) Egger intercept calculation,24 (link) leave-one-out analysis,22 (link) heterogeneity tests,27 (link) and the Steiger test.28 (link)
Li F.F., Zhu M.C., Shao Y.L., Lu F., Yi Q.Y, & Huang X.F. (2023). Causal Relationships Between Glycemic Traits and Myopia. Investigative Ophthalmology & Visual Science, 64(3), 7.
Publication 2023
Adiponectin Blood Glucose Genetic Heterogeneity Genetic Selection Hypersensitivity Insulin Myopia Proinsulin
4
Molecular Profiling of Visceral Adipose and Liver
Visceral fat tissues and liver tissues were collected and lysed in RIPA buffer. Nuclear protein was extracted from the liver tissues as previously described.18 (link) Western blot was performed as previously described.19 (link) β-actin was used as a loading control (Sigma-Aldrich, St. Louis, MO, USA). The primary antibodies including interleukin-6 (IL-6), the cell-bound precursor of tumor necrosis factor-α (TNF-α), adiponectin, nuclear factor-erythroid factor 2-related factor 2 (Nrf2), and heme oxygenase-1 (HO-1) were purchased from Abcam (Cambridge, MA). Histone-3 antibody was purchased from Cell Signaling Technology (Danvers, MA).
Lin B, & Zhang X. (2023). Vitamin E Supplement Protects Against Gestational Diabetes Mellitus in Mice Through nuclear factor-erythroid factor 2-related factor 2/heme oxygenase-1 Signaling Pathway. Diabetes, Metabolic Syndrome and Obesity, 16, 565-574.
Publication 2023
Actins Adiponectin Antibodies Buffers Histones HMOX1 protein, human Immunoglobulins Interleukin-6 Liver NFE2L2 protein, human Nuclear Protein Radioimmunoprecipitation Assay Tissues tumor necrosis factor precursor Visceral Fat Western Blotting
5
Multicolor Immunostaining of Adipose Tissue
The following primary antibodies were used: rat anti-endomuchin antibody (V. 7C7) (Santa Cruz Biotechnology, CA, USA) (1:100), goat anti-mouse leptin receptor (R&D SYSTEMS, Minneapolis, MN, USA) (1:100), goat anti-mouse-CD31antibody coupled to Alexa Fluor (AF) 488 (BD Biosciences, San Jose, CA, USA) (1:50), rat anti-mouse osteocalcin antibody (R21C-01A) (1:200) (Takara, Shiga, Japan), rat anti-CD45 antibody coupled to allophycocyanine (APC) (30-F11) (1:500) and rat anti-Ter119 (TER-119) antibody coupled to APC (1:500) (both from Thermo Fisher Scientific, Waltham, MA, USA), rat anti-Ly-6A/E (Sca-1) antibody coupled to AF 488 (D7) (1:500), hamster anti-CD29 antibody coupled to AF 488 (HMβ1-1) (1:500), rat anti-CD90.2 antibody coupled to AF 488 (30-H12) (1:500), rat anti-CD146 antibody coupled to AF 488 (ME-9F1), isotype control rat IgG2a, κ coupled to AF 488 (RTK2758) (1:500) and isotype control hamster IgG coupled to AF 488 (HTK888) (1:500) (all from BioLegend, San Diego, CA, USA), rabbit anti-adiponectin antibody (1:10) (Novus Biologicals, Centennial, CO, USA), rabbit anti-osterix antibody (1:100) (Abcam, Cambridge, UK) and normal rabbit IgG (60024B) (R&D SYSTEMS).
Donkey anti-rat coupled to AF 647 (1:1000) (Abcam), donkey anti-rabbit coupled to AF 488 (1:1000) and donkey anti-goat coupled to AF 647 (1:1000) (both from Thermo Fisher Scientific) were used as the secondary antibodies for immunostaining. Nuclei were stained using Hoechst 33342 (Thermo Fisher Scientific). Dissection, sectioning, and staining for each experiment were always performed at the same time and conditions.
Oka H., Ito S., Kawakami M., Sasaki H., Abe S., Matsunaga S., Morita S., Noguchi T., Kasahara N., Tokuyama A., Kasahara M., Katakura A., Yajima Y, & Mizoguchi T. (2023). Subset of the periodontal ligament expressed leptin receptor contributes to part of hard tissue-forming cells. Scientific Reports, 13, 3442.
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Publication 2023
Adiponectin alexa fluor 488 Alexa Fluor 647 Antibodies Antibodies, Anti-Idiotypic Biological Factors Cell Nucleus Dissection Equus asinus Goat Hamsters HOE 33342 IgG2A Immunoglobulin Isotypes Immunoglobulins leptin receptor, mouse Mice, House Novus Osteocalcin Rabbits Spinocerebellar Ataxia Type 1 Thy-1 Antigens

