Abstract
Background
Resistance to thyroid hormone shows underlying mechanical link with chronic kidney disease (CKD) and is prevalent in CKD population. However, whether it attributes to mortality risk among CKD population is unknown. This study aimed to examine the association of thyroid hormone resistance (THR) with all-cause and cause-specific mortality among CKD individuals.
Methods
This study extracted CKD population from the National Health and Nutrition Examination Survey (NHANES) 2007–2012. Death outcomes were ascertained by linkage to National Death Index records through 31 December 2019. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence interval (CI) for mortality from all causes, cardiovascular disease (CVD).
Results
A total of 1,634 adults with CKD were included in the cohort, in which 663 deaths were recorded during an average follow-up of 8.72 years. After multivariate adjustment, resistance to thyroid hormone was significantly associated with higher all-cause and CVD mortality. There was an 18% and 31% increase in risks of all-cause and CVD mortality per-standard deviation (SD) increment in Parametric Thyroid Feedback Quantile-based Index (PTFQI) respectively. When PTFQI was analyzed as categorical variable (classified according to PTFQI percentiles), after adjusted for potential confounders and taking PTFQI ≤ P5 as reference, the HRs and 95% CIs in category with PTFQI > P95 for all-cause mortality and CVD mortality were 2.12 [1.10, 4.09] (p for trend 0.026) and 5.14 [1.81, 14.60] for (p for trend 0.018), respectively.
Conclusions
Resistance to thyroid hormone, centered on variations in the typical pituitary response to thyroid hormones, may independently correlate with all-cause and CVD mortality in CKD patients.
Keywords: Chronic kidney disease, mortality, Parametric Thyroid Feedback Quantile-based Index, thyroid hormone resistance
Introduction
Chronic kidney disease (CKD) affects approximately 15% of adults in the United States and represents a growing public health challenge worldwide [1,2]. CKD can incur significant costs and burdens, particularly when it advances to end-stage renal disease (ESRD). CKD is linked with an increased risk of mortality [2,3]. Hence, there is a requirement to investigate the multifaceted factors that contribute to mortality among individuals with CKD.
Several epidemiological investigations have identified a significant correlation between thyroid hormone abnormalities and CKD [4–7]. The underlying mechanisms remain unclear. However, some studies have proposed that kidney disease may lead to alterations in hypothalamic–pituitary–thyroid axis and changes in thyroid hormone uptake and action. Moreover, malnutrition, inflammation, metabolic acidosis, medications, and mineral deficiencies complicated with CKD might also lead to thyroid hormone abnormalities [6–12].
Thyroid hormone mainly targeted on cardiovascular system. It is associated with changes in blood pressure, cardiac contractility and output, vascular resistance, myocardial oxygen consumption, and electrophysiological conduction [13–15]. Thyroid function abnormalities also linked to an increased risk of mortality [6,11,12,16]. Thyroid hormone resistance (THR) is a manifestation of thyroid dysfunction characterized by decreased sensitivity to thyroid hormones, evident from elevated thyroid-stimulating hormone (TSH) and free thyroxine (fT4) levels. Recently, Laclaustra et al. have proposed a novel index for THR, Parametric Thyroid Feedback Quantile-based Index (PTFQI), which focuses on deviations in the average pituitary response to thyroid hormones (inhibition) within populations. This index has been shown to correlate with metabolic syndrome and diabetes [17]. Decreased sensitivity to thyroid hormone (high fT4 and high TSH) is associated with increased mortality in euthyroid individuals [18]. However, whether the association between THR and mortality in the individuals with CKD consistent with the general population is not known yet. The aim of this study was to investigate the association between the newly described PTFQI and mortality in CKD. We hypothesize that this concept may harmonize certain discrepancies identified in previous studies that have linked thyroid hormones with mortality endpoints.
Materials and methods
Data source
Data were acquired from the National Health and Nutrition Examination Survey (NHANES) conducted between 2007 and 2012. NHANES is commonly utilized as a prospective cohort study by linking its data to the National Death Index database. A detailed description of NHANES’ plan and operation has already been published [19]. Informed consent was obtained from all participants. The study protocol was reviewed and approved by the Ethics Review Board of the National Center for Health Statistics, ensuring ethical compliance (Continuation of Protocol no. 2005-06) [20]. As all NHANES data are publicly accessible and free of charge, there was no need for a separate agreement from the medical ethics committee. This study follows the guidelines of Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) [21].
Study population
In this study, participants aged 20 or older were included (n = 17,713). A total of 183 pregnant individuals (n = 183) and 12,716 participants without CKD were excluded from the study. Missing CKD (n = 1,749) or TSH (n = 1,386) data were also excluded. Ultimately, 1,634 participants participated in our study. Figure 1 illustrates the sampling process in detail.
Figure 1.
Study flowchart of the participants.
Parametric Thyroid Feedback Quantile-based Index (PTFQI)
Architect i2000SR electro-chemiluminescent immunoassays (Abbott Laboratories, Chicago, IL) were used to determine the serum levels of free triiodothyronine (fT3), fT4, thyroid stimulating hormone (TSH), thyroglobulin antibody (TgAb), and thyroid peroxidase antibody (TPOAb). Indices reflecting the sensitivity of thyroid hormones were calculated according to previous studies:
TSH index (TSHI) = ln TSH (mIU/L) + 0.1345 * fT4 (pmol/L).
