Impact of climate factors and climate–gene interaction on systemic lupus erythematosus patients' response to glucocorticoids therapy

Abstract

Background

Glucocorticoids (GCs) were the essential drugs for systemic lupus erythematosus (SLE). However, different patients differ substantially in their response to GCs treatment. Our current study aims at investigating whether climate variability and climate‐gene interaction influence SLE patients' response to the therapy of GCs.

Methods

In total, 778 SLE patients received therapy of GCs for a study of 12‐week follow‐up. The efficacy of GCs treatment was evaluated using the Systemic Lupus Erythematosus Disease Activity Index. The climatic data were provided by China Meteorological Data Service Center. Additive and multiplicative interactions were examined.

Results

Compared with patients with autumn onset, the efficacy of GCs in patients with winter onset is relatively poor (OR_adj = 1.805, 95%CI_adj: 1.181–3.014, p _adj = 0.020). High mean relative humidity during treatment decreased the efficacy of GCs (OR_adj = 1.033, 95%CI_adj: 1.008–1.058, p _adj = 0.011), especially in female (OR_adj = 1.039, 95%CI_adj: 1.012–1.067, p _adj = 0.004). There was a significant interaction between sunshine during treatment and TRAP1 gene rs12597773 on GCs efficacy (Recessive model: AP = 0.770). No evidence of significant interaction was found between climate factors and the GR gene polymorphism on the improved GCs efficacy in the additive model. Multiplicative interaction was found between humidity in the month prior to treatment and GR gene rs4912905 on GCs efficacy (Dominant model: OR = 0.470, 95%CI: 0.244–0.905, p = 0.024).

Conclusions

Our findings suggest that climate variability influences SLE patients' response to the therapy of GCs. Interactions between climate and TRAP1/GR gene polymorphisms were related to GCs efficacy. The results guide the individualized treatment of SLE patients.

Climate variability influences SLE patients response to therapy of GCs. Interactions between climate and TRAP1/GR gene polymorphisms were related to GCs efficacy. The results guide the individualized treatment of SLE patients.

1. INTRODUCTION

Systemic lupus erythematosus (SLE), one of the most prevalent multisystemic autoimmune diseases, is well characterized by autoantibody production and almost all vital organ damage, with high clinical heterogeneity. In China, the prevalence of SLE is 30/100,000 ~ 70/100,000 ¹ , ² and females are prone to this disease, the ratio of females to males is about 9.2:1. ³ Its high prevalence and recurrence put a considerable burden on both SLE sufferers and society, and negatively impact the quality of life and the ability to work for a large number of individuals.

Glucocorticoids (GCs) are undoubtedly highly effective drugs for SLE since it was first used in the 1950s. ⁴ Over the past several decades, glucocorticoids as first‐line therapy drugs, exerted anti‐infective and immunosuppressive effects, having contributed tremendously to the improvement of the prognosis of patients. ⁵ However, different patients differ substantially in their response to GCs treatment: some patients responded favorably to GCs, and some patients relapsed frequently and even suffered serious adverse reactions. ⁶ In addition to genetic predisposition, environmental factors have also been recognized as playing a considerable role in the SLE etiology. ⁷ In recent years, a growing body of studies supported that the climate factors, including average temperatures, relative humidity (RH), precipitation, and sunshine duration, may act a key part in the exacerbation of autoimmune diseases. ⁸ , ⁹ Several studies have suggested that SLE had a seasonal variation. ¹⁰ , ¹¹ A study in China provided evidence that the number of active SLE patients had a positive correlation with the amount of precipitation, wind speed, and sunshine duration, while it was negatively correlated with barometric pressure. ⁸ However, till now, the vast majority of studies have mainly focused on the observed relationship between climatic factors and disease susceptibility/activity utilizing inpatient data based on a cross‐sectional study. No prospective studies have paid attention to the role of climatic factors in the efficacy of GCs treatment among SLE patients. Thus, we speculate that climate variability may have an impact on the treatment of autoimmune diseases, and climatic prediction of the response may be a potential possibility for improving the efficacy of disease treatment.

