Abstract
Chronic kidney disease (CKD) and hypothyroidism are prevalent chronic conditions with a generally believed correlation between them. However, large-scale population studies and investigations into causation are lacking. This study analyzed CKD and thyroid function data from the National Health and Nutrition Examination Survey database spanning 2007 to 2012 using multiple regression analyses to examine the correlation between CKD and hypothyroidism. Bidirectional Mendelian randomization analysis was employed to investigate the causal association between the two conditions. As CKD stages deteriorated, there was a significant decrease in total triiodothyronine (TT3) and free triiodothyronine (P < .05). However, no significant decrease was observed in total thyroxine and free thyroxine. Notably, there was a significant increase in thyroid-stimulating hormone levels (P < .05). However, no significant changes were observed in thyroglobulin, thyroglobulin antibody, and thyroid peroxidase antibody levels. A causal relationship between CKD and reduced thyroid function was observed (odds ratio [OR] = 1.0041, 95% confidence interval [CI]: 1.0007–1.0075, P = .0186). Conversely, reverse causality was not statistically significant (OR = 2.540, 95%CI: 0.8680–4.8603, P = .1014). As CKD progressed, the risk of hypothyroidism increased. A causal correlation was observed between CKD and reduced thyroid function. Chronic kidney disease (CKD) and hypothyroidism are prevalent chronic conditions with a generally believed correlation between them. There is no large-scale population studies and the causation relationship between CKD and hypothyroidism are lacking. The finding of the causal relationship between CKD and hypothyroidism may be beneficial to the prevention of the disease and the prognosis of the patients.
Keywords: chronic kidney disease, hypothyroidism, Mendelian randomization
1. Introduction
As social economies advance and medical and health initiatives progress, there have been significant changes in the landscape of human diseases and mortality. Chronic noncommunicable diseases have emerged as the predominant global causes and contributors to mortality. In recent years, the increasing prevalence of chronic kidney disease (CKD) has become a significant worldwide public health concern.[1] Data from the 2017 Global Burden of Disease Study revealed that CKD affects approximately 9.1% of adults globally, leading to 1.2 million deaths and 35.8 million disabilities.[2] Between 1990 and 2017, there was a notable 29.3% increase in the prevalence of CKD across all age groups, with its ranking as a cause of death increasing from 17th to 12th place.[2] In addition, between 1990 and 2019, disability-adjusted life years attributed to CKD increased from 29th to 18th place.[3] Furthermore, CKD serves as a significant risk factor for cardiovascular and cerebrovascular diseases as well as mortality.[4,5] Due to its gradual onset, symptoms often manifest at an advanced stage of the disease, rendering traditional drug treatment ineffective. Kidney replacement therapy, although an option, imposes a substantial financial burden on families and society. Thus, it is advantageous to identify the relationship between CKD and other diseases and implement effective strategies to mitigate its prevalence, mortality rates, and economic impact.
Hypothyroidism, a common thyroid disorder, typically affects approximately 2% of the population in the United States and 2.25% in Europe on average.[6] Literature reports indicate that at least 10% of adults experience hypothyroidism,[7,8] with notable differences between men and women.[9] Thyroid hormones exert widespread influence throughout the body; thus, fluctuations in thyroid hormone levels can impact the function of tissues and organs.[9–11] The primary clinical indicators of hypothyroidism typically include fatigue, dry skin, and breathlessness, but with significant individual differences and poor specificity.[12,13] Currently, laboratory tests are the primary method for diagnosing hypothyroidism, with increased plasma thyroid-stimulating hormone (TSH) and decreased secretion of hormones (T4 and T3) being the diagnostic criteria.[14] However, serum T4 levels can be influenced by thyroid hormone replacement therapy. Consequently, T3 levels may be a more appropriate measure for diagnosing hypothyroidism.[15]
Although there is a prevailing understanding of potential interactions between the thyroid and kidney through various pathways, comprehensive analysis of population-based data, particularly the interaction between hypothyroidism and chronic renal function, remains limited.[16] Increasing evidence indicates that hypothyroidism correlates with a poorer prognosis and increased mortality risk among patients with CKD uremia.[16–18] The National Health and Nutrition Examination Survey (NHANES) is a comprehensive survey that covers diverse populations and health-related topics. This ongoing project employs scientific and rigorous statistical methods for data collection and analysis, ensuring the integrity and reliability of its findings. NHANES data have been extensively employed in numerous health and nutrition investigations.[1,19–22] Mendelian randomization is an approach that uses genetic variation to determine risk factors and causal effects.[23] Compared with randomized clinical trials, Mendelian randomization offers advantages in terms of trial cost control and trial duration.[24] Consequently, this study aims to use the NHANES database to investigate and examine the correlation between hypothyroidism and CKD. Simultaneously, the Mendelian randomization approach was employed to further validate the aforementioned research findings. It is expected that early intervention can slow disease progression and alleviate the medical burden.
2. Methods
2.1. Study population
This study analyzed data from NHANES 2007 to 2012. A detailed description of the data collection approaches can be found on the NHANES website (https://www.cdc.gov/nchs/nhanes/). Three data cycles (2007–2012) involving 6381 participants (53.0% males and 47.0% females) were analyzed. Participants lacking baseline data on eGFR and thyroid function index, as well as those undergoing dialysis or thyroid hormone replacement therapy, were excluded. Figure 1 shows the patient selection flowchart.
Figure 1.
Flowchart of patient selection.
2.2. eGFR and chronic kidney disease stages
Serum creatinine levels, age, race, and sex were extracted from the NHANES database. CKD-EPI was employed to compute eGFR for all patients and is reported in mL/minute/1.73 m2.[25] Non-CKD was defined as eGFR > 120 mL/minute/1.73 m2. CKD stages were defined based on eGFR.