Thyrotroph T4 Resistance index (TT4RI) = fT4 (pmol/L) * TSH (mIU/L). As for TSHI and TT4RI, a higher value indicates a lower central sensitivity to thyroid hormones.
PTFQI reflects the difference between the fT4 and reversed TSH quantiles. By using the standard normal cumulative distribution function, PTFQI can be calculated from fT4 (pmol/L) and TSH (mIU/L) as follows: φ((fT4 - μfT4)/σfT4) - (1 - φ((lnTSH - μlnTSH)/σlnTSH)) [17]. In our cohort, the mean (μ) and standard deviation (SD) (σ) for fT4, and the natural logarithm of TSH (ln TSH) were 10.813, 2.889, 0.503, and 0.878, respectively. The PTFQI values ranged from −1 to 1. In the pituitary, negative values indicated higher sensitivity to thyroid hormones, while positive values indicated lower sensitivity [17]. Based on the PTFQI percentiles, we classified the population as follows: PTFQI > P95; PTFQI > P75 and ≤ P95; PTFQI > P50 and ≤ P75; PTFQI > P25 and ≤ P50; PTFQI > P5 and ≤ P25 and PTFQI ≤ P5.
Ascertainment of mortality
Based on the National Death Index up to 31 December 2019, we assessed mortality from all causes, cardiovascular disease (CVD) and cancer. Following serum thyroid hormone measurements until death or end of follow-up, whichever occurred first, the follow-up period was calculated. Disease-specific deaths were classified using the International Classification of Diseases, Tenth Revision (ICD-10). According to ICD-10, cancer mortality was defined as C00-C97. Codes I00-09, I11, I13, I20-51, and I60-69 were identified as CVD deaths.
Chronic kidney disease
The Mobile Examination Center (MEC) collected urine albumin, urine creatinine, and serum creatinine. The first item was measured by solid-phase fluorescence immunoassay, while the last two by modified Jaffé kinetic tests. We utilized the CKD Epidemiology Collaboration (CKD-EPI) equation to estimate the glomerular filtration rate (eGFR) [22]. An eGFR below 60 mL/min/1.73 m2, or an urine albumin-to-creatinine ratio (UACR) above 30 mg/g, was defined as CKD [23].
Covariates
A questionnaire was used to collect information about participants’ age, gender, race/ethnicity, educational level (less than high school, high school or equivalent, college and above), drinking status (never-drinker, former-drinker, mild drinker, and heavy drinker), smoking status (never smoker, former smoker, and current smoker), family income and leisure-time physical activity (inactive, moderately active, vigorously active). Drinking status was classified as never (less than 12 drinks in a lifetime), former (did not drink last year but consumed more than or equal to12 drinks in a lifetime), light (less than or equal to 1 drink/d for women or less than or equal to 2 drinks/d for men over the past year), or heavy alcohol intake (more than 1 drinks/d for women or more than 2 drinks/d for men on average over the past year) [24,25]. According to the federal poverty line ratio, family incomes were categorized as below 1.3, between 1.3, and 3.5, and above 3.5 [26]. Serum total cholesterol (TC), serum uric acid, and glycohemoglobin (HbA1c) were the laboratory analysis covariates. In accordance with the NHANES anthropometric procedures, body weight and height were measured by trained health technicians. The body mass index (BMI) was calculated by dividing the weight in kilograms by the height in meters squared. Hypertensive individuals met any of the following criteria: antihypertensive medications currently taken, a self-reported diagnosis of hypertension by a doctor, systolic blood pressure ≥ 140 mmHg, or diastolic blood pressure ≥ 90 mmHg [27]. Diabetes was defined as fasting plasma glucose (FPG) level ≥ 7.0 mmol/L, 2-h plasma glucose level ≥ 11.1 mmol/L, HbA1c level ≥ 6.5%, self-reported physician diagnosis or currently taking diabetic medications or insulin [28]. Individual interviews were conducted utilizing physician diagnoses as well as standardized medical status questionnaires to assess CVD. The participants were asked, ‘Has a doctor or other health professional ever told you that you have coronary heart disease (CHD)/congestive heart failure (CHF)/heart attack/angina/stroke’? CVD patients were defined as those who answered ‘yes’ to any of the above questions.
Statistical analyses
Analyses were conducted using R 4.1.3. Characteristics of participants are presented as weighted means or percentages with standard deviation (SDs). The chi-squared test was used for categorical variables and ANOVA for continuous variables. The association between PTFQI and mortality was estimated using multivariate cox regression models. We adjusted for gender, age, race/ethnicity, educational level, drinking status, smoking status, leisure time physical activity, family income level, BMI, TC, HbA1c, uric acid, hypertension status, diabetes status, eGFR, and TPOAb in the fully adjusted model. A restricted cubic spline regression model was used to investigate the dose-response or linear relationship between PTFQI and mortality. We conducted stratified and interaction analyses to investigate potential differences among various subgroups defined by gender, age, race/ethnicity, obesity, diabetes, and hypertension status. Additionally, we performed sensitivity analyses: (1) Participants who died within 2 years of follow-up were excluded. (2) We excluded participants with thyroid disease histories. (3) We further adjusted for CVD and cancer histories in cox regressions. Statistical significance was defined as having a two-sided p value less than 0.05.
