Serum Resistin (FIZZ3) Protein Is Increased in Obese Humans
Bibliographic record
Abstract
Degawa-Yamauchi et al.(1) demonstrate an increase in resistin in obese compared with lean adult subjects. They observe significant positive correlation between resistin and body mass index, however serum resistin was not a significant predictor of insulin resistance in their study. Serious flaws in the study design have led us to question their interpretation of the presented data. The authors compared lean and obese individuals of profoundly different ages and gender distribution (1). The lean group had a mean age of 33 ± 2 yr compared with 47 ± 1 yr in the obese group. Age-related changes in insulin resistance are well described. Similar changes in resistin with age may also occur, however this issue was not addressed by the authors. The sexual dimorphism in circulating levels of adipokines, such as leptin and adiponectin, is well described (2, 3). Based on the review of results published to date, sexual dimorphism in resistin levels has been observed in humans by several authors (2–4). Although the lean group had an equal number of male and female subjects, subjects in the obese group studied by Degawa-Yamauchi et al. were predominantly women (∼75%). Hence, higher resistin levels occurred in the obese group. While interpreting data on resistin levels, caution should be exercised in regard to gonadal hormone status in the studied subjects. It is obvious from the average age of the obese group (47 yr) that a significant number of postmenopausal women must have been included in this study. However, the gonadal hormone status and its relation to resistin levels or the presence of hormone replacement therapy is not discussed. This is an additional confounding variable that should have been addressed by the authors. Inclusion of diabetic subjects in the obese group could also have confounded the results. These diabetic subjects were treated with sulfonylurea, metformin, or insulin therapy. Because fasting insulin and glucose levels are affected by pharmacotherapy, homeostasis model assessment of insulin resistance is a poor measure of insulin resistance in this situation. Furthermore, hyperglycemia and glucotoxicity itself may have an effect on resistin expression independent of obesity (5). Indeed, the average glucose level (6.76 mmol/liter) in the obese group suggests the direct effect of glucotoxicity on insulin resistance in the obese group irrespective of resistin levels. The authors did not exclude the possibility of additional undiagnosed diabetic individuals in either group. An oral glucose tolerance test would have been necessary to ensure that diabetic individuals were not misclassified. These circumstances render rather difficult the authors’ effort to describe the relation between resistin, obesity, and insulin resistance. Statistical analysis and its description are insufficient to engender confidence in the authors’ interpretation of the data. Because the data for male and female subgroups were not presented separately, it is unclear whether the difference in resistin between lean and obese groups would remain significant after adjustment for gender and/or age. In terms of multiple linear regression analysis, it would be of interest to know what would be the power for studied parameters after exclusion of a patient with missing age, five diabetic subjects on pharmacotherapy, and subjects with “two outlying data points” for an unspecified variable. Although it remains to be determined by larger studies whether the physiological concentration of resistin in circulation plays a role in regulating insulin resistance of obesity in humans, clarification of the authors’ results might be useful to the readership of the JCEM.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".