Abstract
Introduction
There is no consensus regarding methods to estimate kidney function in hyperthyroidism. The aim was therefore to assess changes in filtration markers in patients with Graves’ disease undergoing treatment with antithyroid drugs.
Methods
Thirty patients with de novo Graves’ disease were included. Blood sampling, including thyroid-stimulating hormone, fT3, fT4 and creatinine, was performed at baseline, 6 weeks, 3, 6, 12 and 24 months. Cystatin C was measured from frozen samples. To calculate creatinine- and cystatin C-based eGFR, the Lund–Malmö equation (LMR) and the CAPA formula were used.
Results
fT3 and fT4 normalized during treatment. Creatinine increased initially but stabilized after 6 months. eGFRLMR decreased until 12 months. Cystatin C decreased, while eGFRCAPA and eGFRCAPA/eGFRLMR increased until 6 months. The mean of eGFRLMR and eGFRCAPA remained stable. The % changes in creatinine and cystatin C were associated with % changes in fT3 and fT4. In regression models including fT3 or fT4 with body weight (all % change), fT3 and fT4 were the strongest predictors of percentual changes in both creatinine and cystatin C.
Conclusion
The increase in creatinine and decrease in cystatin C during the treatment of Graves’ disease was significantly associated with changes in thyroid hormones, and for creatinine, also body weight. The mean of eGFRLMR and eGFRCAPA remained stable, suggesting that creatinine and cystatin C were affected by different non-GFR-related factors. The potential use of eGFRLMR and eGFRCAPA to assess kidney function in patients with thyroid disorders should be further evaluated in studies measuring kidney function with state-of-the-art methods.
Introduction
Graves’ disease (GD) is an autoimmune disease and the most common cause of hyperthyroidism. The incidence of GD in Sweden is 21 in 100,000 individuals annually and is most common in females aged 30–60 (1, 2, 3). It is believed that GD is a multifactorial disease with both genetic predisposition and environmental factors (4).
The thyroid gland and kidneys have a bidirectional interaction, both indirectly via hemodynamic regulation of kidney blood flow and directly via the effect of hormones on kidney tissue (1, 5, 6). Glomerular filtration rate (GFR) is a validated marker of kidney function recommended in treatment guidelines (7). GFR is most commonly estimated using equations based on creatinine (Cr) or cystatin C (CysC) (8). Each of these markers is influenced by a variety of diseases and conditions (non-GFR-related factors). While Cr is affected by age, sex, ethnicity and muscle mass, CysC is affected by inflammation, smoking and thyroid disorders (9, 10). CysC filters freely through the glomerulus, is completely reabsorbed and is not secreted, making it a reliable marker to measure glomerular filtration. CysC is independent of sex, not affected by muscle mass to the same extent as Cr and has been suggested as a better marker of kidney function (11, 12, 13).
It is well-known that thyroid dysfunction may affect kidney function, and vice versa. Subclinical hypothyroidism is commonly found in patients with chronic kidney disease (CKD). On the other hand, hypothyroidism is known to be a risk factor for the progression of CKD (14, 15, 16, 17). Conversely, there are studies suggesting that hyperthyroidism reduces the risk of end-stage kidney disease and that thyroid hormone replacement therapy has a preserving effect (18, 19). Increased thyroid-stimulating hormone (TSH) levels are associated with increased Cr, while suppressed TSH levels are associated with lower Cr. These alterations normalize after successful treatment of different thyroid dysfunctions (20, 21, 22).
Patients with hyperthyroidism typically experience weight loss due to increased metabolism, whereas hypothyroidism and treatment of hyperthyroidism are associated with weight gain (23, 24, 25, 26). Since levels of Cr are directly related to muscle mass (27), it is unclear whether these changes in Cr levels during the treatment of thyroid disorders are due to changes in body mass, the effects of thyroid hormones on kidney function or both. In contrast to Cr, CysC levels decrease in hypothyroidism and increase in hyperthyroidism. These changes reverse with the treatment of the thyroid disorder (28, 29, 30). However, not all studies are in agreement, and there are studies suggesting that CysC increases in both subclinical hypothyroidism and hyperthyroidism (31).
Thus there are uncertainties about whether changes in Cr and CysC are changes in endogenous kidney filtration markers or reflect de facto changes in kidney function. To be able to accurately evaluate kidney function in patients with thyroid disorders, validated markers of kidney function are needed. Currently, there is no consensus on which endogenous kidney function marker should be used to assess kidney function when it comes to patients with thyroid disorders. While some studies dissuade using CysC in this patient group (28), others consider it a reliable method to assess kidney function (32). The aim of this study was therefore to assess and describe changes in filtration markers in patients with GD undergoing treatment with antithyroid drugs (ATDs).
