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INTERRELATIONSHIPS BETWEEN LEPTIN RESISTANCE, BODY COMPOSITION, AND AGING IN ELDERLY WOMEN

2008· letter· en· W1522085116 on OpenAlexaboutno aff
Elena Zoico, Vincenzo Di Francesco, Luisa Bissoli, Gloria Mazzali, G Fontana, Ketti Giuliano, Ottavio Bosello, Mauro Zamboni

Bibliographic record

VenueJournal of the American Geriatrics Society · 2008
Typeletter
Languageen
FieldMedicine
TopicDietary Effects on Health
Canadian institutionsnot available
FundersMinistero dell’Istruzione, dell’Università e della Ricerca
KeywordsLeptinMedicineWaistInternal medicineEndocrinologyBody mass indexLean body massObesityInsulin resistanceCalorie restrictionFat massBody fat percentageBody weight

Abstract

fetched live from OpenAlex

To the Editor: It has recently been suggested that leptin resistance is a feature of aging.1 Leptin resistance may be responsible for increases in fat mass and visceral fat mass2 and in particular for the greater metabolic risk observed in old age.3 In old rats, leptin infusion failed to produce a significant change in food intake, total or visceral fat mass, or insulin sensitivity.4 Moreover, in a sample of aging rats that underwent calorie restriction, leptin activity was markedly lower than in young controls.1,5 Lower availability of leptin in the hypothalamus, impaired peripheral leptin action, or both have been proposed as mechanisms of leptin resistance across aging.6,7 In humans, leptin resistance and aging have been poorly studied, because direct measurement of leptin sensitivity appears difficult. The aim of this study was to examine, in a group of healthy women, the relationships between aging, body composition, and leptin resistance, evaluated using a surrogate index. Thirty-four healthy women aged 27 to 83 with body mass indexes (BMIs) ranging from 18.5 to 41.5 kg/m2 were studied. (More details can be obtained from the corresponding author.) The ethical committee of the University Hospital of Verona approved the study, and subjects gave informed consent. BMI and waist circumference were obtained in all participants. Total body fat mass (FM) in kg and as a percentage of body weight (FM%) and lean body mass (LBM) were measured using dual-energy X-ray absorptiometry (DXA) (Hologic QDR 4500, Waltham, MA; System Software Version 11.1). In all subjects, in the morning, after 12 hours of fasting, resting energy expenditure (REE) was evaluated using indirect calorimetry (Vmax 29N Sensormedics).8 Insulin was measured using double-antibody radioimmunoassay (Diagnostic Products Corp., Los Angeles, CA) and leptin using enzyme-linked immunosorbent assay (DBC-Diagnostic Biochem Canada Inc, London, Ontario, Canada). The ratio between REE and fasting leptin levels was used as a surrogate index of leptin resistance.9 Leptin-resistant subjects have a low REE:leptin ratio, because high leptin levels are required to maintain a normal REE. Logarithmic transformation was performed for non-normally distributed variables. Data were analyzed using univariate and multivariate analysis of variance. Pearson correlation was used to test associations between variables. Stepwise multiple regression analysis was used to evaluate the effects of independent variables on leptin resistance. Table 1 shows the main characteristics of the women, stratified according to age and BMI. FM% was significantly higher in the group of elderly normal-weight than in younger normal-weight women but significantly lower than in overweight to obese women of the same age. The young women had higher REE:leptin ratios than the elderly normal-weight subjects (Table 1), even after adjustment for LBM (180.8±16.5 vs 95.6±20.5, P<.01). Moreover, a significantly lower REE:leptin ratio was found in overweight to obese elderly subjects than in normal-weight women of the same age after adjustment for LBM (31.6±17.3 vs 95.6±20.5, P<.05). In the whole group, the REE:leptin ratio was negatively related to age (correlation coefficient (r)=−0.723, P<.001), BMI (r=−0.622, P<.001), waist circumference (r=−0.646, P<.001), FM (r=−0.648, P<.001), FM% (r=−0.835, P<.001), and insulin (r=−0.352, P<.05). In a stepwise multiple regression analysis, FM% explained 72.3% of the REE:leptin ratio variance (β coefficient −0.857) and age an additional 4% (β coefficient −0.313), independent of BMI, waist circumference, LBM, and insulin. This study suggests that leptin resistance, as evaluated according to REE:leptin ratio, increases with aging and adiposity. Experimental studies in animals have showed that leptin's action is profoundly impaired in aging rats, independent of their body fat pattern.1,4,5 Old animals kept relatively lean and insulin sensitive using calorie restriction were resistant to leptin administration.1,5 In contrast with the observations of a previous study,1 old CD-1 mice who did not become obese with aging did not develop a defect in leptin transport across the blood–brain barrier;10 moreover, leptin resistance was reversible with modest weight reduction.10 In the current study, a significant negative relationship was observed between age and REE:leptin ratio, suggesting a decline in leptin sensitivity with aging. This decline was stronger in overweight to obese older women than in normal-weight age-matched women. When a multiple regression analysis was performed, the main predictor of leptin resistance was the degree of adiposity, with age explaining approximately 4% of REE:leptin ratio variance. In conclusion, this study shows that leptin resistance increases with adiposity and age. Although leptin resistance may be partially attributed to aging per se, leptin sensitivity appears to be strictly related to adiposity, even in aged individuals. Conflict of Interest: None of the authors had a personal or financial conflict of interest related to this manuscript. This study was supported by grants from MIUR project COFIN n 2005063885_005. Author Contributions: Study concept and design: EZ, LB, OB, and MZ. Acquisition of subjects: GM, GF, and KG. Analysis and interpretation of data: EZ and VDF. Preparation of manuscript: EZ, VDF, MZ, and GM. Sponsor's Role: None.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.487
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.283
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations8
Published2008
Admission routes1
Has abstractyes

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