Search Results

Objective

The present study re-evaluates the inverse log TSH–free thyroxine (fT₄) relationship, which has generally been assumed to characterize the thyroid pituitary hypothalamic feedback regulation in thyroid function.

Design and Methods

The correlation between fT₄ and TSH was analyzed in two data sets from differing time periods involving 3223 and 6605 patients referred for thyroid testing, representing the whole range of thyroid functions from hypothyroidism to hyperthyroidism.

Results

We found that the data do not support a linear log TSH–fT₄ relationship; instead, the correlation's gradient varies with thyroid function. As a consequence, an alternate model, based on the error function, was introduced. When directly comparing the models by means of curve fitting, using F-test and Akaike criteria, the alternate model results in a significantly better fit. The model was verified in the independent second set of data. Subgroup analysis of untreated patients added further proof to the non-linear model.

Conclusions

We propose a refined non-linear model to describe the relationship between TSH and fT₄. It implies that TSH response to a deviating fT₄ value may not be log-linear, but may be disproportionally related to the extent of the deviation from an optimum set point. A better understanding of the complex nature of the TSH–fT₄ relationship may further the development of more precise clinical models and aid in better defining subclinical states of thyroid dysfunction. Also, it may encourage other biological interrelations to be reconsidered in the wake of advanced measurement techniques and more powerful computerized statistical procedures.

Objective

In recognition of its primary role in pituitary–thyroid feedback, TSH determination has become a key parameter for clinical decision–making. This study examines the value of TSH as a measure of thyroid hormone homoeostasis under thyroxine (T₄) therapy.

Design and methods

We have examined the interrelationships between free triiodothyronine (FT₃), free T₄ (FT₄) and pituitary TSH by means of i) a retrospective analysis of a large clinical sample comprising 1994 patients either untreated or on varying doses of l-T₄ and ii) independent mathematical simulation applying a model of thyroid homoeostasis, together with a sensitivity analysis.

Results

Over a euthyroid to mildly hyperthyroid functional range, we found markedly different correlation slopes of log TSH vs FT₃ and FT₄ between untreated patients and l-T₄ groups. Total deiodinase activity (G_D) was positively correlated with TSH in untreated subjects. However, G_D was significantly altered and the correlation was lost under increasing l-T₄ doses. Ninety-five per cent confidence intervals for FT₃ and FT₄, when assessed in defined TSH concentration bands, differed significantly for l-T₄-treated compared with untreated patients. Higher doses were often needed to restore FT₃ levels within its reference range. Sensitivity analysis revealed the influence of various structural parameters on pituitary TSH secretion including an important role of pituitary deiodinase type 2.

Conclusion

The data reveal disjoints between FT₄–TSH feedback and T₃ production that persist even when sufficient T₄ apparently restores euthyroidism. T₄ treatment displays a compensatory adaptation but does not completely re-enact normal euthyroid physiology. This invites a study of the clinical consequences of this disparity.

Although pituitary thyrotropin (TSH) and thyroid hormones are physiologically interrelated, interpretation of measurements is conventionally done separately. Classification of subclinical thyroid dysfunction depends by definition solely on an abnormal TSH. This study examines a composite multivariate approach to disease classification.

Methods

Bivariate and trivariate reference limits were derived from a thyroid-healthy control group (n=271) and applied to a clinically diverse sample (n=820) from a prospective study, comparing their diagnostic efficiency with the conventional method.

Results

The following 95% reference limits were derived from the control group: (i) separate reference intervals for TSH, free thyroxine (FT4) and free triiodothyronine (FT3); (ii) bivariate composite reference limits for the logarithmically transformed TSH and FT4, and (iii) trivariate composite reference limits including all three parameters. A multivariate approach converts the “rectangular” or “cuboid” graphical representations of the independent parameters into an ellipse or ellipsoid. When applying these reference limits to the clinical sample, thyroid dysfunctions were classified differently, compared with the separate method, in 6.3 or 12% of all cases by the bivariate or trivariate method respectively. Of the established dysfunctions according to the separate intervals, 26% were reclassified to “euthyroid” by using the bivariate limit. Discrepancies from the laboratory-evaluated reference range were less pronounced.