Patients and methods
Patients
In this study, we included 30 adult patients (28 women and 2 men), 30–80 years old, with newly diagnosed GD who were referred to the outpatient endocrinology department at Uppsala University Hospital in Uppsala. These patients were recruited in 2017 and the cohort has been described previously (33, 34, 35). GD was defined as low levels of TSH and positive thyrotropin receptor antibodies (TRAbs). TRAb was negative (1.7 IE/L, reference <1.75 IE/L) in one patient. However, this patient had laboratory results and symptoms compatible with GD with high and homogeneous uptake in scintigraphy and was therefore included in the study. No one who was pregnant or planning to become pregnant was included in this study.
Patients were treated according to the standard routine with ‘block-and-replace’ treatment. In this regimen, 10–15 mg of methimazole or 150–300 mg of propylthiouracil was initiated as ATD at the first appointment. When thyroid hormone levels reached the normal range, 50–100 μg of thyroxine daily was added. We aimed to keep TSH in the low-normal range. Three patients received corticosteroid treatments due to endocrine orbitopathy during treatment for GD. Two received oral steroids, and one received intravenous steroids. One patient received radioiodine (RAI) after 5 months of treatment upon the patient’s request. Four patients received RAI after 10–15 months of treatment due to inadequate effects of ATD and rising TRAb levels. Three patients underwent total thyroidectomy, two out of the three because of neutropenia due to methimazole treatment, (one directly after the start of treatment and one after 10 months) and one after 5 months because of a desire to get pregnant. Of the 22 remaining patients, 21 received treatment for 18–24 months until achieving negative TRAb levels, while one experienced spontaneous recovery before starting treatment. The study was complied with the Declaration of Helsinki and was approved by the Regional Ethics Committee in Uppsala (Dnr 2015/469). Study procedures were performed according to the principles of the Declaration of Helsinki, and written informed consent was obtained from all participants.
Assessments
Blood samples including TSH, free triiodothyronine (fT3), free thyroxine (fT4) and Cr were taken at the baseline and followed up after 6 weeks, 3, 6, 12 and 24 months. CysC was later analyzed from frozen EDTA plasma samples. The laboratory testing was performed using routine methods at Uppsala University Hospital. TSH, fT3, fT4, Cr and CysC were analyzed on Cobas Pro instruments (Roche Diagnostics, Switzerland), with reagents from the same manufacturer. Thyroid hormones were analyzed on the e801 module and the GFR markers on the c503 module. Cr was measured with an IDMS-calibrated enzymatic method. To calculate Cr-based eGFR, the Lund–Malmö model (LMR) was used (36), and to calculate the CysC-based eGFR, we used the CAPA formula (37). We chose to use eGFRLMR and eGFRCAPA in this study as these equations are generally recommended for patient care in Sweden. Both equations are also based on large Swedish patient cohorts and should therefore be better adapted to our patient cohort than equations based on populations with other nationalities. Two weighing scales, the Avery Berkel-95 and the ECS, both regularly calibrated, were used to measure body weight at baseline, 12 months and 24 months after baseline. Body weight was also obtained at 1.5, 3 and 6 months, although not for all subjects and using different scales. Height was measured at baseline and was used to calculate body mass index (BMI) at baseline and follow-up. The missingness of outcome variables by time point is summarized in Supplementary Table S1 (see section on Supplementary materials given at the end of the article).
Statistical methods
Temporal trajectories of measurements were analyzed with random intercept linear mixed models using the lme4 package (vers. 1.1.34, Bates, 2015) in R (vers. 4.2.2, R core team, 2021). Time was treated as a categorical variable. In case of a significant type III fixed effect of time, estimated marginal means at follow-up time points (1.5–24 months) were contrasted against baseline and against the previous time point using the emmeans package (vers. 1.8.9, Lfenth, 2023), correcting for multiplicity by the false discovery rate method. Random intercept linear mixed models were also constructed to investigate the impact of body weight change and thyroid hormone dynamics on markers of kidney function at all follow-up time points (1.5–24 months), all expressed in % of baseline measurements. In these models, the estimates of fixed effects were evaluated. Due to the incomplete coverage and non-standardized assessment of body weight at 1.5, 3 and 6 months, these time points were also excluded as a sensitivity analysis.
Results
During follow-up, fT3 (Fig. 1A) and fT4 (Fig. 1B) decreased significantly and normalized. fT3 and fT4 levels stabilized at 6 and 3 months, respectively, after which no significant change was observed. Body weight (Fig. 1C) and BMI (Fig. 1E) increased significantly compared to baseline at 3–24 months, but did not differ significantly between adjacent time points.
Trajectories of fT3, fT4 and body weight during the study period. Data are shown as geometric means and standard deviations (A, B, C, E) or dot plots with median (D). All dependent variables have been transformed to the natural logarithm before statistical analysis; fT3 was log-transformed twice. *P < 0.05 vs visit 1 (0 months), † P < 0.05 vs previous time point, referring to the comparison of estimated marginal means obtained from linear mixed models (summarized in Supplementary Table S2). Dashed horizontal lines in panels a and b refer to the upper reference limit of each analyte.