Results
Demographic and clinical characteristics of the study
We enrolled 1634 adults with CKD in the current study (mean age, 61.16 years; 41.5% males). During a median follow-up of 8.72 years, a total of 663 deaths were recorded, including 181 from CVD and 139 from cancer. TSH levels ranged from 0.006 to 66.768 mUI/mL, while fT4 levels varied from 2.60 to 61.90 pmol/L. Based on the PTFQI distribution, the following percentiles were selected: −0.52 (P5), −0.21 (P25), 0 (P50), 0.19 (P75), and 0.48 (P95). Table 1 presents demographic and clinical characteristics of study participants stratified by PTFQI categories. As anticipated, in the group with the highest sensitivity to fT4 (PTFQI percentile < 5), both fT4 and TSH levels were at the lowest range of their respective distributions and increased with rising PTFQI values, reaching their peak levels in the group with the highest tolerance to fT4 (PTFQ1 percentile ≥ 95). Participants in the category with the lowest sensitivity to fT4 (PTFQI percentile ≥ 95) tended to be older, predominantly non-Hispanic white, and had higher proportions of individuals who never smoked or drank alcohol, and who had diabetes. Additionally, these participants exhibited lower eGFR and fT3 levels and higher TgAb levels.
Table 1.
Baseline characteristics of the study sample according to PTFQI categories.
| Parametric Thyroid Feedback Quantile-based Index (PTFQI) percentile |
||||||||
|---|---|---|---|---|---|---|---|---|
| Variables | Total | p < 5 | p5–25 | p25–50 | p50–75 | p75–95 | p > 95 | p Value |
| Number | 1634 | 82 | 327 | 408 | 408 | 327 | 82 | |
| Sex (Male, N%) | 41.50(0.02) | 33.12(6.22) | 47.24(3.88) | 41.54(3.10) | 39.97(3.00) | 39.80(3.78) | 41.93(5.80) | 0.45 |
| Age ≥ 60 years (N%) | 59.46(0.03) | 33.34(6.15) | 53.99(3.47) | 44.77(3.11) | 69.23(3.31) | 70.06(2.77) | 80.12(8.11) | < 0.0001 |
| Race (N%) | 0.03 | |||||||
| Non-Hispanic White | 70.91(0.05) | 58.46(6.88) | 66.34(4.22) | 67.10(3.53) | 72.72(3.11) | 78.83(3.47) | 78.48(4.53) | |
| Non-Hispanic Black | 12.62(0.01) | 15.71(3.47) | 16.81(3.09) | 16.36(2.08) | 10.27(1.93) | 7.85(1.92) | 5.89(2.19) | |
| Mexican American | 7.03(0.01) | 12.31(3.55) | 8.14(1.55) | 6.25(1.28) | 6.41(1.38) | 6.01(1.31) | 8.34(3.17) | |
| Other Hispanic | 4.73(0.01) | 7.10(2.11) | 5.58(1.69) | 4.96(1.11) | 5.07(1.22) | 3.06(0.92) | 3.19(1.82) | |
| Other race | 4.71(0.01) | 6.43(4.00) | 3.13(0.99) | 5.33(1.86) | 5.53(1.54) | 4.24(1.34) | 4.10(1.77) | |
| Education (N%) | 0.03 | |||||||
| Less than high school | 13.05(0.01) | 15.90(6.05) | 9.52(1.64) | 13.20(1.98) | 14.85(1.53) | 14.27(1.88) | 9.34(2.96) | |
| High school or equivalent | 41.71(0.04) | 45.06(6.45) | 47.49(3.69) | 42.42(3.27) | 36.38(2.71) | 36.57(4.68) | 62.04(6.52) | |
| College or above | 45.11(0.03) | 39.04(7.05) | 42.98(3.74) | 44.38(3.37) | 48.76(2.70) | 49.16(4.84) | 28.62(6.31) | |
| Smoking status (N%) | 0.002 | |||||||
| Never | 50.25(0.03) | 40.57(5.73) | 41.15(3.47) | 57.50(3.34) | 49.63(3.69) | 52.93(2.82) | 53.31(7.54) | |
| Former | 31.64(0.02) | 35.00(6.11) | 29.71(2.35) | 28.88(2.65) | 35.25(2.77) | 30.39(2.83) | 37.49(7.58) | |