2.3. Thyroid function index
Biochemical tests were used to measure thyroglobulin antibodies (TBA), free triiodothyronine (FT3), free thyroxine (FT4), thyroglobulin (TB), TSH, thyroid peroxidase antibodies (TPA), total triiodothyronine (TT3), and total thyroxine (TT4).[26] Blood samples were collected, processed, and frozen at−20°C before being sent to the Collaborative Laboratory Services for analysis. Comprehensive guidelines on specimen collection and processing are provided in the NHANES Laboratory Procedures Manual.
2.4. Covariates
All covariates were selected through single-factor analysis and previous studies.[27–30] In the personal interview, participants reporting at least 1 chronic disease (angina, congestive heart failure, coronary heart disease, type 2 diabetes mellitus, or stroke) were categorized as having a chronic disease. Based on their smoking habits, the participants were categorized into three groups: never smokers, current smokers, and former smokers. Never smokers were individuals who had either never smoked or had smoked fewer than 100 cigarettes in their lifetime. Current smokers were defined as those who had smoked at least 100 cigarettes and were still smoking, whereas former smokers were individuals who had smoked at least 100 cigarettes but had subsequently quit smoking. This categorization enabled researchers to investigate the correlation between smoking status and different health outcomes.[31] Drinkers were defined as individuals who consumed a minimum of 12 alcohols per year. A medical professional measured blood pressure 3 consecutive times using a sphygmomanometer, and the readings were then averaged. Hypertension was determined by either being informed by a health professional or having at least 3 measurements of systolic blood pressure≥140 mm Hg and/or diastolic blood pressure ≥90 mm Hg. Type 2 diabetes mellitus was defined based on either being diagnosed by a health professional or having HbA1c levels of ≥ 6.5%.
2.5. Genetic instrument selection
This study examined bidirectional 2-sample Mendelian randomization (TSMR). In stage I, “CKD” was considered as the exposure and “hypothyroidism” as the outcome for forward TSMR analysis. The primary genetic instruments for CKD were derived from a genome-wide association study involving over 100,000 individuals of European descent.[32,33] In stage II, reverse TSMR analysis was conducted with “Hypothyroidism” as the exposure and “CKD” as the outcome. A summary of data regarding genetic relationships with “Hypothyroidism,” involving 23,497 cases and 461,101 controls, was obtained from the genome-wide association study catalog database.[34] In the Mendelian randomization (MR) analysis, the steps for screening the single nucleotide polymorphisms (SNPs) proceeded as follows: First, SNPs associated with CKD were filtered out with a P < 5 × 10−6. Then, SNPs showing linkage disequilibrium were removed according to the criteria r2 < 0.001 and kb = 10,000. Subsequently, the F statistic was calculated for each SNP, and those with F values exceeding 10 were recognized as robust instrumental variables strongly associated with CKD.
2.6. Genetic summary data
To address the reverse causality, a bidirectional TSMR analysis was conducted.[35] In this analysis, “hypothyroidism” and “CKD” were examined as outcomes for stages I and II, respectively, without altering the source of the database.
2.7. Statistical analyses
Based on NHANES requirements, sample data were weighted to ensure representation of the general population in the United States. EmpowerStats statistical software (X&Y Solutions, Boston) and R software version 4.3.2 were used to perform all statistical analyses. We expressed continuous variables as mean ± standard deviation and minimum/maximum values, and categorical variables as percentages. Multiple regression analyses were used to examine the associations between CKD stages and TBA, FT3, FT4, TB, TSH, TPA, TT3, and TT4. In addition, subgroup analyses were conducted based on sex. Statistical significance was defined as P values <.05.
This study used the inverse variance weighted (IVW) approach in TSMR to estimate the causal effect between the 2 approaches.[36] To mitigate the influence of measurement errors among IVs on the results, this study employs the Q test approach to assess the heterogeneity among IVs across various statistical analysis approaches.[37] During the IV selection and inclusion process, some random errors are inevitable.[38] Sensitivity analysis was conducted using IVW, MR-Egger, weighted mode, simple mode, and weighted median methods.[39]
3. Results
3.1. Participant characteristics
This study included a total of 6381 participants, with a mean age of 48.91 ± 17.52 years. Table 1 shows the clinical characteristics of the study population. Compared with individuals without CKD, those in CKD 4/5 were more inclined to be older, of other Hispanic or non-Hispanic Black ethnicity, high school graduates/GED holders or equivalent, single/divorced/widowed/never married, with lower poverty income ratio, nondrinkers, former smokers, engaged in more other work or recreational activities, and had higher rates of diabetes and hypertension. Furthermore, the CKD 5 group exhibited lower levels of TBA, FT3, FT4, TT3, and TT4. However, this group demonstrated higher TB, TSH, and TPA levels (P < .05).
Table 1.
