Interactions Between Genome-Wide Genetic Factors and Smoking Influencing Risk of Systemic Lupus Erythematosus

Environment and systemic autoimmune rheumatic diseases: an overview and future directions

Introduction

Despite progress in our understanding of disease pathogenesis for systemic autoimmune rheumatic diseases (SARD), these diseases are still associated with high morbidity, disability, and mortality. Much of the strongest evidence to date implicating environmental factors in the development of autoimmunity has been based on well-established, large, longitudinal prospective cohort studies.

Methods

Herein, we review the current state of knowledge on known environmental factors associated with the development of SARD and potential areas for future research.

Results

The risk attributable to any particular environmental factor ranges from 10-200%, but exposures are likely synergistic in altering the immune system in a complex interplay of epigenetics, hormonal factors, and the microbiome leading to systemic inflammation and eventual organ damage. To reduce or forestall the progression of autoimmunity, a better understanding of disease pathogenesis is still needed.

Conclusion

Owing to the complexity and multifactorial nature of autoimmune disease, machine learning, a type of artificial intelligence, is increasingly utilized as an approach to analyzing large datasets. Future studies that identify patients who are at high risk of developing autoimmune diseases for prevention trials are needed.

Environment, Lifestyles, and Climate Change: The Many Nongenetic Contributors to The Long and Winding Road to Autoimmune Diseases

A critical unanswered question is what is causing the increase in the prevalence of autoimmunity and autoimmune diseases around the world. Given the rapidity of change, this is likely the result of major recent alterations in our exposures to environmental risk factors for these diseases. More evidence is becoming available that the evolution of autoimmune disease, years or even decades in the making, results from multiple exposures that alter susceptible genomes and immune systems over time. Exposures during sensitive phases in key developmental or hormonal periods may set the stage for the effects of later exposures. It is likely that synergistic and additive impacts of exposure mixtures result in chronic low-level inflammation. This inflammation may eventually pass thresholds that lead to immune system activation and autoimmunity, and with further molecular and pathologic changes, the complete clinical syndrome emerges. Much work remains to be done to define the mechanisms and risk and protective factors for autoimmune conditions. However, evidence points to a variety of pollutants, xenobiotics, infections, occupational exposures, medications, smoking, psychosocial stressors, changes in diet, obesity, exercise, and sleep patterns, as well as climate change impacts of increased heat, storms, floods, wildfires, droughts, UV radiation, malnutrition, and changing infections, as possible contributors. Substantial investments in defining the role of causal factors, in whom and when their effects are most important, the necessary and sufficient gene-environment interactions, improved diagnostics and therapies, and preventive strategies are needed now to limit the many negative personal, societal, and financial impacts that will otherwise occur.

Obesity and tobacco smoking are independently associated with poor patient-reported outcomes in SLE: a cross-sectional study

We investigated associations of obesity and tobacco smoking with health-related quality of life (HRQoL), pain, fatigue, and functional impairment in systemic lupus erythematosus (SLE). Furthermore, we explored whether there was an effect modification between these two factors. We included adult SLE patients from the Linköping University Hospital (n = 325) in the present cross-sectional analysis. We further included population-based controls and performed cardinality matching to balance age and sex distributions with cases (n = 224). HRQoL was assessed with the EQ-5D index score; pain, fatigue, and overall SLE-related health state with visual analogue scales (VAS; 0 [best] to 100 [worst]); and functional impairment with the HAQ-DI. Unacceptable outcomes were defined as VAS scores corresponding to the 90th percentile derived from the matched controls. SLE patients reported worse scores than controls in all measures, and approximately 30% experienced unacceptable outcomes. When compared with normal-weight, obese SLE patients reported lower HRQoL, and greater functional impairment and risk of unacceptable pain (OR: 3.2; 95% CI 1.6-6.7) and fatigue (OR: 2.1; 95% CI 1.0-4.3). Similarly, the current smokers reported higher levels of functional impairment and a greater risk of unacceptable pain (OR: 3.8; 95% CI 1.8-8.2) and fatigue (OR: 2.8; 95% CI 1.3-5.9) than never smokers. The associations were independent of age, sex, disease duration, disease activity, and organ damage. There was no evidence of a synergistic effect between increased BMI and smoking on any outcome. In summary, obesity and smoking are risk factors for unacceptable patient-reported outcomes in SLE, regardless of clinical activity.