| Current | 18.08(0.01) | 24.43(6.57) | 29.14(3.47) | 13.61(2.17) | 15.11(2.24) | 16.68(2.93) | 9.20(3.20) | |
| Drinking status (N%) | 0.03 | |||||||
| Never | 17.14(0.02) | 14.02(5.82) | 12.74(1.75) | 19.26(2.20) | 20.03(2.27) | 21.00(3.46) | 27.50(7.67) | |
| Former | 24.39(0.02) | 14.82(5.62) | 28.90(3.85) | 24.54(2.64) | 26.26(4.13) | 28.85(2.99) | 29.47(6.35) | |
| Mild | 30.33(0.03) | 25.55(6.78) | 33.92(3.60) | 31.70(3.28) | 35.68(3.71) | 31.97(3.70) | 33.49(7.25) | |
| Heavy | 20.13(0.02) | 45.60(6.48) | 24.45(2.99) | 24.51(3.29) | 18.03(2.62) | 18.19(3.05) | 9.54(4.08) | |
| Family income-to-poverty ratio (N%) | 0.55 | |||||||
| <1.3 | 24.12(0.01) | 16.37(4.44) | 29.77(3.82) | 25.53(3.00) | 28.87(2.69) | 25.60(2.60) | 18.79(5.69) | |
| 1.3–3.5 | 39.44(0.03) | 46.56(7.20) | 38.89(3.43) | 43.90(3.31) | 43.90(3.90) | 42.08(3.38) | 55.33(9.18) | |
| >3.5 | 27.80(0.03) | 37.08(8.79) | 31.34(3.66) | 30.57(3.83) | 27.24(3.32) | 32.33(3.44) | 25.88(7.58) | |
| Physical activity (N%) | 0.37 | |||||||
| Inactive | 61.65(0.04) | 57.93(6.91) | 61.97(3.30) | 57.66(3.06) | 62.44(3.03) | 64.54(3.04) | 68.02(6.95) | |
| Moderately active | 27.32(0.02) | 25.93(6.42) | 23.08(3.05) | 30.90(2.59) | 27.68(2.77) | 27.16(3.23) | 26.96(6.70) | |
| Vigorous active | 11.03(0.01) | 16.14(4.82) | 14.95(2.62) | 11.44(1.97) | 9.88(2.13) | 8.30(2.78) | 5.02(3.55) | |
| Diabetes (yes, N %) | 34.49(0.02) | 23.14(6.24) | 27.87(2.82) | 35.42(2.83) | 36.24(2.70) | 37.38(4.44) | 48.08(6.56) | 0.03 |
| Hypertension (yes, N%) | 63.53(0.03) | 58.48(7.66) | 58.93(3.22) | 62.13(2.65) | 65.60(3.24) | 64.42(2.96) | 80.98(4.14) | 0.08 |
| CVD (yes, N%) | 23.76(0.02) | 16.87(4.90) | 23.55(2.65) | 14.11(2.26) | 26.16(2.50) | 29.47(2.97) | 34.81(7.27) | < 0.001 |
| All-cause mortality (yes, N%) | 36.36(0.02) | 23.22(5.66) | 32.42(3.05) | 29.51(2.99) | 39.16(2.80) | 42.00(2.98) | 63.04(8.12) | < 0.001 |
| CVD mortality (yes, N%) | 10.00(0.01) | 3.94(1.78) | 7.12(1.60) | 7.63(1.97) | 11.83(2.06) | 10.52(1.45) | 29.25(6.16) | < 0.0001 |
| Malignant mortality (yes, N%) | 7.54(0.01) | 2.35(1.00) | 7.49(1.55) | 7.34(1.58) | 7.71(1.73) | 9.11(1.97) | 6.69(3.43) | 0.58 |
| Age (years) | 61.16 ± 0.60 | 50.63 ± 2.86 | 56.68 ± 1.17 | 58.94 ± 1.25 | 64.06 ± 1.40 | 65.15 ± 0.80 | 70.15 ± 2.39 | < 0.0001 |
| BMI (kg/m2) | 29.69 ± 0.25 | 27.66 ± 0.98 | 29.25 ± 0.49 | 30.18 ± 0.56 | 29.98 ± 0.53 | 29.73 ± 0.38 | 29.64 ± 1.62 | 0.27 |
| HbA1C (%) | 6.12 ± 0.06 | 5.85 ± 0.14 | 5.99 ± 0.08 | 6.21 ± 0.10 | 6.17 ± 0.08 | 6.09 ± 0.06 | 6.39 ± 0.28 | 0.06 |
| TC (mmol/L) | 5.03 ± 0.05 | 5.23 ± 0.13 | 5.07 ± 0.07 | 5.04 ± 0.09 | 4.98 ± 0.08 | 5.07 ± 0.11 | 4.69 ± 0.18 | 0.14 |
| Uric acid (umol/L) | 354.85 ± 3.38 | 334.97 ± 13.17 | 348.14 ± 6.59 | 352.16 ± 6.49 | 356.29 ± 5.56 | 359.97 ± 5.07 | 388.60 ± 20.04 | 0.33 |
| eGFR (mL/min/1.73 m2) | 72.55 ± 0.96 | 88.52 ± 4.92 | 79.10 ± 1.72 | 76.19 ± 1.86 | 68.88 ± 1.96 | 65.42 ± 1.97 | 59.54 ± 3.51 | < 0.0001 |
| UACR(mg/g) | 200.33 ± 21.64 | 120.07 ± 25.23 | 191.00 ± 35.96 | 171.52 ± 34.72 | 175.89 ± 31.80 | 267.27 ± 72.68 | 293.19 ± 193.97 | 0.12 |