To identify this association, we evaluated the efficacy of GCs and explored the effects of climate factors on the treatment efficiency of GCs of Chinese Han populations with SLE for the first time. Numerous studies have shown that gene–environment interaction leads to the occurrence and development of diseases. ¹² , ¹³ , ¹⁴ Moreover, our previous studies have demonstrated that the polymorphisms of heat shock protein gene TRAP1 and glucocorticoid receptor gene GR played important roles in GCs efficacy. ¹⁵ , ¹⁶ Therefore, we also explored whether exist an interaction between climatic factors and TRAP1/GR gene polymorphisms affects the efficacy of GCs.

2. PATIENTS AND METHODS

Anhui Medical University Ethics Committee approved the study. Each participant fully understood the content of the investigation and signed written informed consent.

2.1. Subjects, treatment, and GCs efficacy assessment

In total, 778 Chinese patients with SLE were recruited from the Department of Rheumatology and Immunology at the First and the Second Affiliated Hospital of Anhui Medical University between January 2011 and October 2020, fulfilling the revised criteria that were published by the ACR in 1997. ¹⁷ All patients' baseline Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) > 4, and they did not take GCs (over the past 3 months) or only low‐dose GCs (not more than 7.5 mg/day) ¹⁸ were received using for maintenance therapy. In addition, exclusion criteria for this study included: patients who were gravida or during lactating, patients who were refused follow‐up, patients who have an allergy history of hydroxychloroquine or contraindications to GCs as well as bacterial or fungal infection, patients who have lupus crisis or require GCs plus therapy.

All the patients received therapy of GCs and were follow‐up for 12 weeks. Generally, patients with a baseline score of the SLEDAI of less than 10 received GCs (prednisone) at a therapeutic dose of 10 mg–0.5 mg/kg daily, and patients with a baseline score of the SLEDAI of 10 or higher received GCs (prednisone) at therapy dose of 0.5–1.0 mg/kg daily. During the treatment, all the patients also received combination therapy with hydroxychloroquine, which has multiple protective effects on the patient's organs and with a slow onset of action (approximately 3–6 months). ¹⁹ In the course of treatment, dosage adjustment of drugs for the patients was on the basis of the consultation made by rheumatologists.

The SLEDAI was used to evaluate the efficiency of GCs therapy, and it was estimated at weeks 0, 4, 8, and 12 by rheumatologists. A total of 750 (96.40%) patients adhered to the 12‐week follow‐up study, and they were classified into two cases: GCs sensitive and GCs insensitive. Patients whose SLEDAI scores were ≤4 points at week 12 or the decreased score (baseline SLEDAI minus 12‐week) was ≥5 points were considered as the GCs‐sensitive group, whereas the GCs‐insensitive group was defined as patients' SLEDAI score >4 points at week 12 and dropped score <5 points after 12 weeks, or the patients received the therapy of other immunosuppressants for the reason that lacking efficiency during the course of treatment.

2.2. Climatic data collection

Anhui Province (longitude 114°54′ E‐119°37′ E, latitude 29°41′ N–34°38′ N) has a warm temperate and subtropical climate with four distinct seasons. Summer and winter are long while spring and autumn are short, and the duration of spring, summer, autumn, and winter in Anhui is 2 (April–May), 4 (June–September), 2 (October–November), and 4 (December–March) months, respectively, which was determined by the geographical location and climatic characteristics. ⁸

Climatic data between January 2011 and December 2020 were gathered from the China Meteorological Information Center (http://data.cma.cn). The available daily meteorological data including average atmospheric pressure (0.1 hPa), average temperature (0.1°C), average RH (1%), cumulative precipitation from 20:00 to 20:00 (0.1 mm), average wind velocity (0.1 m/s), and sunshine duration (0.1 h). The mean and variability of each climatic factor during the GCs treatment of each patient were calculated, and the variability of each climatic factor was represented by the standard deviation (SD). In addition, we also calculated the mean and variability of meteorological factors of each SLE patient in the month prior to treatment.