Weighted demographic characteristics.
| No CKD | CKD 1 | CKD 2 | CKD 3 | CKD 4 | CKD 5 | P value | |
|---|---|---|---|---|---|---|---|
| Age (yr, mean ± SD) | 31.62 ± 9.84 | 42.33 ± 13.13 | 55.69 ± 14.00 | 69.84 ± 10.92 | 73.87 ± 11.00 | 71.34 ± 7.54 | <.0001 |
| Gender (%) | <.0001 | ||||||
| Male | 57.51 | 49.67 | 54.19 | 43.01 | 36.88 | 53.03 | |
| Female | 42.49 | 50.33 | 45.81 | 56.99 | 63.12 | 46.97 | |
| Race/ethnicity (%) | <.0001 | ||||||
| Mexican American | 30.56 | 4.23 | 1.42 | 1.81 | 2.12 | 12.80 | |
| Other Hispanic | 7.15 | 5.68 | 3.24 | 3.35 | 76.53 | ||
| Non-Hispanic White | 40.33 | 73.63 | 82.76 | 81.53 | 19.45 | ||
| Non-Hispanic Black | 15.87 | 9.76 | 7.50 | 10.90 | 1.90 | 87.20 | |
| Other race | 6.09 | 6.71 | 5.07 | 2.40 | |||
| Education level (%) | <.0001 | ||||||
| Less than High school | 27.75 | 16.22 | 14.51 | 26.09 | 46.56 | 34.01 | |
| High school graduate/GED or equivalent | 22.91 | 22.20 | 24.70 | 30.11 | 21.14 | 65.99 | |
| College or above | 49.34 | 61.58 | 60.79 | 43.81 | 32.30 | ||
| Marital status (%) | <.0001 | ||||||
| Married/living as married | 54.85 | 65.54 | 67.51 | 63.22 | 46.11 | 34.38 | |
| Single/divorced/widowed/ never married | 45.15 | 34.46 | 32.49 | 36.78 | 53.89 | 65.62 | |
| PIR (%) | 2.27 ± 1.58 | 3.15 ± 1.63 | 3.32 ± 1.59 | 2.84 ± 1.54 | 2.31 ± 1.37 | 1.32 ± 0.71 | <.0001 |
| Body mass index (kg/m2) | 28.17 ± 7.11 | 28.58 ± 6.76 | 28.58 ± 5.71 | 29.32 ± 7.28 | 29.11 ± 6.67 | 27.24 ± 2.97 | .1362 |
| Drinking status (%) | <.0001 | ||||||
| No | 22.89 | 19.60 | 21.93 | 38.35 | 38.60 | 60.15 | |
| Yes | 77.11 | 80.40 | 78.07 | 61.65 | 61.40 | 39.85 | |
| Smoking status (%) | <.0001 | ||||||
| No | 58.04 | 51.83 | 52.63 | 49.98 | 34.07 | 60.15 | |
| Now | 28.03 | 25.27 | 16.59 | 10.05 | 15.03 | 39.85 | |
| Former | 13.93 | 22.91 | 30.77 | 39.97 | 50.89 | ||
| Work activity (%) | <.0001 | ||||||
| Vigorous | 27.55 | 23.46 | 18.78 | 11.20 | 1.31 | ||
| Moderate | 21.30 | 22.65 | 28.07 | 22.10 | 21.20 | ||
| Other | 51.16 | 53.89 | 53.15 | 66.70 | 77.50 | 100.00 | |
| Recreational activity (%) | <.0001 | ||||||
| Vigorous | 31.00 | 29.67 | 22.00 | 6.91 | 2.16 | ||
| Moderate | 24.55 | 27.77 | 33.00 | 27.90 | 9.76 | ||
| Other | 44.45 | 42.56 | 45.00 | 65.19 | 88.08 | 100.00 | <.0001 |
| Diabetes (%) | |||||||
| No | 93.54 | 91.21 | 87.67 | 74.48 | 59.68 | 34.38 | |
| Yes | 6.46 | 8.79 | 12.33 | 25.52 | 40.32 | 65.62 | |
| Hypertension (%) | <.0001 | ||||||
| No | 82.14 | 71.74 | 54.51 | 22.69 | 10.85 | 21.21 | |
| Yes | 17.86 | 28.26 | 45.59 | 77.31 | 89.15 | 78.79 | |
| Thyroglobulin antibodies (IU/mL) | 4.14 ± 45.71 | 6.82 ± 70.08 | 10.13 ± 82.36 | 10.84 ± 90.67 | 5.54 ± 35.96 | 2.28 ± 3.51 | .2992 |
| Triiodothyronine (T3), free (pg/mL) | 3.38 ± 0.81 | 3.22 ± 0.40 | 3.10 ± 0.33 | 2.94 ± 0.32 | 2.62 ± 0.27 | 2.48 ± 0.16 | <.0001 |
| Thyroxine, free (ng/dL) | 0.79 ± 0.18 | 0.78 ± 0.13 | 0.78 ± 0.13 | 0.82 ± 0.18 | 0.91 ± 0.24 | 0.72 ± 0.11 | <.0001 |
| Thyroglobulin (ng/mL) | 14.20 ± 24.65 | 14.84 ± 27.12 | 17.66 ± 33.20 | 20.32 ± 57.82 | 23.38 ± 23.39 | 62.97 ± 56.81 | .0002 |
| Thyroid-stimulating hormone (µIU/mL) | 1.66 ± 1.50 | 1.97 ± 3.63 | 2.13 ± 2.15 | 2.50 ± 3.78 | 2.83 ± 5.56 | 1.75 ± 0.79 | <.0001 |
| Thyroid peroxidase antibodies (IU/mL) | 12.73 ± 71.39 | 17.32 ± 83.46 | 22.33 ± 99.18 | 14.59 ± 77.27 | 36.04 ± 143.66 | 98.57 ± 135.63 | .0360 |
| Triiodothyronine (T3), total (ng/dL) | 121.19 ± 26.61 | 116.15 ± 24.75 | 110.39 ± 20.41 | 100.94 ± 20.77 | 89.04 ± 15.97 | 76.53 ± 9.49 | <.0001 |
| Thyroxine (T4), total (µg/dL) | 7.84 ± 1.71 | 7.71 ± 1.50 | 7.70 ± 1.50 | 7.99 ± 1.69 | 8.58 ± 1.74 | 7.44 ± 2.00 | .0006 |
Results in table: Mean + SD/N(%).