| SBP (mmHg) | 133.29 ± 0.56 | 130.59 ± 4.34 | 132.55 ± 1.16 | 133.57 ± 1.40 | 133.42 ± 1.25 | 133.62 ± 1.22 | 135.84 ± 2.51 | 0.81 |
| DBP (mmHg) | 68.87 ± 0.58 | 72.98 ± 2.16 | 71.67 ± 1.08 | 69.41 ± 0.89 | 67.01 ± 1.00 | 67.84 ± 1.22 | 63.78 ± 1.91 | 0.002 |
| fT4 (pmol/L) | 10.87 ± 0.10 | 8.47 ± 0.11 | 9.41 ± 0.09 | 10.21 ± 0.09 | 11.70 ± 0.24 | 11.94 ± 0.12 | 14.04 ± 0.29 | < 0.0001 |
| fT3 (pg/mL) | 3.03 ± 0.01 | 3.09 ± 0.05 | 3.02 ± 0.03 | 3.10 ± 0.03 | 3.05 ± 0.05 | 2.96 ± 0.03 | 2.84 ± 0.06 | < 0.001 |
| TSH (mIU/mL) | 2.26 ± 0.06 | 0.71 ± 0.03 | 1.18 ± 0.03 | 1.73 ± 0.06 | 2.82 ± 0.16 | 3.08 ± 0.15 | 4.63 ± 0.35 | < 0.0001 |
| TPOAb (IU/mL) | 25.25 ± 2.73 | 10.03 ± 3.84 | 15.38 ± 4.49 | 24.43 ± 7.23 | 25.74 ± 4.55 | 36.42 ± 9.54 | 35.69 ± 13.82 | 0.06 |
| TgAb (IU/mL) | 16.77 ± 3.62 | 9.72 ± 8.25 | 8.09 ± 3.97 | 3.15 ± 1.95 | 25.13 ± 8.49 | 22.90 ± 8.11 | 55.60 ± 25.91 | 0.02 |
| PTFQI | −0.01 ± 0.01 | −0.60 ± 0.01 | −0.34 ± 0.01 | −0.10 ± 0.00 | 0.08 ± 0.00 | 0.31 ± 0.01 | 0.64 ± 0.01 | < 0.0001 |
| TT4RI | 22.94 ± 0.69 | 5.82 ± 0.23 | 10.50 ± 0.27 | 16.26 ± 0.35 | 25.28 ± 1.12 | 34.76 ± 1.46 | 63.38 ± 4.60 | < 0.0001 |
| TSHI | 1.95 ± 0.03 | 0.66 ± 0.06 | 1.32 ± 0.03 | 1.76 ± 0.03 | 2.11 ± 0.03 | 2.60 ± 0.02 | 3.31 ± 0.06 | < 0.0001 |
Data are presented as means ± (SE) for continuous measures and percentage (SE) for categorical measures.
BMI: body mass index; CVD: cardiovascular disease; WC: waist circumference; TC: total cholesterol; eGFR: estimate glomerular filtration rate; fT4: free thyroxine; fT3: free triiodothyronine; HbA1c: glycohemoglobin; PTFQI: Parametric Thyroid Feedback Quantile-based Index; TSH: thyroid-stimulating hormone; TPOAb: thyroid peroxidase antibody; TgAb: thyroglobulin antibody; TT4RI: Thyrotroph T4 Resistance index; TSHI: TSH index; SD: standard deviation; UACR: urine albumin-to-creatinine ratio
Association between thyroid function and mortality
Figure 2 illustrates the dose-response association between the PTFQI and both all-cause and CVD mortality. Following multivariate adjustment, a non-linear relationship was observed: per-SD increase in PTFQI was linked to an 18% elevated risk of all-cause mortality and a 31% increased risk of CVD mortality (Table 2). No significant association was found between PTFQI and cancer mortality (Table 2). TT4RI was positively correlated with all-cause and CVD mortality. However, this association was no longer observed after adjustments were made for demographic characteristics and other confounders (Table 2). TSHI demonstrated a positive association with all-cause mortality exclusively in the unadjusted model (Table 2). Additionally, fT3 was inversely associated with all-cause mortality and CVD mortality (Supplementary Table 5). No significant association was found between TgAb and mortality (Supplementary Table 5).
Figure 2.
Restricted cubic spline fitting for the association between PTFQI with all-cause and CVD mortality.
Table 2.
Cox regressions of all-cause and cause-specific mortality for per SD increase of PTFQI, 1 unit TSHI and 1 unit TT4RI among participants with CKD in NHANES 2007–2012.