Cholesterol-rich dietary pattern during early pregnancy and genetic variations of cholesterol metabolism genes in predicting gestational diabetes mellitus: a nested case-control study

BACKGROUND

Higher dietary cholesterol intake during pregnancy increases risk of gestational diabetes mellitus (GDM). However, no studies have investigated interindividual variability in cholesterol metabolism and the association of genetics and diet on GDM.

OBJECTIVE

; To prospectively evaluate the joint association of cholesterol-rich dietary patterns and polymorphisms of genes coding for cholesterol metabolism pathway proteins with GDM.

METHODS

A total of 1116 pregnant females from the Tongji Birth Cohort were enrolled. GDM was diagnosed according to a 75-g 2-h oral glucose tolerance test at 24-28 wk of gestation. Dietary data were collected by a validated food frequency questionnaire. The reduced-rank regression method was used to identify dietary patterns using dietary cholesterol as the response variable. Time-of-flight mass spectrometry was used for genotyping. The genetic risk score (GRS) for GDM was constructed with genetic variants in 28 cholesterol metabolism-related single-nucleotide polymorphisms (SNPs). Conditional logistic regression models were used to assess the odds ratio (OR) for GDM.

RESULTS

The cholesterol-rich dietary pattern was rich in livestock and poultry meat and eggs but lower in cereals. The multivariable-adjusted ORs for GDM were 1.24 (95% confidence interval: 1.06-1.44) per SD increment of cholesterol-rich pattern scores and 1.28 (1.09-1.49) per tertile GRS. The variants of the CYP7A1 rs3808607 G→T/rs8192871 G→A/rs7833904 A→T, as well as AGGG and TTGA haplotypes of 4 CYP7A1-spanning SNPs, were significantly associated with GDM. For the joint effect, the OR was 3.53 (1.71-7.31) in the highest categories of both dietary pattern scores and GRS compared with individuals with the lowest strata without significant interaction (P for interaction = 0.101).

CONCLUSIONS

Both a cholesterol-rich dietary pattern and genetic variants of cholesterol metabolism genes are associated with risk of GDM. Adherence to a cholesterol-rich dietary pattern during early pregnancy promotes the chance of GDM, especially in women with higher GRS.

CLINICAL TRIAL REGISTRY

This trial was registered at http://www.chictr.org.cn (Registration number: ChiCTR1800016908). URL: =https://www.chictr.org.cn/showprojEN.html?proj=28081.

Phenotype Risk Score but Not Genetic Risk Score Aids in Identifying Individuals With Systemic Lupus Erythematosus in the Electronic Health Record

OBJECTIVE

Systemic lupus erythematosus (SLE) poses diagnostic challenges. We undertook this study to evaluate the utility of a phenotype risk score (PheRS) and a genetic risk score (GRS) to identify SLE individuals in a real-world setting.

METHODS

Using a de-identified electronic health record (EHR) database with an associated DNA biobank, we identified 789 SLE cases and 2,261 controls with available MEGAEX genotyping. A PheRS for SLE was developed using billing codes that captured American College of Rheumatology SLE criteria. We developed a GRS with 58 SLE risk single-nucleotide polymorphisms (SNPs).