CKD = chronic kidney disease, PIR = poverty income ratio, SD = standard deviation, T3 = triiodothyronine, T4 = thyroxine.
3.2. Multiple regression analysis
Multiple regression analysis revealed a negative association between FT3 and TT3 levels and CKD stages, indicating a noticeable decrease in FT3 and TT3 levels as the disease progressed (P < .05). These associations persisted even after adjusting for covariates. However, TSH exhibited a positive relationship with CKD stages (P < .05), except for CKD5. FT4 exhibited a negative association with CKD stage 1, whereas FT4 and TT4 demonstrated a positive association with CKD stage 3 (P < .05). In addition, FT4 was positively associated with the CKD stage 4 (P < .05). Conversely, the levels of FT3 and TT3 demonstrated only slight progression as the disease progressed. No significant association was observed between TBA, TB, or TPA levels and the CKD stage. Table 2 shows the results of the multiple regression analysis.
Table 2.
Multiple regression analysis of the association of CKD stages with TBA, FT3, FT4, TB, TSH, TPA, TT3, TT4, and BMD.
| Variable | CKD stages | Nonadjusted | Adjust I | Adjust II | |||
|---|---|---|---|---|---|---|---|
| β | 95% CI | β | 95% CI | β | 95% CI | ||
| TBA | Non-CKD | Reference | – | – | – | – | – |
| CKD 1 | 3.40 | −1.17, 7.96 | 0.72 | −4.67, 6.11 | 0.96 | −4.72, 6.64 | |
| CKD 2 | 7.86** | 2.91, 12.81 | 3.34 | −3.46, 10.14 | 2.94 | −4.23, 10.12 | |
| CKD 3 | 3.60 | −3.91, 11.10 | −2.36 | −12.24, 7.51 | −0.72 | −11.14, 9.70 | |
| CKD 4 | 2.08 | −19.59, 23.75 | −3.51 | −27.53, 20.51 | −0.41 | −25.94, 25.12 | |
| CKD 5 | −1.99 | 65.57, 61.58 | −3.21 | −72.33, 65.91 | 0.04 | −67.30, 67.39 | |
| FT3 | Non-CKD | Reference | – | – | – | – | – |
| CKD 1 | −0.18*** | −0.21, −0.15 | −0.07*** | −0.11, −0.03 | −0.05** | −0.09, −0.01 | |
| CKD 2 | −0.32*** | −0.35, −0.29 | −0.12*** | −0.17, −0.07 | −0.10*** | −0.14, −0.05 | |
| CKD 3 | −0.50*** | −0.55, −0.45 | −0.22*** | −0.29, −0.15 | −0.18*** | −0.25, −0.11 | |
| CKD 4 | −0.73*** | −0.88, −0.58 | −0.45*** | −0.62, −0.28 | −0.46*** | −0.63, −0.30 | |
| CKD 5 | −0.93*** | −1.37, −0.49 | −0.63* | −1.12, −0.14 | −0.58* | −1.03, −0.14 | |
| FT4 | Non-CKD | Reference | – | – | – | – | – |
| CKD 1 | 0.02*** | −0.03¸ −0.01 | −0.02** | −0.03, 0 | −0.01* | −0.02, 0 | |
| CKD 2 | −0.01 | −0.02, 0 | −0.01 | −0.02, 0 | 0 | −0.02, 0.01 | |
| CKD 3 | 0.03*** | 0.01, 0.04 | 0.03* | 0.01, 0.05 | 0.03** | 0.01, 0.05 | |
| CKD 4 | 0.09*** | 0.04, 0.13 | 0.07** | 0.02, 0.12 | 0.07** | 0.02, 0.12 | |
| CKD 5 | −0.14* | −0.27, −0.01 | −0.10 | −0.25, 0.04 | −0.10 | −0.24, 0.03 | |
| TB | Non-CKD | Reference | – | – | – | – | – |
| CKD 1 | 2.12 | −1.56, 5.80 | −1.00 | −3.91, 1.90 | −0.58 | −3.79, 2.62 | |
| CKD 2 | 3.78 | −0.22, 7.77 | −2.31 | −5.98, 1.36 | −2.13 | −6.18, 1.93 | |
| CKD 3 | 15.83*** | 9.78, 21.88 | −1.18 | −6.50, 4.14 | 0.11 | −5.78, 5.99 | |
| CKD 4 | 12.37 | −5.16, 29.90 | 1.38 | −11.60, 14.37 | −2.5 | −16.94, 11.95 | |
| CKD 5 | 33.01 | −18.41, 84.44 | 27.56 | −9.81, 64.92 | 28.21 | −9.90, 66.31 | |
| TSH | Non-CKD | Reference | – | – | – | – | – |
| CKD 1 | 0.25** | 0.09, 0.41 | 0.21* | 0.02, 0.39 | 0.24* | 0.03, 0.45 | |