| Model 1 | p | Model 2 | p | Model 3 | p | Model 4 | p | |
|---|---|---|---|---|---|---|---|---|
| All-cause mortality | ||||||||
| PTFQI (+1SD) | 1.34 [1.23, 1.47] | <0.001 | 1.16 [1.05, 1.29] | 0.004 | 1.20 [1.08, 1.34] | 0.001 | 1.18 [1.05, 1.32] | 0.004 |
| TSHI (+1 unit) | 1.29 [1.13, 1.47] | <0.001 | 1.10 [0.96, 1.26] | 0.18 | 1.11 [0.96, 1.29] | 0.146 | 1.10 [0.94, 1.28] | 0.234 |
| TT4RI (+1 unit) | 1.01 [1.00, 1.01] | <0.001 | 1.00 [1.00, 1.01] | 0.021 | 1.00 [1.00, 1.01] | 0.198 | 1.00 [1.00, 1.01] | 0.215 |
| CVD mortality | ||||||||
| PTFQI (+1SD) | 1.59 [1.31, 1.94] | <0.001 | 1.36 [1.09, 1.70] | 0.006 | 1.39 [1.12, 1.73] | 0.003 | 1.31 [1.07, 1.61] | 0.01 |
| TSHI (+1 unit) | 1.32 [0.96, 1.82] | 0.091 | 1.10 [0.79, 1.52] | 0.577 | 1.14 [0.82, 1.58] | 0.45 | 1.06 [0.74, 1.50] | 0.759 |
| TT4RI (+1 unit) | 1.01 [1.00, 1.01] | 0.003 | 1.00 [1.00, 1.01] | 0.228 | 1.00 [1.00, 1.01] | 0.49 | 1.00 [0.99, 1.01] | 0.757 |
| Malignant mortality | ||||||||
| PTFQI (+1SD) | 1.22 [0.99, 1.51] | 0.063 | 1.09 [0.85, 1.39] | 0.497 | 1.13 [0.87, 1.47] | 0.37 | 1.19 [0.91, 1.56] | 0.203 |
| TSHI (+1 unit) | 1.15 [0.87, 1.52] | 0.329 | 1.01 [0.76, 1.35] | 0.929 | 1.01 [0.75, 1.35] | 0.96 | 1.06 [0.75, 1.50] | 0.74 |
| TT4RI (+1 unit) | 1.00 [1.00, 1.01] | 0.709 | 1.00 [0.99, 1.00] | 0.488 | 1.00 [0.99, 1.00] | 0.26 | 1.00 [0.99, 1.01] | 0.466 |
HRs are estimated with cox regression models for the increase of 0.30 units PTFQI (1 SD), 1 unit TSHI and 1 unit TT4RI. Indices enter the models as continuous variables. Model 1: crude model; Model 2: adjusted for age and sex; Model 3: adjusted for age, sex, race, smoking status, drinking status, family income and physical activity; Model 4: adjusted for age, sex, race, smoking status, drinking status, family income, physical activity, BMI, HbA1c, TC, UA, diabetes, hypertension, eGFR and TPOAb.
Figure 3 shows the Kaplan–Meier curves for all-cause and CVD mortality stratified by PTFQI groups. Individuals with the highest sensitivity to fT4 (PTFQI ≤ P5) had a lower risk of all-cause mortality and CVD mortality compared to those with the lowest sensitivity to fT4 (PTFQI > P95). Table 3 presents the hazard ratios (HRs) for mortality among participants categorized by PTFQI levels. After multivariable adjustment, the relationship between the variables remained statistically significant. Compared with the group the highest sensitivity to fT4 group (PTFQI ≤ P5, reference category), the relative risks of all-cause mortality for the categories PTFQI > P5 and ≤ P25; > P25 and ≤ P50; > P50 and ≤ P75; > P75 and ≤ P95; and > P95 were as follows: 0.91 [0.47, 1.73], 0.94 [0.48, 1.86], 0.98 [0.51, 1.88], 1.08 [0.53, 2.21], and 2.12 [1.10, 4.09], respectively (P for trend 0.026). Similarly, with the highest sensitivity to fT4 (PTFQI ≤ P5) as the reference, the fully adjusted HRs (95% CI) for CVD mortality across the PTFQI categories were 1.24 [0.43, 3.56], 1.45 [0.39, 5.41], 1.61 [0.51, 5.08], 1.39 [0.42, 4.57], and 5.14 [1.81, 14.60], respectively (p for trend 0.018).
Figure 3.
Kaplan–Meier curves for mortality incidence according to PTFQI categories.
Table 3.
Cox regressions of PTFQI categories to all-cause and CVD mortality among participants with CKD in NHANES 2007–2012.
