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Leptin

Leptin is a hormone produced by adipose tissue that plays a crucial role in regulating energy homeostasis, appetite, and metabolism.
It acts on the hypothalamus to suppress hunger and increase energy expenditure, making it a key target for research on obesity, diabetes, and related metabolic disorders.
Leptin research aims to uncover the mechanisms underlying its physiological effects and explore its potential as a therapeutic target.
PubCompare.ai helps optimize this research by locating the best protocols from literature, pre-prints, and patents, enhancing reproducibility and accuracy to ensure researchers find the most effective methods for their leptin studies.
Streamlien your leptin research with PubComapre.ai.

Most cited protocols related to «Leptin»

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).
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
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.
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
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.
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

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Publication 2010
AG-490 Carbohydrates Diet Diet, High-Fat Enzyme-Linked Immunosorbent Assay Fat-Restricted Diet Freezing Hormones Insulin Leptin Mice, Inbred C57BL Mus Neoplasms Neoplasms, Liver Pharmaceutical Preparations Proteins Serum Tissues TNFRSF1A protein, human Transplantation
For the first part of the analysis, 16S rRNA data sets were obtained from 111 studies of physical environments, covering 202 samples, as previously described (10 (link)). This data set consisted of 21 173 unique sequences. The three 16S rRNA data sets for the second part of the analysis were the following: (i) sequences from the bacteria in the distal cecum of 19 mice, consisting of a mixture of obese individuals homozygous for a mutation in the leptin gene and heterozygous and wild-type lean individuals, for a total of 3732 sequences (3453 unique sequences) (4 (link)), (ii) 16S rRNA sequences from the bacteria in five sites along gut transects of three healthy human individuals as well as stool for a total of 11 738 sequences (7761 unique sequences) (11 (link)) and (iii) 16S rRNA sequences from the bacteria in a depth profile of the hypersaline Guerrero Negro microbial mat, in Baha Mexico, consisting of about 11 738 sequences (11 164 unique sequences), obtained from 10 depths ranging from 0 to 40 mm (Harris, J.K., Walker, J.J. and Pace, N.R., unpublished data). These samples are from the location described in ref. (12 (link)).
Publication 2007
Bacteria Cecum Feces Genes Heterozygote Homo sapiens Homozygote Leptin Mice, House Mutation Negroes Obesity Physical Examination RNA, Ribosomal, 16S Walkers

Most recents protocols related to «Leptin»

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Example 1

To examine the function of ACATs in obesity, the expression patterns of ACAT1 and ACAT2 genes, and their gene products during adipogenesis of murine 3T3-L1 preadipocytes in vitro were examined. ACAT1 mRNA level was markedly increased in adipocytes from 2 days after initiation of adipogenesis (i.e., D2) as judged by real-time PCR assay (FIG. 1). However, ACAT2 mRNA level was similar between preadipocytes (D0) and mature adipocytes (D6) while a temporal reduction of ACAT2 level was observed at D2 (FIG. 1). In addition, white adipose tissue (WAT) isolated from high fat diet-induced obese mice displayed elevated mRNA level of ACAT1 and reduced mRNA level of ACAT2 when compared with those in lean mice as judged by real-time PCR assay. Leptin level was measured in WAT from lean and obese mice to ensure the development of obesity (FIG. 2). In addition, brown adipose tissue (BAT) from obese mice exhibited elevated levels of both ACAT1 and ACAT2. Uncoupling protein-1 (UCP-1) level was measured in BAT from lean and obese mice as a BAT-specific marker protein (FIG. 2). However, liver from lean and obese mice exhibited similar levels of ACAT1 and ACAT2 (FIG. 2).