2.3. Interaction analysis

According to our previous researches, Seven tag SNPs in TRAP1 gene (rs3794701, rs17183750, rs6500552, rs6500550, rs4786428, rs3751842, rs12597773) and four tag SNPs in GR gene (rs7701443, rs17100234, rs10482672, rs4912905) are SNPs of GCs efficacy‐related. A total of 647 subjects in this study were willing to provide peripheral blood, Blood Genome DNA Extraction Kit (QIAGEN) was used to do genomic DNA extraction from the peripheral blood samples, Determining genotype adopted the Multiplex SNaPshot technique based on ABI fluorescence‐based assay discrimination method (Applied Biosystems). See supplementary files for Genotype frequencies of TRAP1/GR gene polymorphisms and Hardy–Weinberg equilibrium results. Multifactor Dimensionality Reduction (MDR) was applied to detect high‐order interactions. We used both multiplicative and additive scales to detect interactions between climate factors and the 11 SNPs. Multifactor dimensionality reduction (MDR) was applied to detect high‐order interactions between climatic factors and TRAP1/GR gene polymorphisms. This is a non‐parametric and no genetic pattern interaction analysis method and training balanced accuracy, testing balanced accuracy, and cross‐validation consistency were used for the evaluation and selection advantage model. We used the logistic regression model to test the multiplicative interaction. Detailed additive interaction was described by Andersson et al. ²⁰ Additive interactions were tested using the relative excess risk of interaction (RERI) and the attributable proportion of interaction (AP). If RERI and AP are equal to 0, there is no biological interaction. Multiplicative interactions only reflect the statistical interaction. By calculating additive interactions, it can help to identify high‐risk groups and give intervention measures, which is more of public health significance.

2.4. Statistical analysis

Mean ± SD and p ₅₀ (interquartile range) were suitable for describing normally distributed continuous variables and askew‐distributed continuous variables respectively. The number (percentage) was suitable for describing qualitative variables. The differences between normally distributed data and qualitative data were calculated by t‐test and χ² test respectively. Associations between the efficacy of GCs and climatic factors were evaluated by univariate logistic regression and expressed by crude odds ratio (OR) with a 95% confidence interval (95%CI; Sensitive = 0, Insensitive = 1). Multivariate logistic regression was performed to control the potential confounding, including sex, age, smoking, drinking, weight, height, exposed to GCs before baseline, GCs dose, and baseline SLEDAI score. Besides, a sub‐group analysis based on the gender of SLE patients was also conducted. All probability values <0.05 were regarded as significantly different (two‐sided test). Data were managed and analyzed using the SPSS software 16.0, SAS version 9.1, R software version 4.1, GraphPad version 8.0, and GMDR software version 0.7.

3. RESULTS

3.1. Characteristics of demographic and climatic factors of patients with SLE

In the present research, we collected a total of 778 SLE patients, 750 patients completed the follow‐up for 12 weeks and were involved in the final statistical analyses. Patients were included in two groups: 468 patients in the GCs‐sensitive group and 282 patients in the GCs‐insensitive group. Comparisons of baseline demographic characteristics between groups of GCs sensitive and insensitive were displayed in Table S1. The baseline clinical characteristics of the two groups were reported in Table S2. Climatic characteristics of follow‐up patients between sensitive and insensitive groups during treatment and in the month prior to treatment were listed in Tables S3 and S4, respectively.