| Parametric Thyroid Feedback Quantile-based Index (PTFQI) percentile |
|||||||
|---|---|---|---|---|---|---|---|
| ≤p5 | >p5 and ≤ p25 | >p25 and ≤ p50 | >p50 and ≤ p75 | >p75 and ≤ p95 | >p95 | P for trend | |
| All-cause mortality | |||||||
| Model 1 | 1 | 1.47 [0.80, 2.72] | 1.34 [0.72, 2.49] | 1.91 [1.10, 3.32] | 2.10 [1.16, 3.80] | 4.13 [2.20, 7.76] | <0.0001 |
| Model 2 | 1 | 1.05 [0.58, 1.87] | 0.89 [0.50, 1.59] | 1.02 [0.58, 1.80] | 1.13 [0.62, 2.05] | 2.10 [1.18, 3.72] | 0.016 |
| Model 3 | 1 | 0.93 [0.50, 1.73] | 0.99 [0.52, 1.88] | 1.03 [0.56, 1.90] | 1.19 [0.60, 2.37] | 2.25 [1.25, 4.03] | 0.006 |
| Model 4 | 1 | 0.91 [0.47, 1.73] | 0.94 [0.48, 1.86] | 0.98 [0.51, 1.88] | 1.08 [0.53, 2.21] | 2.12 [1.10, 4.09] | 0.026 |
| CVD mortality | |||||||
| Model 1 | 1 | 1.91 [0.79, 4.60] | 2.05 [0.64, 6.55] | 3.42 [1.28, 9.11] | 3.10 [1.13, 8.56] | 11.27 [4.03, 31.50] | <0.001 |
| Model 2 | 1 | 1.25 [0.51, 3.07] | 1.25 [0.39, 4.04] | 1.61 [0.57, 4.60] | 1.49 [0.53, 4.20] | 5.01 [1.84, 13.64] | 0.01 |
| Model 3 | 1 | 1.23 [0.45, 3.34] | 1.41 [0.40, 4.97] | 1.73 [0.57, 5.23] | 1.57 [0.51, 4.84] | 5.72 [2.10, 15.59] | 0.005 |
| Model 4 | 1 | 1.24 [0.43, 3.56] | 1.45 [0.39, 5.41] | 1.61 [0.51, 5.08] | 1.39 [0.42, 4.57] | 5.14 [1.81, 14.60] | 0.018 |
Model 1: crude model; Model 2: adjusted for age and sex; Model 3: adjusted for age, sex, race, smoking status, drinking status, family income and physical activity; Model 4: adjusted for age, sex, race, smoking status, drinking status, family income, physical activity, BMI, HbA1c, TC, UA, diabetes, eGFR, hypertension and TPOAb.
Subgroup and sensitivity analysis
Analysis stratified by age, gender, ethnicity, BMI, diabetes, and hypertension showed consistent results (Supplementary Figure 1). There was no significant interaction between PTFQI levels and these stratifying variables. Sensitivity analyses revealed generally robust results when participants who died within 2 years of follow-up were excluded (Supplementary Table 1), excluding participants with thyroid disease histories (Supplementary Table 2), excluding participants with cancer (Supplementary Table 3), further adjusting for UACR or CVD and cancer histories (model 6 and 7 in Supplementary Table 4).
Discussion
In this cohort study of United States individuals with CKD, followed for a median duration of 8.72 years, we observed that elevated levels of PTFQI were associated with heightened risk of all-cause and CVD mortality among patients. Our findings suggested an dose–response relationship between PTFQI and mortality, independent of established risk factors. Furthermore, sensitivity and stratified analyses corroborated our findings’ robustness.
Physiologically, TSH and thyroid hormones are inversely related due to the negative feedback loop. The expected relationship between TSH, fT4 and mortality has not been observed in previous studies [21,29–33]. Several studies proved that higher levels of fT4 were associated with an increased risk of mortality [30,31,34]. To illustrate, a prospective cohort study demonstrated a linear increase in the risk of mortality from atherosclerotic CVD with rising levels of fT4 (HR, 2.41; 95%CI, 1.68–3.47) [34]. High fT4 concentrations were significantly related to all-cause and CVD mortality in patients at intermediate to high risk of CVD [30]. In the NHANES data, higher fT4 was linked to increased all-cause mortality (HR, 1.15; 95% CI, 1.09–1.22), cardiovascular mortality (HR, 1.18; 95% CI, 1.01–1.39) [31]. Interestingly, other studies have proved that TSH elevation is associated with increased mortality in CKD patients [6,29]. A prospective cohort study including 227,422 United States veterans with non-dialysis-dependent CKD found a U-shaped relationship between TSH and the increased mortality [33]. Moreover, similar findings were reported in euthyroid and general individuals. Inoue et al. reported that among United States adults, high-normal TSH levels were associated with an increased risk of mortality compared to mid-normal TSH levels [35]. Based on negative feedback loop, we anticipated an inverse relationship between TSH and mortality, akin to that of fT4. However, the expected inverse correlation between TSH and clinical outcomes is not observed. RTH may provide a potential explanation for these discrepancies. In individuals with RTH, an inherited autosomal recessive trait, elevated levels of thyroid hormones coexist with high levels of TSH [16,36,37]. However, CKD patients may develop reversible acquired resistance to thyroid hormones [38]. PTFQI is a new index reflecting pituitary-thyroid status, considered to be more stable than the TT4RI and TSHI [17]. Previous studies have demonstrated an association between reduced sensitivity to thyroid hormone and diabetes, metabolic syndrome, obesity, ischemic heart disease, atrial fibrillation, and hypertension [17,39,40]. Compared to TSH or fT4 alone, PTFQI demonstrates a stronger correlation with renal function [41]. Given the significant findings of PTFQI in previous research and the intricate dynamics within the hypothalamic–pituitary-thyroid (HPT) axis, we initiated an investigation into the unresolved discussion concerning the association between thyroid function and mortality, focusing on thyroid hormone sensitivity through the utilization of composite indices.