Citation: Endocrine Connections 14, 5; 10.1530/EC-24-0698
Cr increased (Fig. 2C) and the eGFRLMR (Fig. 2D) decreased significantly during follow-up but stabilized after 6 and 12 months, respectively. CysC levels (Fig. 2A) decreased, and the eGFRCAPA (Fig. 2B) increased significantly during follow-up. After 6 months, they did not change in any consistent direction. The ratio eGFRCAPA/eGFRLMR (Fig. 2E) increased significantly during follow-up, stabilizing at 6 months. By contrast, the mean of eGFRLMR and eGFRCAPA (Fig. 2F) remained stable, with only minor fluctuations during the study period and only differed significantly compared to baseline values at 6 months. Results from linear mixed models presented above and in Figs 1 and 2 are summarized in Supplementary Tables S2 and S3.
Trajectories of kidney markers during the study period. Data are shown as geometric means and standard deviations. All dependent variables have been transformed to the natural logarithm before statistical analysis. *P < 0.05 vs visit 1 (0 months), † P < 0.05 vs previous time point, referring to the comparison of estimated marginal means obtained from linear mixed models (summarized in Supplementary Table S3).
Citation: Endocrine Connections 14, 5; 10.1530/EC-24-0698
The trajectory of Cr and CysC did not deviate consistently as a result of corticosteroid treatment (n = 3), radioiodine (n = 5) or thyroidectomy (n = 3) during the follow-up period (data not shown).
The percentual changes of Cr and CysC were strongly associated with the percentual changes of fT3 and fT4 during follow-up (Table 1, Fig. 3A, B, C, D). The direction of the associations was negative for Cr and positive for CysC. The associations were particularly strong for the change of fT3, which explained 29.2% (marginal R 2 = 0.292) of the variation of Cr change and 31.2% of the variation of CysC change (marginal R 2 = 0.312). With regard to percentual change in body weight, there was a significant positive association with the change of Cr and a negative trend with the change of CysC (Table 1, Fig. 3E and F). In models combining changes of fT3 or fT4 with the change in body weight as independent variables, fT3 and fT4 remained significantly associated with changes of both Cr and CysC, while a change of body weight was only independently associated with the change of Cr (Table 1). In sensitivity analyses, excluding time points 1.5, 3 and 6 months, associations between changes in body weight and kidney markers were attenuated below the significance level, whereas the associations between changes in thyroid hormones and kidney markers were enhanced (Supplementary Table S4).
Associations between percentual changes of kidney function markers, fT3, fT4 and body weight.
| Model | Cystatin C | Creatinine | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| m1 | m2 | m3 | m4 | m5 | m6 | m7 | m8 | m9 | m10 | ||
| Fixed effects | |||||||||||
| (Intercept) | Std est (SE) | −0.00 | −0.00 | −0.08 | −0.06 | −0.05 | 0.01 | 0.01 | −0.04 | −0.04 | −0.05 |
| (0.12) | (0.12) | (0.15) | (0.11) | (0.11) | (0.12) | (0.12) | (0.15) | (0.12) | (0.13) | ||
| fT3 | Std est (SE) | 0.59 ‡ | na | na | 0.56 ‡ | na | −0.53 ‡ | na | na | −0.47 ‡ | na |
| (0.09) | na | na | (0.11) | na | (0.08) | na | na | (0.11) | na | ||
| fT4 | Std est (SE) | na | 0.57 ‡ | na | na | 0.60 ‡ | na | −0.43 ‡ | na | na | −0.36 † |
| na | (0.08) | na | na | (0.10) | na | (0.08) | na | na | (0.11) | ||
| Body weight | Std est (SE) | na | na | −0.18 | −0.08 | −0.11 | na | na | 0.26 † | 0.20* | 0.23 † |
| na | na | (0.09) | (0.09) | (0.08) | na | na | (0.08) | (0.08) | (0.08) | ||
| Random effects | |||||||||||
| Id | Var | 0.01 | 0.01 | 0.01 | 0.01 | 0.01 | 0.01 | 0.02 | 0.02 | 0.01 | 0.01 |
| Residual | Var | 0.01 | 0.01 | 0.01 | 0.01 | 0.01 | 0.02 | 0.02 | 0.01 | 0.01 | 0.01 |
| Obs | 148 | 148 | 104 | 103 | 103 | 149 | 149 | 103 | 103 | 103 | |
| R2m | 0.319 | 0.306 | 0.031 | 0.328 | 0.374 | 0.292 | 0.198 | 0.070 | 0.306 | 0.212 | |
| R2c | 0.622 | 0.601 | 0.523 | 0.589 | 0.601 | 0.631 | 0.580 | 0.655 | 0.666 | 0.625 | |
Results from linear mixed models with random intercept. Percentual changes of all variables with respect to visit 1 have been pooled from visits 2 through 6. Std est, standardized estimate of fixed effect; SE, a standard error; na, not applicable, i.e., variable not included; Var, variance; R2m, marginal R2, quantifying how much of the variation of the dependent variable that is explained by the fixed effects; R2c, conditional R2, quantifying how much of the dependent variable variation that is explained by the fixed and random effects.