3.2. Association between season onset and the efficacy of GCs treatment

In our present study, a total of 107 patients were in autumn, 204 patients in winter, 161 patients in spring, and 278 patients in summer. We investigate the association between GCs efficiency and season of onset using univariate logistic regression. The result was represented in Table 1, showing that patients' season of onset was associated with GCs efficacy. Compared with patients with autumn onset, the efficacy of GCs treatment in patients with winter onset is relatively poor (OR = 1.814, 95%CI: 1.103–2.984, p = 0.019). In multivariate logistic regression adjusting for potential confounders, the season of onset still correlated significantly with the response of patients to GCs therapy (OR_adj = 1.834, 95%CI_adj: 1.102–3.050, p _adj = 0.020).

TABLE 1.

Influence of seasonal variables on the efficacy of glucocorticoids (GCs) treatment in patients with systemic lupus erythematosus (SLE).

Season	Sensitive (n = 468)	Insensitive (n = 282)	Crude OR (95% CI)	Crude p value	Adjusted a OR (95% CI)	Adjusted p value
Autumn (n = 107)	75 (70.09)	32 (29.91)	Ref	Ref	Ref	Ref
Winter (n = 204)	115 (56.37)	89 (43.63)	1.814 (1.103–2.984)	0.019	1.834 (1.102–3.050)	0.020
Spring (n = 161)	107 (66.46)	54 (33.54)	1.183 (0.698–2.005)	0.533	1.166 (0.681–1.996)	0.576
Summer (n = 278)	171 (61.51)	107 (38.49)	1.450 (0.907–2.320)	0.117	1.460 (0.894–2.385)	0.131

^a Adjusted for sex, age, smoking, drinking, weight, height, exposed to GCs before baseline, baseline SLEDAI scores, and GCs dose.

3.3. Association between climatic factors and GCs efficacy

The mean and variability of climatic factors of SLE patients during treatment and in the month prior to treatment were calculated, and their correlation with GCs efficacy was shown in Tables 2 and 3. In univariate logistic regression, significant associations were found between GCs efficacy and the mean RH during treatment (OR = 1.031, 95%CI: 1.007–1.056, p = 0.013, Table 2). After multivariate logistic regression, it has a more significant association with the mean RH during treatment (OR_adj = 1.033, 95%CI_adj: 1.008–1.058, p _adj = 0.011). The boxplot of the mean RH during treatment was shown in Figure 1A. OR value increases with increasing humidity, and high mean relative humidity during treatment reduced the GCs treatment effect (Figure 1B). But we were not able to find any relationship between climatic factors in the month prior to treatment and the efficacy of GCs.

TABLE 2.

Analysis of glucocorticoids efficacy and climatic factors during treatment.

Climatic factors	Sensitive (n = 468)	Insensitive (n = 282)	Crude OR (95% CI)	Crude p value	Adjusted a OR (95% CI)	Adjusted p value
The mean of climatic factors
Atmospheric pressure	10,082.16 (138.99)	10,076.16 (124.42)	1.000 (0.999–1.001)	0.612	1.000 (0.999–1.001)	0.518
Temperature	205.05 (141.72)	203.82 (135.10)	1.000 (0.998–1.002)	0.711	1.001 (0.999–1.003)	0.582
Relative humidity	75.44 (8.37)	75.89 (8.77)	1.031 (1.007–1.056)	0.013	1.033 (1.008–1.058)	0.011
Cumulative precipitation	34.45 (31.84)	34.46 (30.97)	1.000 (0.994–1.006)	0.978	1.001 (0.994–1.007)	0.821
Wind velocity	20.55 (5.96)	20.12 (5.40)	0.980 (0.953–1.009)	0.588	0.993 (0.962–1.024)	0.638
Sunshine duration	49.44 (17.96)	47.37 (18.31)	0.997 (0.985–1.010)	0.694	0.998 (0.985–1.010)	0.706
The variability of climatic factors
Atmospheric pressure	54.96 (15.70)	54.40 (14.85)	0.997 (0.985–1.009)	0.629	0.995 (0.983–1.008)	0.465
Temperature	44.73 (17.73)	44.08 (17.07)	0.995 (0.982–1.008)	0.425	0.994 (0.981–1.008)	0.418
Relative humidity	12.02 (3.70)	11.62 (3.76)	0.940 (0.892–0.990)	0.054	0.945 (0.891–1.003)	0.062
Cumulative precipitation	88.83 (79.62)	89.55 (83.05)	1.000 (0.997–1.002)	0.926	1.000 (0.997–1.003)	0.950
Wind velocity	7.98 (2.88)	7.92 (2.32)	1.000 (0.947–1.057)	0.992	1.001 (0.947–1.058)	0.974
Sunshine duration	40.27 (5.73)	40.35 (4.98)	1.015 (0.980–1.051)	0.411	1.018 (0.982–1.055)	0.337