| CKD 2 | 0.52*** | 0.34, 0.69 | 0.35** | 0.11, 0.59 | 0.38** | 0.11, 0.65 | |
| CKD 3 | 0.82*** | 0.56, 1.09 | 0.65*** | 0.30, 0.99 | 0.61** | 0.22, 1.01 | |
| CKD 4 | 1.51*** | 0.76, 2.27 | 1.40** | 0.56, 2.22 | 1.62*** | 0.66, 2.57 | |
| CKD 5 | 0.25 | −1.99, 2.50 | 0.42 | −1.99, 2.84 | 0.38 | −2.16, 2.93 | |
| TPA | Non-CKD | Reference | – | – | – | – | – |
| CKD 1 | 2.40 | −2.62, 7.43 | 2.48 | −3.53, 8.48 | 3.99 | −2.60, 10.58 | |
| CKD 2 | 4.55 | −0.91, 10.01 | 5.84 | −1.75, 13.42 | 6.79 | −1.55, 15.12 | |
| CKD 3 | −0.38 | −8.62, 7.87 | 0.8 | −10.19, 11.80 | 2.90 | −9.17, 14.97 | |
| CKD 4 | 15.69 | −8.18, 39.56 | 21.97 | −4.81, 48.74 | 27.14 | −2.11, 56.39 | |
| CKD 5 | 33.43 | −36.57, 103.44 | 48.21 | −28.78, 125.19 | 49.63 | −28.43, 127.69 | |
| TT3 | Non-CKD | Reference | – | – | – | – | – |
| CKD 1 | −5.44*** | −6.86, −4.02 | −1.43 | −3.12, 0.26 | −1.25 | −2.97, 0.47 | |
| CKD 2 | −12.04*** | −13.58, −10.50 | −4.26*** | −6.40, −2.13 | −4.18*** | −6.36, −2.00 | |
| CKD 3 | −22.19*** | −24.52, −19.85 | −12.38*** | −15.47, −9.28 | −11.13*** | −14.30, −7.97 | |
| CKD 4 | −30.66*** | −37.37, −23.95 | −19.42*** | −26.90, −11.94 | −20.07*** | −27.75, −12.40 | |
| CKD 5 | −45.49*** | −65.35, −25.64 | −34.03** | −55.77, −12.30 | −31.54** | −52.03, −11.06 | |
| TT4 | Non-CKD | Reference | – | – | – | – | – |
| CKD 1 | −0.17*** | −0.26, −0.08 | −0.05 | −0.16, 0.06 | −0.05 | −0.16, 0.07 | |
| CKD 2 | −0.16** | −0.26, −0.57 | 0.01 | −0.13, 0.15 | 0.02 | −0.13, 0.17 | |
| CKD 3 | 0.10 | −0.05, 0.25 | 0.19 | −0.01, 0.39 | 0.22* | 0.01, 0.44 | |
| CKD 4 | 0.50* | 0.06, 0.94 | 0.43 | −0.06, 0.91 | 0.27 | −0.25, 0.79 | |
| CKD 5 | −1.18 | −2.48, 0.12 | −1.12 | −2.53, 0.30 | −1.14 | −2.52, 0.25 | |
Non-adjusted: no adjustment covariates.
Adjust I: adjusted for gender, age, race, education level, marital status, and PIR.
Adjust II: adjusted for body mass index, hypertension, diabetes, chronic disease, cancer, smoking status, drinking status, work activity, and recreational activities.
CKD = Chronic kidney disease, FT3 = free triiodothyronine, FT4 = free thyroxine, TB = thyroglobulin, TBA = thyroglobulin antibodies, TPA = thyroid peroxidase antibodies, TSH = thyroid-stimulating hormone, TT3 = total triiodothyronine, TT4 = total thyroxine.
P < .05.
P < .01.
P < .001.
The associations between FT3, TT3, FT4, TT4, TSH, and CKD were further analyzed by gender grouping. Subgroup analysis by gender revealed that FT3 and TT3 were negatively associated with CKD stages in both males and females, except for TT3 in males, which exhibited a positive association with CKD stage 1; however, this difference was not statistically significant. TSH exhibited a positive association with CKD, particularly in CKD stages 2 to 4, with significance observed only in males (P < .05). In males, FT4 and TT4 demonstrated a positive association with CKD stage 3. In females, FT4 was positively associated with CKD 4 stage, and negatively with CKD stages 1 and 2. TT4 exhibited a negative association with CKD 5 only in males. Table 3 shows the subgroup analysis.
Table 3.
Subgroup analysis of the association of CKD stages with FT3, FT4, TSH, TT3, and TT4 by gender.