RESULTS

SLE cases had a significantly higher PheRS (mean ± SD 7.7 ± 8.0 versus 0.8 ± 2.0 in controls; P < 0.001) and GRS (mean ± SD 12.2 ± 2.3 versus 11.0 ± 2.0 in controls; P < 0.001). Black individuals with SLE had a higher PheRS compared to White individuals (mean ± SD 10.0 ± 10.1 versus 7.1 ± 7.2, respectively; P = 0.002) but a lower GRS (mean ± SD 9.0 ± 1.4 versus 12.3 ± 1.7, respectively; P < 0.001). Models predicting SLE that used only the PheRS had an area under the curve (AUC) of 0.87. Adding the GRS to the PheRS resulted in a minimal difference with an AUC of 0.89. On chart review, controls with the highest PheRS and GRS had undiagnosed SLE.

CONCLUSION

We developed a SLE PheRS to identify established and undiagnosed SLE individuals. A SLE GRS using known risk SNPs did not add value beyond the PheRS and was of limited utility in Black individuals with SLE. More work is needed to understand the genetic risks of SLE in diverse populations.

Interactions Between Genetic Risk and Diet Influencing Risk of Incident Female Gout: Discovery and Replication Analysis of Four Prospective Cohorts

OBJECTIVE

To examine whether the cross-sectional gene-diet interaction for prevalent hyperuricemia among women translates prospectively to risk of incident female gout.

METHODS

We analyzed the interaction between genetic predisposition and adherence to a healthy dietary pattern (i.e., Dietary Approaches to Stop Hypertension [DASH] score) on risk of incident female gout in 18,244 women from Nurses' Health Study (NHS; discovery) and 136,786 women from 3 additional prospective female cohorts from the US and UK (replication). Genetic risk score (GRS) was calculated from 114 urate-associated loci.

RESULTS

In the NHS and replication cohorts, association between diet and gout risk was larger and stronger among women with higher genetic risk. In all cohorts combined, compared to women with an unhealthy DASH score (less than the mean score), multivariable relative risk (RR) for incident gout among women with a healthy DASH score (greater than/equal to the mean score) was 0.67 (95% confidence interval [95% CI] 0.60-0.76) among higher GRS (greater than/equal to the mean score) and 0.91 (0.78-1.05) among lower GRS (P for multiplicative interaction = 0.001); multivariable RR for higher versus lower GRS was 2.03 (95% CI 1.80-2.29) and 1.50 (95% CI 1.31-1.71) among unhealthy and healthy DASH score groups, respectively. Additive interaction was also significant, in both the discovery and replication cohorts (P < 0.001), with 51% of the excess risk attributable to the additive gene-diet interaction in all cohorts combined.

CONCLUSION

The deleterious effect of genetic predisposition on risk of incident female gout was more pronounced among women with unhealthy diets, with nearly half the excess risk attributable to this gene-diet interaction. These data elucidate the important synergy of genetics and diet for female gout development.

Differential impact of environmental factors on systemic and localized autoimmunity

The influence of environmental factors on the development of autoimmune disease is being broadly investigated to better understand the multifactorial nature of autoimmune pathogenesis and to identify potential areas of intervention. Areas of particular interest include the influence of lifestyle, nutrition, and vitamin deficiencies on autoimmunity and chronic inflammation. In this review, we discuss how particular lifestyles and dietary patterns may contribute to or modulate autoimmunity. We explored this concept through a spectrum of several autoimmune diseases including Multiple Sclerosis (MS), Systemic Lupus Erythematosus (SLE) and Alopecia Areata (AA) affecting the central nervous system, whole body, and the hair follicles, respectively. A clear commonality between the autoimmune conditions of interest here is low Vitamin D, a well-researched hormone in the context of autoimmunity with pleiotropic immunomodulatory and anti-inflammatory effects. While low levels are often correlated with disease activity and progression in MS and AA, the relationship is less clear in SLE. Despite strong associations with autoimmunity, we lack conclusive evidence which elucidates its role in contributing to pathogenesis or simply as a result of chronic inflammation. In a similar vein, other vitamins impacting the development and course of these diseases are explored in this review, and overall diet and lifestyle. Recent work exploring the effects of dietary interventions on MS showed that a balanced diet was linked to improvement in clinical parameters, comorbid conditions, and overall quality of life for patients. In patients with MS, SLE and AA, certain diets and supplements are linked to lower incidence and improved symptoms. Conversely, obesity during adolescence was linked with higher incidence of MS while in SLE it was associated with organ damage. Autoimmunity is thought to emerge from the complex interplay between environmental factors and genetic background. Although the scope of this review focuses on environmental factors, it is imperative to elaborate the interaction between genetic susceptibility and environment due to the multifactorial origin of these disease. Here, we offer a comprehensive review about the influence of recent environmental and lifestyle factors on these autoimmune diseases and potential translation into therapeutic interventions.