^a Adjusted for sex, age, smoking, drinking, weight, height, exposed to GCs before baseline, baseline SLEDAI scores, and GCs dose.

TABLE 3.

Analysis of glucocorticoids efficacy and climatic factors in the month prior to treatment.

Climatic factors	Sensitive (n = 468)	Insensitive (n = 282)	Crude OR (95% CI)	Crude p value	Adjusted a OR (95% CI)	Adjusted p value
The mean of climatic factors
Atmospheric pressure	10,088.34 (156.50)	10,084.21 (149.27)	1.000 (0.999–1.001)	0.598	1.000 (0.999–1.001)	0.511
Temperature	191.03 (140.31)	198.58 (144.84)	1.000 (0.999–1.002)	0.709	1.000 (0.999–1.002)	0.654
Relative humidity	74.41 (11.66)	75.58 (10.55)	1.011 (0.993–1.029)	0.232	1.012 (0.993–1.030)	0.209
Cumulative precipitation	25.69 (38.41)	28.88 (36.89)	1.001 (0.997–1.005)	0.617	1.002 (0.997–1.006)	0.483
Wind velocity	20.52 (6.79)	20.74 (6.31)	1.001 (0.976–1.027)	0.933	1.003 (0.977–1.030)	0.807
Sunshine duration	50.02 (22.18)	50.07 (21.08)	0.999 (0.989–1.008)	0.766	0.998 (0.989–1.008)	0.752
The variability of climatic factors
Atmospheric pressure	38.74 (19.93)	39.67 (20.16)	0.997 (0.986–1.008)	0.543	0.996 (0.985–1.007)	0.466
Temperature	28.53 (11.42)	28.46 (12.34)	1.005 (0.988–1.023)	0.577	1.005 (0.987–1.023)	0.614
Relative humidity	11.45 (4.80)	11.14 (4.33)	0.994 (0.955–1.034)	0.758	0.992 (0.952–1.033)	0.688
Cumulative precipitation	66.99 (83.03)	65.46 (79.72)	1.000 (0.998–1.002)	0.981	1.000 (0.998–1.002)	0.848
Wind velocity	7.75 (3.66)	7.58 (3.14)	0.989 (0.943–1.038)	0.656	0.990 (0.944–1.040)	0.699
Sunshine duration	39.66 (7.07)	40.06 (7.72)	1.012 (0.984–1.041)	0.400	1.016 (0.987–1.046)	0.287

^a Adjusted for sex, age, smoking, drinking, weight, height, exposed to GCs before baseline, baseline SLEDAI scores, and GCs dose.

FIGURE 1.

Association between relative humidity and glucocorticoids (GCs) efficacy of systemic lupus erythematosus (SLE) patients. (A) Boxplot of the relative humidity during treatment and GCs efficacy of SLE patients. (B) Association between relative humidity during treatment and GCs efficacy of SLE patient.