Top products related to «Adiponectin»

1
Adiponectin by R&D Systems
Sourced in United States, United Kingdom, Sweden
Adiponectin is a protein secreted by adipose tissue that plays a role in regulating glucose and lipid metabolism. It is involved in the modulation of insulin sensitivity and energy homeostasis.
2
Leptin by R&D Systems
Sourced in United States, United Kingdom, Japan, Sweden, Germany
Leptin is a protein hormone produced primarily by adipocytes (fat cells) in the body. It plays a key role in regulating energy homeostasis, appetite, and body weight. Leptin acts on specific receptors in the hypothalamus of the brain to signal the body's energy status and modulate food intake and energy expenditure.
3
Adiponectin by Merck Group
Sourced in United States, Germany, Canada, Spain, Sao Tome and Principe, Switzerland
Adiponectin is a laboratory equipment product manufactured by Merck Group. It is a protein involved in the regulation of glucose and fat metabolism.
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Interleukin 6 (il 6) by R&D Systems
Sourced in United States, United Kingdom, Germany, China, Japan, Canada, Sweden, France, Switzerland, Mongolia, Czechia, Austria, Israel
IL-6 is a recombinant human interleukin-6 protein. Interleukin-6 is a multifunctional cytokine that regulates immune response, inflammation, and hematopoiesis.
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Fetal bovine serum (fbs) by Thermo Fisher Scientific
Sourced in United States, China, United Kingdom, Germany, Australia, Japan, Canada, Italy, France, Switzerland, New Zealand, Brazil, Belgium, India, Spain, Israel, Austria, Poland, Ireland, Sweden, Macao, Netherlands, Denmark, Cameroon, Singapore, Portugal, Argentina, Holy See (Vatican City State), Morocco, Uruguay, Mexico, Thailand, Sao Tome and Principe, Hungary, Panama, Hong Kong, Norway, United Arab Emirates, Czechia, Russian Federation, Chile, Moldova, Republic of, Gabon, Palestine, State of, Saudi Arabia, Senegal
Fetal Bovine Serum (FBS) is a cell culture supplement derived from the blood of bovine fetuses. FBS provides a source of proteins, growth factors, and other components that support the growth and maintenance of various cell types in in vitro cell culture applications.
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Mrp300 by R&D Systems
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The MRP300 is a precision microplate reader designed for high-throughput, multi-mode detection of fluorescence, luminescence, and absorbance in a 96-well format. It features a xenon flash lamp, monochromator-based optics, and advanced detection capabilities to provide accurate and reliable measurements across a wide range of applications.
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Insulin by Mercodia
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Insulin is a hormone produced by the pancreas that regulates blood sugar levels. It is an essential lab equipment used in various research and clinical applications involving the study of diabetes and other metabolic disorders.

More about "Adiponectin"

Adiponectin, also known as Acrp30, apM1, GBP28, and AdipoQ, is a protein hormone secreted by adipose tissue (fat cells) that plays a crucial role in regulating glucose and lipid (fat) metabolism.
This adipokine, or adipose-derived hormone, has been linked to various health conditions, including type 2 diabetes, obesity, and cardiovascular disease.
Adiponectin levels are typically inversely correlated with body mass index (BMI) and insulin resistance.
In other words, individuals with higher adiponectin levels tend to have a lower BMI and better insulin sensitivity, which is important for maintaining healthy blood sugar levels.
Aside from its metabolic functions, adiponectin has been shown to have anti-inflammatory and cardioprotective properties.
It can help reduce inflammation, improve endothelial function (the health of blood vessel linings), and lower the risk of atherosclerosis (buildup of plaque in the arteries).
Leptin, another adipokine, and interleukin-6 (IL-6), a pro-inflammatory cytokine, are also closely related to adiponectin and play important roles in energy homeostasis and metabolic regulation.
Fasting blood sugar (FBS) and the macrophage-related protein MRP300 are also relevant biomarkers and factors associated with adiponectin signaling and function.
Optimizing adiponectin research protocols is crucial for advancing our understanding of this important hormone and its physiological and pathological roles.
PubCompare.ai's AI-driven platform can help researchers locate the best protocols from literature, pre-prints, and patents, enabeling them to enhance their research outcomes and drive forward the field of adiponectin biology.