Risk prediction models for incident systemic lupus erythematosus among women in the Nurses' health study cohorts using genetics, family history, and lifestyle and environmental factors

OBJECTIVE

Systemic lupus erythematosus (SLE) is a severe multisystem autoimmune disease that predominantly affects women. Its etiology is complex and multifactorial, with several known genetic and environmental risk factors, but accurate risk prediction models are still lacking. We developed SLE risk prediction models, incorporating known genetic, lifestyle and environmental risk factors, and family history.

METHODS

We performed a nested case-control study within the Nurses' Health Study cohorts (NHS). NHS began in 1976 and enrolled 121,700 registered female nurses ages 30-55 from 11 U.S. states; NHSII began in 1989 and enrolled 116,430 registered female nurses ages 25-42 from 14 U.S. states. Participants were asked about lifestyle, reproductive and environmental exposures, as well as medical information, on biennial questionnaires. Incident SLE cases were self-reported and validated by medical record review (Updated 1997 American College of Rheumatology classification criteria). Those with banked blood samples for genotyping (∼25% of each cohort), were selected and matched by age (± 4 years) and race/ethnicity to women who had donated a blood sample but did not develop SLE. Lifestyle and reproductive variables, including smoking, alcohol use, body mass index, sleep, socioeconomic status, U.S. region, menarche age, oral contraceptive use, menopausal status/postmenopausal hormone use, and family history of SLE or rheumatoid arthritis (RA) were assessed through the questionnaire prior to SLE diagnosis questionnaire cycle (or matched index date). Genome-wide genotyping results were used to calculate a SLE weighted genetic risk score (wGRS) using 86 published single nucleotide polymorphisms (SNPs) and 10 classical HLA alleles associated with SLE. We compared four sequential multivariable logistic regression models of SLE risk prediction, each calculating the area under the receiver operating characteristic curve (AUC): 1) SLE wGRS, 2) SLE/RA family history, 3) lifestyle, environmental and reproductive factors and 4) combining model 1-3 factors. Models were internally validated using a bootstrapped estimate of optimism of the AUC. We also examined similar sequential models to predict anti-dsDNA positive SLE risk.

RESULTS

We identified and matched 138 women who developed incident SLE to 1136 women who did not. Models 1-4 yielded AUCs 0.63 (95%CI 0.58-0.68), 0.64 (95%CI 0.59-0.68), 0.71(95% CI 0.66-0.75), and 0.76 (95% CI 0.72-0.81). Model 4 based on genetics, family history and eight lifestyle and environmental factors had best discrimination, with an optimism-corrected AUC 0.75. AUCs for similar models predicting anti-dsDNA positive SLE risk, were 0.60, 0.63, 0.81 and 0.82, with optimism corrected AUC of 0.79 for model 4.

CONCLUSION

A final model including SLE weighted genetic risk score, family history and eight lifestyle and environmental SLE risk factors accurately classified future SLE risk with optimism corrected AUC of 0.75. To our knowledge, this is the first SLE prediction model based on known risk factors. It might be feasibly employed in at-risk populations as genetic data are increasingly available and the risk factors easily assessed. The NHS cohorts include few non-White women and mean age at incident SLE was early 50s, calling for further research in younger and more diverse cohorts.