In gender subgroup analysis, the mean RH during treatment showed a statistically significant correlation with the therapeutic effects of GCs in female patients (OR = 1.036, 95%CI: 1.010–1.063, p = 0.007; OR_adj = 1.039, 95%CI_adj: 1.012–1.067, p _adj = 0.004, Table S5), we also found a marginal correlation between mean RH in the month prior to treatment and GCs efficacy in female patients (OR_adj = 1.020, 95%CI_adj: 1.001–1.040, p _adj = 0.044 Table S7), the boxplots were shown in Figure S1. In males, significant associations were found between GCs efficacy and the variability of wind velocity and sunshine duration during treatment (OR = 0.748, 95%CI: 0.576–0.972, p = 0.030; OR_adj = 0.687, 95%CI = 0.506–0.933, p _adj = 0.016; OR = 1.232, 95%CI: 1.057–1.436, p = 0.008, OR_adj = 1.379, 95%CI = 1.121–1.697, p _adj = 0.002, Table S6). A significant association was found between the variability of temperature in the month prior to male patients' treatment and the efficacy of GCs (OR = 1.068, 95%CI: 1.010–1.130, p = 0.021; OR_adj = 1.078, 95%CI_adj: 1.008–1.152, p _adj = 0.029, Table S8). Meanwhile, we found that mean temperature and sunshine duration during treatment in male patients have a marginal association with the therapeutic effects of GCs (OR = 1.007, 95%CI: 1.000–1.013, p = 0.040; OR_adj = 1.007, 95%CI_adj: 1.000–1.014, p _adj = 0.047; OR = 1.037, 95%CI: 0.990–1.086, p = 0.127; OR_adj = 1.055, 95%CI_adj: 1.001–1.113, p _adj = 0.047, Table S5). Other subgroup analysis results are presented in the Supplementary Material.

3.4. Interaction analysis

Interaction between climate factor and TRAP1 gene polymorphism.

A significant multiplicative interaction was observed between humidity during treatment and rs6500550 for the therapeutic effects of GCs (OR = 0.457, 95%CI:0.236–0.882, p = 0.020, Table 4), no additive interaction was observed. There is a significant multiplicative interaction between sunshine during treatment and rs17183750 on GCs efficacy, and also significant multiplicative interaction with rs12597773 on GCs efficacy (OR = 2.190, 95%CI:1.122–4.274, p = 0.022 in the dominant model; OR = 0.196, 95%CI:0.042–0.901, p = 0.036 in the recessive model, Table 4). There was an additive interaction between sunshine during treatment and rs12597773 on GCs efficacy (AP = 0.770, 95%CI: 0.464–1.077 in the recessive model, Table 4), and the synergy of two factors increased the risk of poor therapeutic efficacy. Therefore, 77% of the population exposed to sunshine and carrying this variant allele can be attributed to the superposition of the two risk factors. MDR analysis showed that there was a multifactor interaction of sunshine, pressure, and precipitation during treatment and rs4786428 on GC efficacy (p = 0.011, Table S17), there was a multifactor interaction between rs3794701, rs6500550, rs4786428, and the humidity in the month prior to treatment (p = 0.011, Table S17).

TABLE 4.

Interaction analysis of climatic factors‐TRAP1 SNP for glucocorticoids efficacy during treatment.

Groups	Multiplicative interaction		Additive interaction
	OR (95%CI)	p value	RERI (95%CI)	AP (95%CI)
Dominant model
RHU and rs6500550	0.457 (0.236–0.882)	0.020	−1.194 (−0.511–0.122)	−0.719 (−1.490–0.052)
SSD and rs17183750	2.190 (1.122–4.274)	0.022	−1.077 (−2.169–0.015)	−0.843(−1.789–0.103)
Recessive model
SSD and rs12597773	0.196 (0.042–0.901)	0.036	2.650 (−0.858–6.159)	0.770 (0.464–1.077)

Abbreviations: RHU, relative humidity; SSD, sunshine duration.

Interaction between climate factor and GR gene polymorphism.

No evidence of significant interaction was found between climate factors and the GR gene polymorphism on the efficacy of GCs in the additive model. Only a multiplicative interaction was found between humidity in the month prior to treatment and rs4912905 (OR = 0.470, 95%CI:0.244–0.905, p = 0.024 in the dominant model).