P < 0.05.
P < 0.01.
P < 0.001.
Associations of percentual changes with respect to baseline (0 months) of cystatin C and creatinine vs fT3, fT4 and body weight during the study period. Data are shown as a scatter plot of real data (open circles) and model-predicted values (solid lines) with 95% CI (dashed lines). Predicted values, 95% CI and annotated P-values of fixed effects estimates have been obtained from linear mixed models (summarized in Table 1 as model m1–m3 and m6–m8).
Citation: Endocrine Connections 14, 5; 10.1530/EC-24-0698
Discussion
In this study, we describe how the endogenous kidney function markers Cr and CysC change over time (up to 24 months) in patients with GD treated with ATDs. Both fT3 and fT4 normalized through treatment. During the same time, Cr and CysC diverged in their trajectories. Cr increased and stabilized after 6 months of treatment without significant changes after that, but eGFRLMR continued to decrease until 12 months. This could partly be explained by patients aged by 2 years during the treatment since age is used in the LMR equation. Significant changes in CysC and eGFRCAPA stabilized around 6 months after diagnosis, and eGFRCAPA/eGFRLMR exhibited a similar pattern (Fig. 1).
In our study, the change in Cr and CysC was associated with changes in hormone levels (fT3 and fT4) and took place during the first 6 months. More rapid changes in CysC than Cr have been reported in other populations, and CysC has therefore been suggested as a better marker of kidney function in, for example, critically ill patients with known rapid fluctuations in kidney function (38, 39, 40, 41). In our study, we observed an early increase in Cr, which could be due to a combination of ATD and changes in hormone levels affecting weight and muscle mass (27). This could be a possible explanation for the discrepancy with other studies that were not conducted on patients with thyroid disorders.
Several studies have reported similar dissociations of kidney markers in thyroid disease. In 2005, Yoshitake Suzuki et al. (29) showed that eGFRLMR decreased and eGFRCAPA increased during the treatment of GD, but the difference was not significant, and they therefore suggested that both markers could effectively be used in clinical practice. Manetti et al. (42) also observed that CysC levels decreased with hypothyroidism and increased with hyperthyroidism. However, they noted that in most cases, the CysC levels remained within the normal range. Therefore, they concluded that the use of CysC has limitations in assessing the peripheral effects of thyroid hormones. In contrast, Ye et al. (31) showed that CysC was elevated in both hypo- and hyperthyroidism and therefore advised against using CysC as a marker of kidney function in any patient with a thyroid disorder.
Efforts to map the mechanisms linking kidney markers and thyroid hormones have mainly focused on thyroid hormones’ hemodynamic effects. An increase in thyroid hormones leads to increased cardiac output, decreased vascular resistance and thus potentially increased kidney blood flow (1, 43). fT3 is known to be a potent vasodilator. Furthermore, it increases contractility and enhances cardiac output through both expressing specific cardiac genes in myocytes and non-genomic pathways by modulating ion channels (44). Allam et al. (45) in their study noted a significant decrease in total peripheral resistance and flow-mediated dilation and an increase in renal arterial distensibility in patients with subclinical hyperthyroidism. They concluded that the changes in kidney glomerular and tubular functions observed in this patient group are driven both directly by the vascular effects of hormones and indirectly by hypertension. Jiménez et al. (46) and Marchant et al. (47) showed that the increase in concentration and activity of plasma renin in patients with hyperthyroidism was positively associated with fT3, but not fT4 levels. CysC, unlike Cr, is produced in all nucleated cells constantly (9). Christoph Schmid et al. (48) suggested that the increase in CysC production results from the direct effect of fT3 hormones on cells throughout the body, and using T3-responsive osteoblast cells, they observed increased CysC mRNA expression and CysC production, which they interpreted as a consequence of T3-induced metabolic activity. Kotajima et al. (49) suggested that fT3 together with transforming growth factor-β1 (TGF-β1) has a stimulatory effect on the production of CysC in patients with hyperthyroidism. TGF-β1 stimulates vascular smooth muscle cells to secrete CysC. Both TGF-β1 and fT3 stimulate the secretion of CysC and increase the expression of CysC mRNA in vivo. In line with this, studies have shown decreased levels of TGF-β1 in patients with hypothyroidism. This is also in line with the fact that CysC production is influenced by and increases with glucocorticoid treatment, similar to the effects of cell receptor hormones such as fT3 and growth hormone (50, 51). Whether these changes in CysC are just changes in the production of endogenous kidney filtration markers or reflect true fluctuation in kidney function is still debated (28, 32).