Patent 2024
3T3-L1 Cells Adipocytes Adipogenesis a protein, mouse Biological Assay Brown Adipose Tissue Uncoupling Protein Brown Fat CES1 protein, human Diet, High-Fat Genes Leptin Liver Mice, Obese Mus Obesity Proteins Real-Time Polymerase Chain Reaction RNA, Messenger White Adipose Tissue

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Publication 2023
angiogen Angiogenesis Factor Biological Assay Cytokine Enzyme-Linked Immunosorbent Assay Epidermal growth factor Fibroblast Growth Factor 2 Hypersensitivity IGF1 protein, human IL6 protein, human Immunoassay Insulin-Like Growth Factor I Leptin Proteins Transforming Growth Factor beta Tumor Necrosis Factor-alpha Vascular Endothelial Growth Factors
Blood was collected during terminal procedures after fasting (5 h) and spun to isolate serum, then stored at − 80 °C. Serum samples were subsequently analysed for total cholesterol and triglycerides (Sigma, cat: MAK043 and MAK266 respectively) or Insulin and Leptin (Crystal Chem, cat 90080 and 90030 respectively) according to manufacturer’s instructions.
Publication 2023
BLOOD Cholesterol Insulin Leptin Serum Triglycerides
The mouse leptin ELISA kit (Abcam, ab100718) was used according to the manufacturer’s instructions. Samples were run in duplicates. Data were acquired using an Infinite 200 PRO microplate reader (Tecan).
Publication 2023
Enzyme-Linked Immunosorbent Assay Leptin Mice, House
Fasting blood samples were collected from all participants and sent to a commercial laboratory for analysis (BML Inc., Tokyo, Japan). All samples were analyzed for triglycerides, free fatty acids, insulin growth factor-1 (IGF-1), leptin, and adiponectin.
Publication 2023
ADIPOQ protein, human BLOOD Fibrinogen Insulin Leptin Nonesterified Fatty Acids Triglycerides

Top products related to «Leptin»

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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.
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Leptin is a hormone produced by adipose (fat) tissue. It plays a role in regulating energy balance, appetite, and metabolism. Leptin lab equipment is used for the detection and quantification of leptin levels in biological samples.
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Leptin is a hormone produced by adipose tissue that regulates energy balance and metabolism. It acts on the hypothalamus to inhibit hunger and promote feelings of satiety. Leptin plays a key role in regulating body weight and is involved in various physiological processes.
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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.

More about "Leptin"

Leptin is a crucial hormone produced by adipose (fat) tissue that plays a vital role in regulating energy balance, appetite, and metabolism.
It acts on the hypothalamus, a part of the brain, to suppress hunger and increase energy expenditure, making it a key target for research on obesity, diabetes, and related metabolic disorders.
Leptin research aims to uncover the complex mechanisms underlying its physiological effects and explore its potential as a therapeutic target for various metabolic conditions.
Researchers utilize a range of techniques, including cell culture studies, animal models, and human clinical trials, to understand the role of leptin in energy homeostasis, appetite regulation, and metabolic processes.
In addition to leptin, other hormones like insulin and adiponectin also play important roles in these physiological processes.
Insulin, for example, is a hormone produced by the pancreas that helps regulate blood sugar levels and promote the storage of energy as fat.
Adiponectin is another adipose-derived hormone that has been implicated in insulin sensitivity and energy metabolism.
Researchers often employ techniques like TRIzol reagent, a popular RNA extraction method, to isolate and analyze the expression of genes and proteins involved in leptin signaling and metabolic pathways.
By understanding the intricate interactions between leptin and other key regulators of energy balance, scientists can develop more effective strategies for the prevention and treatment of obesity, diabetes, and related metabolic disorders.
PubCompare.ai is a valuable tool that helps streamline leptin research by locating the best protocols from scientific literature, preprints, and patents.
Its AI-driven comparisons enhance the reproducibility and accuracy of research methods, ensuring researchers find the most effective techniques for their leptin studies.
With PubCompare.ai, researchers can optimize their investigations and accelerate the progress towards unlocking the full potential of leptin as a therapeutic target.