4. DISCUSSION

In this study, we proposed that climatic factors may act a key part in influencing patients' response to glucocorticoid therapy for the first time. We found that compared with patients with autumn onset, the efficacy of GCs treatment in winter‐onset patients is relatively poor. Second, high mean relative humidity is a climatic factor that reduces the efficacy of GCs, the result exists in any subgroup analysis by gender, age, and BMI. There is a significant link between the variability of climatic factors and the GCs efficacy in male patients. Interactions between climate factors and the TRAP1/GR gene polymorphism were also observed.

In our present study, we found that, compared with patients with autumn onset, the efficacy of GCs treatment in patients with winter onset was relatively poor, especially in winter, indicating that GCs efficacy was affected by patients' season of onset. We propose the following mechanisms to explain this association: first, in winter, an increase in the intensity of the infection for SLE might be decreasing the efficacy of GCs treatment. Second, studies have found that, in winter, levels of inflammatory biomarkers such as neutrophils were elevated and the plasma cortisol level of SLE patients was decreased, ²¹ , ²² leading to a sharp increase in the level of autoimmunity and aggravating the patients' symptoms. Third, in winter, the increasing opportunities which patients' exposure to the cold stimulation leads to the proinflammatory cytokines accumulated in patients' bodies (IL‐1β, IL‐12, and TNF‐α), increasing the proportion of TNF‐α/IL‐10 and IL‐12/IL‐10, and then aggravate the inflammatory response in SLE patients, thereby reduce GCs curative effects. ²³ , ²⁴

In the regression analysis between climatic variability and the efficacy of GCs, we found that the high RH during the treatment decreased GCs efficacy, especially in female, youth, and normal body mass group. This may be due to the fact that high relative humidity causes an immune imbalance of Th/Treg in the human body, thereby increasing the expression of inflammatory cytokines which IL‐6 and other cytokines. ²⁵ They have been confirmed to be involved in the pathogenesis and prognosis of SLE. Then the increase of inflammatory cytokines in SLE patients will be harmful to the efficacy of GCs. On the contrary, the drugs are affected by humidity. Excessive humidity may damage the stability of the tablet and affect the quality of drugs, then decreased the efficacy of GCs. ²⁶ , ²⁷ Besides, the high RH could reduce the adaptability of patients' physical function to the external environment, causing the body's first line of defense to be destroyed by bacteria and viruses, ²⁵ , ²⁸ finally aggravating patients' inflammatory reactions and clinical symptoms, which is not conducive to the prognosis of SLE patients and make GCs less effective.

In addition to this, in our present study, we also found that long sunshine duration and the high variability of sunshine duration during treatment were considered factors in reducing GCs efficacy in males. As we all know, sunshine duration is one of the environmental factors not conducive to SLE. Ultraviolet (UV) may induce DNA damage and stimulate autoantibodies producing and increasing the level of the pro‐inflammatory cytokine, resulting in recrudescence of SLE and disease course prolonging, decreasing the efficacy of GCs. ²⁹ Our study also found that the high variability of wind velocity during treatment was considered a protective factor for GCs efficacy in male patients. At present, there are few studies on the relationship between wind speed and autoimmune diseases. Increasing the efficacy of GCs, we guess may be related to promoting the circulation of blood. ³⁰ Finally, we also found that the variability of temperature in the month prior to treatment was considered a factor in reducing GC efficacy in males. This may be due to that the coordinated actions of the endocrine and immune systems are paramount to the maintenance of human health and the variability of temperature can affect the body's endocrine system, neuroendocrine receptors respond to stress by inhibiting or reducing glucocorticoid secretion, then cause loss of immune function regulation and increasing inflammation. ³¹ , ³² , ³³