In this study, the mean of eGFRLMR and eGFRCAPA remained unchanged before and during the treatment, which is consistent with Kimmel et al.’s study (28). In addition, Wang et al. (10) suggested that in cases of discordance between eGFRLMR and eGFRCAPA, combination methods provide a more accurate measure of kidney function than only either eGFRLMR or eGFRCAPA. After testing different combination equations of Cr and CysC, Nyman et al. (52) concluded that a simple mean of eGFRLMR and eGFRCAPA was as reliable for clinical use as more complex formulas. But, Grubb et al. (53) stated that if the difference between eGFRLMR and eGFRCAPA was ≥40%, an exogenous kidney function marker, such as iohexol, should be used to assess kidney function.
In the current study, the observed stable mean of eGFRLMR and eGFRCAPA could suggest a preserved kidney function over time in this patient group, and that Cr and CysC were affected by different non-GFR-related factors. Thyroid hormones influence both of these two kidney markers in different ways, and it remains uncertain whether these markers are affected by thyroid hormones to the same extent. Consistent with findings from other studies, an increase in body weight was observed, potentially secondary to the normalization of a previously elevated metabolism in GD (24, 54). This is of interest since, as previously mentioned, Cr is associated with muscle mass and body weight. Unfortunately, data on body weight was incomplete during the first 6 months of follow-up, when changes in kidney markers were most marked. Nevertheless, body weight exhibited a more protracted trajectory in comparison with thyroid hormones in our material. Moreover, changes of both fT3 and fT4 were strongly associated with changes in both Cr and CysC, independent of body weight change. Taken together, while Cr, unlike CysC, is highly influenced by body weight, the trajectory of neither kidney marker during the course of GD seems to be fully explained by concomitant weight gain, judging by the present results.
As previously mentioned, the stable mean of eGFRLMR and eGFRCAPA suggests that Cr and CysC were primarily influenced by different non-GFR-related factors and that there was no significant change in kidney function. However, this was not possible to be firmly ascertained in this study since kidney function was not measured using an exogenous kidney function marker (55, 56). Further studies are therefore warranted to evaluate the effect of thyroid hormones on kidney function and to determine the most suitable method to estimate and measure kidney function in these patients. Since the use of exogenous kidney function markers, such as iohexol, is time-consuming, cumbersome and therefore impractical in a clinical setting, validation of other methods, such as using the mean eGFRLMR and eGFRCAPA would be of considerable clinical utility.
Strengths of this study include the robust statistical methods employed, including visualization of mean eGFRLMR and eGFRCAPA, which is unique to this study. The main weakness of our study is the relatively small sample size, which may theoretically limit the generalizability of our results to larger populations of GD patients. This limitation is common in longitudinal studies but is often counterbalanced by the prospect of elucidating biological processes over time. In relation to the research question, the sample size in our study was evidently large enough to display clear and clinically meaningful results with adequate statistical power. As mentioned, the incomplete collection of body weight at critical time points is another weakness. We recognize that this limitation prevents strong conclusions regarding the relative importance of thyroid hormone and weight dynamics in explaining the temporal variation of kidney markers during the treatment of GD. Moreover, we did not assess body composition, which is unfortunate considering the strong correlation between muscle mass and Cr levels (27). In summary, our study showed that Cr increased and CysC decreased during treatment of GD with ATD, showing significant associations with changes in thyroid hormone levels (both fT3 and fT4) and, for Cr, potentially also body weight. Correspondingly, eGFRLMR decreased and eGFRCAPA increased, and thus the mean of eGFRLMR and eGFRCAPA remained stable, suggesting that both Cr and CysC, respectively, were affected by different non-kidney function-related factors over time. The potential use of eGFRLMR and eGFRCAPA to assess kidney function in patients with GD undergoing treatment with ATD should be further evaluated in studies also measuring kidney function with state-of-the-art methods.
Supplementary materials
This is linked to the online version of the paper at https://doi.org/10.1530/EC-24-0698.
Declaration of interest
The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the work reported.
Funding
The study was financed by grants from the Swedish state under an agreement between the Swedish government and the County Council Research Foundation in the Uppsala region, Sweden.
Author contribution statement
All authors fulfill the ICMJE requirements for authorship. SA participated in data analysis, helped to draft the manuscript and read and approved the final manuscript. ML participated in data analysis, was responsible for the statistical analyses, helped draft the manuscript and read and approved the final manuscript. ÖJ contributed to the study conception, study design, data collection and data analysis, made critical revisions to the draft manuscript for important intellectual content and read and approved the final manuscript. AOL contributed to the study conception and study design, participated in coordination and read and approved the final manuscript. MKS participated in data analysis, helped draft the manuscript and read and approved the final manuscript. SK contributed to the study conception and study design, participated in coordination, data collection and data analysis, helped draft the manuscript and read and approved the final manuscript. No funding sources were involved in the study design; the collection, analysis and interpretation of data; the writing process; or the decision to submit the article for publication.