| Variable | Gender | CKD stages | Nonadjusted | Adjust I | Adjust II | |||
|---|---|---|---|---|---|---|---|---|
| β | 95% CI | β | 95% CI | β | 95% CI | |||
| FT3 | Male | Non-CKD | Reference | – | – | – | – | – |
| CKD 1 | −0.13*** | −0.16, −0.11 | −0.01 | −0.04, 0.02 | −0.01 | −0.05, 0.02 | ||
| CKD 2 | −0.34*** | −0.34, −0.28 | −0.05** | −0.09, −0.01 | −0.05** | −0.10, −0.01 | ||
| CKD 3 | −0.54*** | −0.58, −0.49 | −0.16*** | −0.22, −0.11 | −0.16*** | −0.22, −0.10 | ||
| CKD 4 | −0.84*** | −1.00, −0.68 | −0.43*** | −0.58, −0.27 | −0.43*** | −0.59, −0.27 | ||
| CKD 5 | −0.93*** | −1.33, −0.53 | −0.57** | −0.95, −0.20 | −0.55** | −0.93, −0.17 | ||
| Female | Non-CKD | Reference | – | – | – | – | – | |
| CKD 1 | −0.21*** | −0.27, −0.16 | −0.15*** | −0.23, −0.08 | −0.10** | −0.18, −0.03 | ||
| CKD 2 | −0.33*** | −0.40, −0.27 | −0.22*** | −0.32, −0.13 | −0.17*** | −0.26, −0.07 | ||
| CKD 3 | −0.46*** | −0.55, −0.37 | −0.33*** | −0.47, −0.19 | −0.24*** | −0.38, −0.11 | ||
| CKD 4 | −0.64*** | −0.88, −0.40 | −0.55*** | −0.84, −0.26 | −0.55*** | −0.84, −0.26 | ||
| CKD 5 | −0.92* | −1.71, −0.13 | −0.66 | −1.66, 0.34 | −0.61 | −1.51, 0.29 | ||
| FT4 | Male | Non-CKD | Reference | – | – | – | – | – |
| CKD 1 | −0.01 | −0.02, 0 | 0. | −0.01, 0.01 | 0 | −0.01, 0.01 | ||
| CKD 2 | 0. | −0.02, 0.01 | 0.01 | 0, 0.03 | 0.01 | 0, 0.03 | ||
| CKD 3 | 0.03** | 0.01, 0.05 | 0.05*** | 0.03, 0.08 | 0.05*** | 0.02, 0.07 | ||
| CKD 4 | 0.04 | −0.02, 0.11 | 0.07* | 0, 0.13 | 0.05 | −0.02, 0.12 | ||
| CKD 5 | −0.13 | −0.29, 0.03 | −0.09 | −0.25, 0.07 | −0.09 | −0.25, 0.06 | ||
| Female | Non-CKD | Reference | – | – | – | – | – | |
| CKD 1 | −0.02** | −0.04, −0.01 | −0.03*** | −0.05, −0.01 | −0.02** | −0.04, −0.01 | ||
| CKD 2 | −0.02 | −0.03, 0 | −0.04** | −0.06, −0.01 | −0.03* | −0.05, 0. | ||
| CKD 3 | 0.02* | 0, 0.05 | −0.01 | −0.04, 0.03 | 0.01 | −0.03, 0.04 | ||
| CKD 4 | 0.11*** | 0.05¸ 0.17 | 0.05 | −0.02, 0.13 | 0.08* | 0, 0.15 | ||
| CKD 5 | −0.15 | −0.35, 0.06 | −0.10 | −0.35, 0.16 | −0.09 | −0.32, 0.14 | ||
| TSH | Male | Non-CKD | Reference | – | – | – | – | – |
| CKD 1 | 0.18* | 0.02, 0.34 | 0.15* | 0, 0.29 | 0.15 | −0.01, 0.32 | ||
| CKD 2 | 0.47*** | 0.30, 0.64 | 0.30*** | 0.12, 0.48 | 0.30** | 0.10, 0.50 | ||
| CKD 3 | 0.64*** | 0.37, 0.91 | 0.45*** | 0.18, 0.72 | 0.45** | 0.15, 0.75 | ||
| CKD 4 | 3.46*** | 2.57, 4.35 | 3.20*** | 2.47, 3.94 | 3.58*** | 2.78, 4.39 | ||
| CKD 5 | 0.33 | −1.95, 2.61 | 0.53 | −1.27, 2.32 | 0.59 | −1.27, 2.44 | ||
| Female | Non-CKD | Reference | – | – | – | – | – | |
| CKD 1 | 0.31* | 0.02, 0.59 | 0.28 | −0.09, 0.65 | 0.34 | −0.09, 0.78 | ||
| CKD 2 | 0.58*** | 0.27, 0.90 | 0.41 | −0.07, 0.89 | 0.46 | −0.11, 1.03 | ||
| CKD 3 | 1.00*** | 0.54, 1.46 | 0.87* | 0.19, 1.55 | 0.80 | 0, 1.59 | ||
| CKD 4 | 0.35 | −0.83, 1.54 | 0.16 | −1.28, 1.61 | 0.12 | −1.61, 1.84 | ||
| CKD 5 | 0.17 | −3.74, 4.08 | 0.21 | −4.80, 5.21 | −0.12 | −5.47, 5.24 | ||
| TT3 | Male | Non-CKD | Reference | – | – | – | – | – |
| CKD 1 | −4.71*** | −6.38, −3.03 | 0 | −1.91, 1.91 | 0.03 | −1.99, 2.05 | ||
| CKD 2 | −12.21*** | −13.99, −10.43 | −2.81* | −5.15, −0.47 | −3.02* | −5.52, −0.52 | ||
| CKD 3 | −22.06*** | −24.85, −19.28 | −10.18*** | −13.65, −6.71 | −9.49*** | −13.20, −5.77 | ||
| CKD 4 | −36.47*** | −45.77, −27.17 | −22.04*** | −31.61, −12.47 | −20.19*** | −30.25, −10.14 | ||
| CKD 5 | −44.62*** | −68.41, −20.82 | −34.11** | −57.51, −10.70 | −32.42** | −55.61, −9.23 | ||
| Female | Non-CKD | Reference | – | – | – | – | – | |
| CKD 1 | −6.11*** | −8.45, −3.77 | −3.22* | −6.13, −0.30 | −2.59 | −5.56, 0.37 | ||
| CKD 2 | −11.86*** | −14.45, −9.27 | −6.19** | −10.00, −2.38 | −5.37** | −9.23, −1.52 | ||
| CKD 3 | −22.26*** | −26.04, −18.48 | −15.32*** | −20.70, −9.94 | −13.13*** | −18.54, −7.72 | ||
| CKD 4 | −27.09*** | −36.77, −17.40 | −18.93** | −30.37, −7.48 | −20.61*** | −32.32, −8.90 | ||
| CKD 5 | −46.33** | −78.36, −14.31 | −32.03 | −71.63, 7.56 | −30.10 | −66.48, 6.29 | ||
| TT4 | Male | Non-CKD | Reference | – | – | – | – | – |
| CKD 1 | −0.09 | −0.21, 0.04 | 0.04 | −0.10, 0.18 | 0.02 | −0.12, 0.17 | ||
| CKD 2 | −0.03 | −0.16, 0.10 | 0.13 | −0.04, 0.30 | 0.15 | −0.03, 0.34 | ||
| CKD 3 | 0.18 | −0.02, 0.38 | 0.28* | 0.03, 0.54 | 0.30* | 0.03, 0.57 | ||
| CKD 4 | 0.20 | −0.47, 0.87 | 0.29 | −0.41, 0.99 | 0.32 | −0.42, 1.06 | ||
| CKD 5 | −1.75* | −3.47, −0.04 | −1.75* | −3.45, −0.05 | −1.72* | −3.42, −0.02 | ||
| Female | Non-CKD | Reference | – | – | – | – | – | |
| CKD 1 | −0.29*** | −0.44, −0.15 | −0.12 | −0.30, 0.05 | −0.11 | −0.30, 0.07 | ||
| CKD 2 | −0.29*** | −0.45, −0.14 | −0.09 | −0.32, 0.14 | −0.11 | −0.35, 0.14 | ||
| CKD 3 | −0.02 | −0.25, 0.21 | 0.12 | −0.21, 0.44 | 0.16 | −0.19, 0.50 | ||
| CKD 4 | 0.53 | −0.05, 1.12 | 0.53 | −0.16, 1.22 | 0.22 | −0.52, 0.96 | ||
| CKD 5 | −0.65 | −2.58, 1.28 | −0.01 | −2.40, 2.38 | −0.32 | −2.63, 1.99 | ||
Non-adjusted: no adjustment covariates.