Interaction results displayed that there was an interaction of climate factors and genetic polymorphisms of the TRAP1 gene on the efficacy of GCs. Especially, the coexistence of rs12597773 variation and sunshine substantially increased the risk of poor efficacy GCs. Existing evidence shows that both UV and TRAP1 are related to DNA damage. TRAP1 is a heat shock protein that can prevent DNA damage induced by oxidants. ³⁴ TRAP1 is a component of the mitochondrial pathway and interacts with mitochondria to maintain various important biological functions of the cell. ³⁵ , ³⁶ Glucocorticoid receptor (GR) exists in mitochondria, and mitochondria may be an important place for its function. Therefore, TRAP1 may influence the therapeutic effect of GCs in patients with SLE by affecting the GR biological activity and mitochondrial function. TRAP1 gene polymorphism may affect the function of TRAP1, leading to poor efficacy of GCs. From this, we speculate that the synergistic effect of TRAP1 gene polymorphism and UA may increase the risk of poor efficacy of GCs.

Adverse climatic factors affect the therapeutic effect, on the contrary, positive and favorable climatic factors can also improve the therapeutic effect. It suggests that we should pay attention to climate predictions and avoid adverse effects on therapeutic efficacy. According to climate change, choose the appropriate area to live in or cross‐regional rest timely, especially for patients with gene polymorphism mutations. In combination with the local characteristics of climate resources, the introduction of the health care industry, the development and utilization of health care climate resources, to create a professional health care area for people.

This study, for the first time, investigated comprehensively the impact of climatic factors on GCs efficiency in patients with SLE. Especially, the relationships between climatic factors and the therapeutic effect of GCs on SLE patients. We also explored the influence of climatic factors and gene interaction on the efficacy of GCs. The reliable sources of data employed guarantee the credibility of our study. The above are the major strengths of our study. There are several limitations to our present study. First, the extrapolation of present findings needs to be cautious as all the SLE patients were selected from Anhui Province. Second, a total of 28 patients were excluded from our final statistical analysis since they did not complete the 12‐week follow‐up, which may affect our results mildly. Finally, all patients received long‐term therapy with hydroxychloroquine and its onset of action was slow (3–6 months). ¹⁹ In our present study, all the patients just had a shorter follow‐up time (with a minimum of 12 weeks). Therefore, this is generally thought to have a negligible impact on our current research results.

5. CONCLUSIONS

To sum up, our current study suggests that the efficiency of GCs treatment for SLE patients is affected by climatic factors, and climatic factors‐TRAP1/GR gene interaction could also affect GCs efficacy. This study aimed to provide clues for the prediction of GCs efficacy in the clinical treatment and can be considered for use as a prerequisite for creating personalized drug regimens for SLE patients. Our research results suggest that research on the relationship between climatic factors and drug efficacy may be of great significance for the individualized treatment of patients with some diseases.

AUTHOR CONTRIBUTIONS

YFZ and WBH designed the study, LLW, YHW, ZYY, ZL, and YT collected the data and analyzed this data, ZWX, YFC, JHT, JC, CML, and JC contributed to the interpretation of data, TYZ and QMX drafted the manuscript, FMP, HFP, HS, WBH, and YFZ supervised this study. All authors edited the manuscript. All authors read and approved the final manuscript for submission.

FUNDING INFORMATION

This work was supported by grants from the National Natural Science Foundation of China (81872683, 81872687), Key Laboratory of Dermatology, Anhui Medical University, Ministry of Education, China, (AYPYS2022‐6).

CONFLICT OF INTEREST STATEMENT

None.

CONSENT TO PARTICIPATE

Each participant fully understood the investigation and signed written informed consent.

Supporting information

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Zhang T, Xie Q, Wang L, et al. Impact of climate factors and climate–gene interaction on systemic lupus erythematosus patients' response to glucocorticoids therapy. J Clin Lab Anal. 2023;37:e24945. doi: 10.1002/jcla.24945

DATA AVAILABILITY STATEMENT

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.