Acknowledgments
We sincerely thank the nurses for their dedicated support, particularly in assisting with sample collection, and the patients for their invaluable participation, which made this study possible.
References
- 1↑
Vargas F , Moreno JM , Rodríguez-Gómez I , et al. Vascular and renal function in experimental thyroid disorders. Eur J Endocrinol 2006 154 197–212. (https://doi.org/10.1530/eje.1.02093)
- 2↑
Abraham-Nordling M , Byström K , Törring O , et al. Incidence of hyperthyroidism in Sweden. Eur J Endocrinol 2011 165 899–905. (https://doi.org/10.1530/EJE-11-0548)
- 3↑
Antonelli A , Ferrari SM , Ragusa F , et al. Graves’ disease: epidemiology, genetic and environmental risk factors and viruses. Best Pract Res Clin Endocrinol Metabol 2020 34 101387. (https://doi.org/10.1016/j.beem.2020.101387)
- 4↑
Marinò M , Latrofa F , Menconi F , et al. Role of genetic and non-genetic factors in the etiology of Graves’ disease. J Endocrinol Invest 2015 38 283–294. (https://doi.org/10.1007/s40618-014-0214-2)
- 5↑
Basu G & Mohapatra A . Interactions between thyroid disorders and kidney disease. Indian J Endocrinol Metab 2012 16 204–213. (https://doi.org/10.4103/2230-8210.93737)
- 6↑
Mariani LH & Berns JS . The renal manifestations of thyroid disease. J Am Soc Nephrol 2012 23 22–26. (https://doi.org/10.1681/ASN.2010070766)
- 7↑
Cusumano AM , Tzanno-Martins C & Rosa-Diez GJ . The glomerular filtration rate: from the diagnosis of kidney function to a public health tool. Front Med 2021 8 769335. (https://doi.org/10.3389/fmed.2021.769335)
- 8↑
Ebert N , Bevc S , Bökenkamp A , et al. Assessment of kidney function: clinical indications for measured GFR. Clin Kidney J 2021 14 1861–1870. (https://doi.org/10.1093/ckj/sfab042)
- 9↑
Fricker M , Wiesli P , Brändle M , et al. Impact of thyroid dysfunction on serum cystatin C. Kidney Int 2003 63 1944–1947. (https://doi.org/10.1046/j.1523-1755.2003.00925.x)
- 10↑
Wang Y , Adingwupu OM , Shlipak MG , et al. Discordance between creatinine-based and cystatin C-based estimated GFR: interpretation according to performance compared to measured GFR. Kidney Med 2023 5 100710. (https://doi.org/10.1016/j.xkme.2023.100710)
- 11↑
Abrahamson M . Human cysteine proteinase inhibitors. Isolation, physiological importance, inhibitory mechanism, gene structure and relation to hereditary cerebral hemorrhage. Scand J Clin Lab Invest Suppl 1988 191 21–31. (https://doi.org/10.1080/00365518809168291)
- 12↑
Abrahamson M , Olafsson I , Palsdottir A , et al. Structure and expression of the human cystatin C gene. Biochem J 1990 268 287–294. (https://doi.org/10.1042/bj2680287)
- 13↑
Fernando S & Polkinghorne KR . Cystatin C: not just a marker of kidney function. J Bras Nefrol 2020 42 6–7. (https://doi.org/10.1590/2175-8239-JBN-2019-0240)
- 14↑
Chonchol M , Lippi G , Salvagno G , et al. Prevalence of subclinical hypothyroidism in patients with chronic kidney disease. Clin J Am Soc Nephrol 2008 3 1296–1300. (https://doi.org/10.2215/CJN.00800208)
- 15↑
Hataya Y , Igarashi S , Yamashita T , et al. Thyroid hormone replacement therapy for primary hypothyroidism leads to significant improvement of renal function in chronic kidney disease patients. Clin Exp Nephrol 2013 17 525–531. (https://doi.org/10.1007/s10157-012-0727-y)
- 16↑
Rhee CM . The interaction between thyroid and kidney disease: an overview of the evidence. Curr Opin Endocrinol Diabetes Obes 2016 23 407–415. (https://doi.org/10.1097/MED.0000000000000275)
- 17↑
Kim HJ , Park SJ , Park HK , et al. Subclinical thyroid dysfunction and chronic kidney disease: a nationwide population-based study. BMC Nephrol 2023 24 64. (https://doi.org/10.1186/s12882-023-03111-7)
- 18↑
Shin DH , Lee MJ , Kim SJ , et al. Preservation of renal function by thyroid hormone replacement therapy in chronic kidney disease patients with subclinical hypothyroidism. J Clin Endocrinol Metab 2012 97 2732–2740. (https://doi.org/10.1210/jc.2012-1663)
- 19↑
Cho YY , Kim B , Shin DW , et al. Graves’ disease and the risk of end-stage renal disease: a Korean population-based study. Endocrinol Metab 2022 37 281–289. (https://doi.org/10.3803/EnM.2021.1333)