We utilized a nationally representative sample of patients with CKD in the United States, taking into account other potential confounding variables. A dose–response relationship was observed between PTFQI levels and both all-cause and CVD mortality. Higher levels of PTFQI are associated with increased risk of all-cause and CVD mortality in patients with CKD. Participants with the highest sensitivity to fT4 exhibited a lower risk of all-cause mortality (HR: 4.13; 95% CI: 2.20, 7.76) and CVD mortality (HR: 11.27; 95% CI: 4.03, 31.50) compared to those with the lowest sensitivity to fT4. Our findings indicate that elevated levels of PTFQI are associated with an increased incidence of adverse cardiovascular outcomes, characterized by higher rates of hypertension and diabetes, compared to baseline. Consequently, while we adjusted for these cardiovascular risk factors in our multivariate analysis, residual confounding cannot be ruled out. Given the established effects of age, gender, race, obesity, diabetes, and hypertension on mortality, we conducted a stratified analysis. The findings among subgroup populations were largely in alignment with those of the broader population.
Elevated TSHI and TT4RI levels were linked to higher risks of all-cause and CVD mortality in CKD. After controlling for additional potential confounding variables, the association between TSHI, TT4RI with all-cause and CVD mortality were no longer present. However, the strong correlation between PTFQI and mortality remains. Hence, in our study, using PTFQI showed a stronger link between thyroid function and all-cause and CVD mortality.
In our study, there was no significant association between THR and cancer mortality. Previous research on the relationship between cancer and thyroid function has reported inconsistent findings across different types of tumors. For instance, Ben et al. demonstrated that both untreated hypothyroidism and hyperthyroidism were linked with an elevated risk of colorectal cancer [42]. However, another study indicated no associations between TSH, fT4 and the risk of colorectal, breast, or lung cancer, yet it revealed that higher fT4 and lower TSH were linked to a higher risk of prostate cancer [43]. A Mendelian randomization study has found a causal association between thyroid dysfunction and an elevated risk of breast cancer [44]. Given that our study collected data on overall cancer mortality, we were unable to examine the association with specific cancer mortality causes, and therefore, we could not further investigate these hypotheses.
To elucidate the potential mechanisms underlying our findings, we propose that PTFQI reflects pituitary thyroidal status, while other peripheral tissues may exhibit differential sensitivity to thyroid hormone. If sensitivity in non-pituitary tissues remains normal, a shift in the individual set point of the HPT axis may result in serum fT4 levels being perceived as appropriate by the pituitary but elevated for other organs, including the cardiovascular system [45]. Patients with resistance to thyroid hormone (RTH) exhibit persistently elevated circulating fT4 levels with non-suppressed serum TSH. These individuals often show signs of thyrotoxicosis in tissues such as the heart, leading to increased myocardial contractility, enhanced cardiac output, widened pulse pressure, tachycardia, and reduced total peripheral resistance [13,45–49]. Excess thyroid hormone has also been demonstrated to increase reactive oxygen species production [50,51], upregulate adhesion molecules [52], and promote thrombogenesis [53]. Enhanced myocardial contractility is mediated by upregulation of myosin heavy chain isoforms, improved calcium handling, and stimulation of the beta-adrenergic system [13,46,47]. These changes may account for the observed adverse cardiovascular outcomes.
Our study has several limitations. First, the observational cohort design precludes us from excluding residual confounding. Second, only baseline thyroid function data were available, with no data on changes in thyroid function over time. Third, as the study was conducted on a representative sample from the United States, caution is warranted when generalizing the findings to other populations. Finally, our sample size is relatively small, and the number of events available for analysis was limited. To enhance the robustness of our findings, it is imperative that further research replicates our findings in larger and more diverse populations. Conducting longitudinal study to discern PTFQI’s temporal patterns and clinical significance is also crucial. Such endeavors will corroborate our results and possibly reveal new intervention strategies for CKD.
Conclusions
In conclusion, evidence from a nationally representative cohort indicates that THR is an independent risk factor for all-cause and CVD mortality in individuals with CKD. The newly described PTFQI more reliably reflects the association between THR and mortality compared to other indices (TSHI and TT4RI). Further research is warranted to validate these findings in larger populations of CKD individuals.
Supplementary Material
Acknowledgements
We are grateful to the participants and to the people involved in the National Health and Nutrition Examination Survey study. Thanks to Zhang Jing (Second Department of Infectious Disease, Shanghai Fifth People’s Hospital, Fudan University) for his work on the NHANES database.
Funding Statement
This work was supported by grants from the National Natural Science Foundation of China (Nos. 82200820, 81970599 and, 82170737)), Guangzhou Science and Technology Project (202201011483), Key Laboratory of National Health Commission, and Key Laboratory of Nephrology, Guangdong Province, Guangzhou, China (2020B1212060028), and 5010 Clinical Program of Sun Yat-Sen University (No. 2017007) and National Key Research and Development Project of China (No. 2021YFC2501302).
Ethical approval
NHANES was approved by the NCHS Research Ethics Review Board (https://www.cdc.gov/nchs/nhanes/irba98.htm).
Authors’ contributions
Chan Liu and Xiaoxiao Zhu conceived and designed the study; Chan Liu and Dingding Wang extracted and analyzed the data; Chan Liu wrote the manuscript; Naya Huang and Wei Chen helped review the manuscript. All authors interpreted the results and approved the submitted version.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
NHANES data and material availability can be obtained from the CDC NHANES website (https://wwwn.cdc.gov/nchs/nhanes).
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
NHANES data and material availability can be obtained from the CDC NHANES website (https://wwwn.cdc.gov/nchs/nhanes).