Conclusions

Frequent divergencies between composite multivariate reference limits and a combination of separate univariate reference intervals suggest that statistical analytic techniques may heavily influence thyroid disease classification. This challenges the validity of the conjoined roles of TSH currently employed as both a sensitive screening test and a reliable classification tool for thyroid disease.

Objective

While androgen deprivation therapy (ADT) has been associated with insulin resistance and frailty, controlled prospective studies are lacking. We aimed to examine the relationships between insulin resistance and frailty with body composition and testosterone.

Design

Case–control prospective study.

Methods

Sixty three men with non-metastatic prostate cancer newly commencing ADT (n=34) and age-matched prostate cancer controls (n=29) were recruited. The main outcomes were insulin resistance (HOMA2-IR), Fried’s frailty score, body composition by dual x-ray absorptiometry and short physical performance battery (SPPB) measured at 0, 6 and 12months. A generalised linear model determined the mean adjusted difference (95% CI) between groups.

Results

Compared with controls over 12months, men receiving ADT had reductions in mean total testosterone level (14.1–0.4nmol/L, P<0.001), mean adjusted gain in fat mass of 3530g (2012, 5047), P<0.02 and loss of lean mass of 1491g (181, 2801), P<0.02. Visceral fat was unchanged. HOMA2-IR in the ADT group increased 0.59 (0.24, 0.94), P=0.02, which was most related to the increase in fat mass (P=0.003), less to lean mass (P=0.09) or total testosterone (P=0.088). Frailty increased with ADT (P<0.0001), which was related to decreased testosterone (P=0.028), and less to fat mass (P=0.056) or lean mass (P=0.79). SPPB was unchanged.

Conclusions

ADT is associated with increased insulin resistance and frailty within 12months of commencement, independently of confounding effects of cancer or radiotherapy. Insulin resistance appears to be mediated by subcutaneous or peripheral sites of fat deposition. Prevention of fat gain is an important strategy to prevent adverse ADT-associated cardiometabolic risks.

Objective

Indirect evidence suggests that the effects of testosterone on fat mass in men are dependent on aromatization to estradiol (E2). However, no controlled study has assessed the effects of E2 in the absence of testosterone.

Design

Six-month randomized, placebo-controlled trial with the hypothesis that men randomized to E2 would reduce their fat mass.

Methods

Seventy-eight participants receiving androgen deprivation therapy for prostate cancer were randomized to 0.9 mg of 0.1% E2 gel per day, or matched placebo. Dual x-ray absorptiometry body composition was measured at baseline, month 3, and month 6. The primary outcome was total fat mass.

Results

Serum E2 increased in the estradiol group over 6 months compared to placebo, and mean-adjusted difference (MAD) was 207 pmol/L (95% CI: 123–292), P  < 0.001. E2 treatment changed total fat mass, MAD 1007 g (95% CI: 124–1891), but not significantly, so P = 0.09. There were other consistent non-significant trends toward increased proportional fat mass, MAD 0.8% (95% CI: 0.0–1.6), P= 0.15; gynoid fat, MAD 147 g (95% CI: 2–293), P = 0.08; visceral fat, 53 g (95% CI: 1–105) P = 0.13; and subcutaneous fat, MAD 65 g (95% CI: 5–125), P = 0.11. Android fat increased, MAD 164 g (95% CI: 41–286), P = 0.04.

Conclusion

Contrary to our hypothesis, we provide suggestive evidence that E2 acting in the absence of testosterone, may increase total and regional fat mass in men. Given the premature closure of clinical trials due to the COVID pandemic, this potentially important observation should encourage additional studies to confirm or refute whether E2 promotes fat expansion in the absence of testosterone.