- 20↑
Shirota T , Shinoda T , Yamada T , et al. Alteration of renal function in hyperthyroidism: increased tubular secretion of creatinine and decreased distal tubule delivery of chloride. Metab Clin Exp 1992 41 402–405. (https://doi.org/10.1016/0026-0495(92)90075-l)
- 21↑
Sönmez E , Bulur O , Ertugrul DT , et al. Hyperthyroidism influences renal function. Endocrine 2019 65 144–148. (https://doi.org/10.1007/s12020-019-01903-2)
- 22↑
Naguib R & Elkemary E . Thyroid dysfunction and renal function: a crucial relationship to recognize. Cureus 2023 15 e35242. (https://doi.org/10.7759/cureus.35242)
- 23↑
Lahesmaa M , Orava J , Schalin-Jäntti C , et al. Hyperthyroidism increases brown fat metabolism in humans. J Clin Endocrinol Metab 2014 99 E28–E35. (https://doi.org/10.1210/jc.2013-2312)
- 24↑
Kim MJ , Cho SW , Choi S , et al. Changes in body compositions and basal metabolic rates during treatment of Graves’ disease. Int J Endocrinol 2018 2018 9863050. (https://doi.org/10.1155/2018/9863050)
- 25↑
Karmisholt J , Carlé A & Andersen S . Body weight changes in hyperthyroidism: timing and possible explanations during a one year repeated measurement study. Eur Thyroid J 2021 10 208–214. (https://doi.org/10.1159/000512078)
- 26↑
Yan Y , Xu M , Wu M , et al. Obesity is associated with subclinical hypothyroidism in the presence of thyroid autoantibodies: a cross-sectional study. BMC Endocr Disord 2022 22 94. (https://doi.org/10.1186/s12902-022-00981-0)
- 27↑
Baxmann AC , Ahmed MS , Marques NC , et al. Influence of muscle mass and physical activity on serum and urinary creatinine and serum cystatin C. Clin J Am Soc Nephrol 2008 3 348–354. (https://doi.org/10.2215/CJN.02870707)
- 28↑
Kimmel M , Braun N & Alscher MD . Influence of thyroid function on different kidney function tests. Kidney Blood Press Res 2012 35 9–17. (https://doi.org/10.1159/000329354)
- 29↑
Suzuki Y , Matsushita K , Seimiya M , et al. Paradoxical effects of thyroid function on glomerular filtration rate estimated from serum creatinine or standardized cystatin C in patients with Japanese Graves’ disease. Clin Chim Acta 2015 451 316–322. (https://doi.org/10.1016/j.cca.2015.10.018)
- 30↑
Can N , Ozsoy E , Kobat SG , et al. Serum cystatin C concentrations in patients with Graves’ ophthalmopathy. Korean J Ophthalmol 2020 34 398–403. (https://doi.org/10.3341/kjo.2020.0006)
- 31↑
Ye Y , Gai X , Xie H , et al. Impact of thyroid function on serum cystatin C and estimated glomerular filtration rate: a cross-sectional study. Endocr Pract 2013 19 397–403. (https://doi.org/10.4158/EP12282.OR)
- 32↑
Xin C , Xie J , Fan H , et al. Association between serum cystatin C and thyroid diseases: a systematic review and meta-analysis. Front Endocrinol 2021 12 766516. (https://doi.org/10.3389/fendo.2021.766516)
- 33↑
Khamisi S , Lundqvist M , Emadi P , et al. Serum thyroglobulin is associated with orbitopathy in Graves’ disease. J Endocrinol Invest 2021 44 1905–1911. (https://doi.org/10.1007/s40618-021-01505-8)
- 34↑
Khamisi S , Udumyan R , Sjölin G , et al. Fracture incidence in Graves’ disease: a population-based study. Thyroid 2023 33 1349–1357. (https://doi.org/10.1089/thy.2023.0162)
- 35↑
Khamisi S , Lundqvist M , Rasmusson AJ , et al. Vitamin D and bone metabolism in Graves’ disease: a prospective study. J Endocrinol Invest 2023 46 425–433. (https://doi.org/10.1007/s40618-022-01927-y)
- 36↑
Björk J , Grubb A , Sterner G , et al. Revised equations for estimating glomerular filtration rate based on the Lund–Malmö Study cohort. Scand J Clin Lab Invest 2011 71 232–239. (https://doi.org/10.3109/00365513.2011.557086)
- 37↑
Grubb A , Horio M , Hansson LO , et al. Generation of a new cystatin C-based estimating equation for glomerular filtration rate by use of 7 assays standardized to the international calibrator. Clin Chem 2014 60 974–986. (https://doi.org/10.1373/clinchem.2013.220707)
- 38↑
Villa P , Jiménez M , Soriano MC , et al. Serum cystatin C concentration as a marker of acute renal dysfunction in critically ill patients. Crit Care 2005 9 R139. (https://doi.org/10.1186/cc3044)