Adjust I: adjusted for gender, age, race, education level, marital status, and PIR.
Adjust II: adjusted for body mass index, hypertension, diabetes, chronic disease, cancer, smoking status, drinking status, work activity, and recreational activities.
CI = confidence interval, CKD = chronic kidney disease, FT3 = free triiodothyronine, FT4 = free thyroxine, TSH = thyroid-stimulating hormone, TT3 = total triiodothyronine, TT4 = total thyroxine.
P < .05.
P < .01.
P < .001.
3.3. Mendelian randomization study
After single nucleotide polymorphism screening, 4 SNPs were selected for CKD (stage 1) and 141 SNPs for hypothyroidism (stage 2), for forward and reverse analyses, respectively. Furthermore, F statistics were conducted for the selected SNPs, indicating that the study findings are unlikely to be affected by weak instrumental variable bias.
In Phase 1 analysis with CKD as the exposure variable, no evidence of horizontal gene pleiotropy (egger_intercept = −0.00014, P = .88) and heterogeneity (P > .05) was observed. IVW analysis indicated that CKD was a risk factor for hypothyroidism (odds ratio [OR] = 1.0041, 95% confidence interval [CI]: 1.0007–1.0075, P = .0186). The weighted median yielded consistent findings (OR = 1.0042, 95%CI: 1.0002–1.0082, P = .0399), and similar directions and magnitudes were observed with the weighted mode, simple mode, and MR egger methods (Table 4, Fig. 2).
Table 4.
Associations between CKD and heterogeneity.
| Phase | Exposure | Outcome | Methods | OR | 95% CI | P value |
|---|---|---|---|---|---|---|
| 1 | CKD | Hypothyroidism | IVW | 1.0041 | 1.0007, 1.0075 | .0186 |
| Weighted median | 1.0042 | 1.0002, 1.0082 | .0399 | |||
| Weighted mode | 1.0043 | 0.9999, 1.0086 | .1498 | |||
| Simple mode | 1.0046 | 0.9998, 1.0094 | .1550 | |||
| MR Egger | 1.0051 | 0.9932, 1.0170 | .4910 | |||
| 2 | Hypothyroidism | CKD | IVW | 2.0540 | 0.8680, 4.8603 | .1014 |
| Weighted median | 2.1119 | 0.5775, 7.7225 | .2584 | |||
| Weighted mode | 2.6926 | 0.6186, 11.7208 | .1902 | |||
| Simple mode | 2.4788 | 0.2183, 28.1494 | .4659 | |||
| MR Egger | 1.8204 | 0.2597, 12.7588 | .5480 |
CI = confidence interval, CKD = chronic kidney disease, IVW = inverse variance weighted, MR = Mendelian randomization, OR = odds ratio.
Figure 2.
Scatter plot of the genetic association of CKD-associated SNPs with hypothyroidism. CKD = chronic kidney disease, MR = Mendelian randomization, SNPs = single nucleotide polymorphisms.
In Phase 2 analysis, neither horizontal gene pleiotropy (egger_intercept = 0.00448, P = .89) nor heterogeneity (P > .05) was observed. IVW analysis revealed no causal association between hypothyroidism and CKD (OR = 2.540, 95%CI: 0.8680–4.8603, P = .1014). The remaining 4 analyses yielded consistent findings (Table 4, Fig. 3). Sensitivity analysis of the Mendelian randomization analysis results was conducted by systematically eliminating IVs one by one. This approach can effectively identify whether the random error introduced during IV selection influences the research findings (Figs. S1 and S2, Supplemental Digital Content, http://links.lww.com/MD/O140). Figures S3 and S4, Supplemental Digital Content, http://links.lww.com/MD/O140 show the forest plots.
Figure 3.
Scatter plot of the genetic association of hypothyroidism-associated SNPs and CKD. CKD = chronic kidney disease, MR = Mendelian randomization, SNPs = single nucleotide polymorphisms.
4. Discussion
This study employed multiple regression analysis to assess the relationship between CKD and hypothyroidism using NHANES 2007 to 2012 data. The findings indicated that FT3 and TT3 levels were negatively associated with CKD stages, whereas TSH levels exhibited a positive association with CKD stages. Subgroup analysis by gender revealed similar associations, particularly among males. Bidirectional Mendelian randomization analysis further indicated a causal association between CKD and hypothyroidism.
Hypothyroidism is a common yet often overlooked endocrine complication in patients with CKD.[40] The changes in T3, T4, and TSH levels were the primary manifestations. TT3 and FT3 exhibited the most significant decrease, whereas TT4 exhibited a small decrease. FT4 and TT4 demonstrated similar performance.[41] Our analysis of NHANES data indicated a more significant decrease in T3 levels as CKD stages deteriorated, which is consistent with previous studies. This change may be associated with systemic acidosis, malnutrition, and validation markers induced by CKD.[42–45] Furthermore, our study revealed a slight decline in T4 levels, which may be associated with the presence of circulating inhibitors.[41] TSH levels were consistent with findings in the literature, demonstrating a significant increase with the progression of CKD.[41,46] Low T3 levels are an independent risk factor for all-cause mortality in patients with thyroid disorders.[47] Thus, T3 or T3 combined with T4 supplementation may enhance outcomes in patients with hypothyroidism and CKD.[48] Consistent with previous studies, no significant differences were observed in thyroglobulin and thyroglobulin antibodies across various CKD stages.[49] However, thyroid peroxidase antibodies exhibited an increasing trend with the progression of the CKD stage.[49,50]
To examine the causal association between CKD and reduced thyroid function, a bidirectional Mendelian randomization analysis was conducted. The results indicated a positive causal association between CKD and hypothyroidism, whereas no robust reverse causal relationship was observed. The study population analyzed in the Mendelian randomization analysis of CKD and reduced thyroid function conducted by Ellervik et al[46] was solely females, whereas we did not differentiate by gender in our analysis. Moreover, our findings indicated that the risk of reduced thyroid function increased with higher CKD stages. Phosphate binders decrease the absorption of thyroid hormones, leading to decreased thyroid function.[51] SLC34A1 (rs3812035), as a cotransporter of sodium phosphate, influences phosphate stability,[52] which may be a possible cause of hypothyroidism induced by CKD. Tamm-Horsfall protein (rs13333226, THP) was thought to play a crucial role in renal function alterations associated with hypothyroidism, although causal investigations are lacking.[53] However, it was suggested that THP may play a role in CKD and hypothyroidism. Rs7805747 and rs2453533 were genetic variations, with limited research reported aside from their associations with kidney-related diseases.
This study has several limitations. First, the experimental design was retrospective, necessitating longitudinal studies to validate our results. Second, our study did not investigate the genetic loci and mechanisms underlying CKD and hypothyroidism; thus, the causal association between the 2 conditions cannot be confirmed.
In summary, our findings indicate a causal association between CKD and hypothyroidism, with the risk of hypothyroidism increasing as CKD progresses. However, this study did not identify a reverse causal association between CKD and reduced thyroid function.
Acknowledgments
We would like to thank TopEdit (www.topeditsci.com) for its linguistic assistance during the preparation of this manuscript.
Author contributions
Conceptualization: Yin Xu, Guofeng Wang, Wei Wei, Ning Li.
Data curation: Yin Xu, Xinmei Wang, Guofeng Wang, Ning Li.
Formal analysis: Yin Xu, Xinmei Wang, Guofeng Wang, Ning Li.
Investigation: Wei Wei.
Methodology: Yin Xu, Wei Wei.
Project administration: Ning Li.
Resources: Guofeng Wang, Wei Wei, Ning Li.
Software: Yin Xu, Xinmei Wang, Guofeng Wang.
Validation: Xinmei Wang, Guofeng Wang, Wei Wei, Ning Li.
Visualization: Yin Xu, Xinmei Wang.
Writing – original draft: Yin Xu, Xinmei Wang, Guofeng Wang, Wei Wei, Ning Li.
Writing – review & editing: Yin Xu, Guofeng Wang, Wei Wei, Ning Li.
Supplementary Material
Abbreviations:
- CI
- confidence interval
- CKD
- chronic kidney disease
- FT3
- free triiodothyronine
- FT4
- free thyroxine
- IVW
- inverse variance weighted
- MR
- Mendelian randomization
- NHANES
- National Health and Nutrition Examination Survey
- OR
- odds ratio
- SNP
- single nucleotide polymorphism
- TB
- thyroglobulin
- TBA
- thyroglobulin antibodies
- TPA
- thyroid peroxidase antibodies
- TSH
- thyroid-stimulating hormone
- TSMR
- 2-sample Mendelian randomization
- TT3
- total triiodothyronine
- TT4
- total thyroxine.
The patients/participants provided their written informed consent to participate in this study.
The studies involving human participants were reviewed and approved by NCHS of the CDC.
The authors have no funding and conflicts of interest to disclose.
The datasets generated during and/or analyzed during the current study are publicly available.
Supplemental Digital Content is available for this article.
How to cite this article: Xu Y, Wang X, Wang G, Wei W, Li N. Relationship between hypothyroidism and chronic kidney disease: Results from the National Health and Nutrition Examination Survey 2007 to 2012 and Mendelian randomization study. Medicine 2024;103:51(e40925).
WW and NL contributed to this article equally.
Contributor Information
Yin Xu, Email: xuyinxu0071@163.com.
Xinmei Wang, Email: 397451715@qq.com.
Guofeng Wang, Email: 397451715@qq.com.
Wei Wei, Email: 377268368@qq.com.
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