- 39↑
Ravn B , Prowle JR , Mårtensson J , et al. Superiority of serum cystatin C over creatinine in prediction of long-term prognosis at discharge from ICU. Crit Care Med 2017 45 e932–e940. (https://doi.org/10.1097/CCM.0000000000002537)
- 40↑
Zhang D , Gao L , Ye H , et al. Impact of thyroid function on cystatin C in detecting acute kidney injury: a prospective, observational study. BMC Nephrol 2019 20 41. (https://doi.org/10.1186/s12882-019-1201-9)
- 41↑
Haines RW , Fowler AJ , Liang K , et al. Comparison of cystatin C and creatinine in the assessment of measured kidney function during critical illness. Clin J Am Soc Nephrol 2023 18 997–1005. (https://doi.org/10.2215/CJN.0000000000000203)
- 42↑
Manetti L , Pardini E , Genovesi M , et al. Thyroid function differently affects serum cystatin Cand creatinine concentrations. J Endocrinol Invest 2005 28 346–349. (https://doi.org/10.1007/BF03347201)
- 43↑
Klein I & Danzi S . Thyroid disease and the heart. Circulation 2007 116 1725–1735. (https://doi.org/10.1161/CIRCULATIONAHA.106.678326)
- 44↑
Razvi S , Jabbar A , Pingitore A , et al. Thyroid hormones and cardiovascular function and diseases. J Am Coll Cardiol 2018 71 1781–1796. (https://doi.org/10.1016/j.jacc.2018.02.045)
- 45↑
Allam MM , El-Zawawy HT & El-Zawawy TH . Renal function changes in patients with subclinical hyperthyroidism: a novel postulated mechanism. Endocrine 2023 82 78–86. (https://doi.org/10.1007/s12020-023-03361-3)
- 46↑
Jiménez E , Montiel M , Narváez JA , et al. Effects of hyper- and hypothyroidism on the basal levels of angiotensin I and kinetic parameters of renin-angiotensin system in male rats. Rev Esp Fisiol 1982 38 149–154.
- 47↑
Marchant C , Brown L & Sernia C . Renin-angiotensin system in thyroid dysfunction in rats. J Cardiovasc Pharmacol 1993 22 449–455. (https://doi.org/10.1097/00005344-199309000-00016)
- 48↑
Schmid C , Ghirlanda-Keller C , Zwimpfer C , et al. Triiodothyronine stimulates cystatin C production in bone cells. Biochem Biophys Res Commun 2012 419 425–430. (https://doi.org/10.1016/j.bbrc.2012.02.040)
- 49↑
Kotajima N , Yanagawa Y , Aoki T , et al. Influence of thyroid hormones and transforming growth factor-β1 on cystatin C concentrations. J Int Med Res 2010 38 1365–1373. (https://doi.org/10.1177/147323001003800418)
- 50↑
Schmid C , Ghirlanda C , Zwimpfer C , et al. Cystatin C in adipose tissue and stimulation of its production by growth hormone and triiodothyronine in 3T3-L1 cells. Mol Cell Endocrinol 2019 482 28–36. (https://doi.org/10.1016/j.mce.2018.12.004)
- 51↑
Kleeman SO , Thakir TM , Demestichas B , et al. Cystatin C is glucocorticoid responsive, directs recruitment of Trem2+ macrophages, and predicts failure of cancer immunotherapy. Cell Genom 2023 3 100347. (https://doi.org/10.1016/j.xgen.2023.100347)
- 52↑
Nyman U , Grubb A , Sterner G , et al. Different equations to combine creatinine and cystatin C to predict GFR. Arithmetic mean of existing equations performs as well as complex combinations. Scand J Clin Lab Invest 2009 69 619–627. (https://doi.org/10.1080/00365510902946992)
- 53↑
Grubb A , Nyman U & Björk J . Improved estimation of glomerular filtration rate (GFR) by comparison of eGFRcystatin C and eGFRcreatinine. Scand J Clin Lab Invest 2012 72 73–77. (https://doi.org/10.3109/00365513.2011.634023)
- 54↑
Kyriacou A , Kyriacou A , Makris KC , et al. Weight gain following treatment of hyperthyroidism-A forgotten tale. Clin Obes 2019 9 e12328. (https://doi.org/10.1111/cob.12328)
- 55↑
Brown SC & O’Reilly PH . Iohexol clearance for the determination of glomerular filtration rate in clinical practice: evidence for a new gold standard. J Urol 1991 146 675–679. (https://doi.org/10.1016/s0022-5347(17)37891-6)
- 56↑
Delanaye P , Ebert N , Melsom T , et al. Iohexol plasma clearance for measuring glomerular filtration rate in clinical practice and research: a review. Part 1: how to measure glomerular filtration rate with iohexol? Clin Kidney J 2016 9 682–699. (https://doi.org/10.1093/ckj/sfw070)