Understanding the Concept of Pre-Clinical Autoimmunity: Prediction and Prevention of Systemic Lupus Erythematosus: Identifying Risk Factors and Developing Strategies Against Disease Development

There is growing evidence that preceding the diagnosis or classification of systemic lupus erythematosus (SLE), patients undergo a preclinical phase of disease where markers of inflammation and autoimmunity are already present. Not surprisingly then, even though SLE management has improved over the years, many patients will already have irreversible disease-related organ damage by time they have been diagnosed with SLE. By gaining a greater understanding of the pathogenesis of preclinical SLE, we can potentially identify patients earlier in the disease course who are at-risk of transitioning to full-blown SLE and implement preventative strategies. In this review, we discuss the current state of knowledge of SLE preclinical pathogenesis and propose a screening and preventative strategy that involves the use of promising biomarkers of early disease, modification of lifestyle and environmental risk factors, and initiation of preventative therapies, as examined in other autoimmune diseases such as rheumatoid arthritis and type 1 diabetes.

The role of environmental exposures and gene-environment interactions in the etiology of systemic lupus erythematous

Systemic lupus erythematosus (SLE) is a complex, chronic autoimmune disease, whose etiology includes both genetic and environmental factors. Individual genetic risk factors likely only account for about one-third of observed heritability among individuals with a family history of SLE. A large portion of the remaining risk may be attributable to environmental exposures and gene-environment interactions. This review focuses on SLE risk associated with environmental factors, ranging from chemical and physical environmental exposures to lifestyle behaviors, with the weight of evidence supporting positive associations between SLE and occupational exposure to crystalline silica, current smoking, and exogenous estrogens (e.g., oral contraceptives and postmenopausal hormones). Other risk factors may include lifestyle behaviors (e.g., dietary intake and sleep) and other exposures (e.g., ultraviolet [UV] radiation, air pollution, solvents, pesticides, vaccines and medications, and infections). Alcohol use may be associated with decreased SLE risk. We also describe the more limited body of knowledge on gene-environment interactions and SLE risk, including IL-10, ESR1, IL-33, ITGAM, and NAT2 and observed interactions with smoking, UV exposure, and alcohol. Understanding genetic and environmental risk factors for SLE, and how they may interact, can help to elucidate SLE pathogenesis and its clinical heterogeneity. Ultimately, this knowledge may facilitate the development of preventive interventions that address modifiable risk factors in susceptible individuals and vulnerable populations.

Impact of adiposity on risk of female gout among those genetically predisposed: sex-specific prospective cohort study findings over >32 years

OBJECTIVES

To evaluate the joint (combined) association of excess adiposity and genetic predisposition with the risk of incident female gout, and compare to their male counterparts; and determine the proportion attributable to body mass index (BMI) only, genetic risk score (GRS) only, and to their interaction.

METHODS

We prospectively investigated potential gene-BMI interactions in 18 244 women from the Nurses' Health Study and compared with 10 888 men from the Health Professionals Follow-Up Study. GRS for hyperuricaemia was derived from 114 common urate-associated single nucleotide polymorphisms.

RESULTS

Multivariable relative risk (RR) for female gout was 1.49 (95% CI 1.42 to 1.56) per 5 kg/m² increment of BMI and 1.43 (1.35 to 1.52) per SD increment in the GRS. For their joint association of BMI and GRS, RR was 2.18 (2.03 to 2.36), more than the sum of each individual factor, indicating significant interaction on an additive scale (p for interaction <0.001). The attributable proportions of joint effect for female gout were 42% (37% to 46%) to adiposity, 37% (32% to 42%) to genetic predisposition and 22% (16% to 28%) to their interaction. Additive interaction among men was smaller although still significant (p interaction 0.002, p for heterogeneity 0.04 between women and men), and attributable proportion of joint effect was 14% (6% to 22%).

CONCLUSIONS

While excess adiposity and genetic predisposition both are strongly associated with a higher risk of gout, the excess risk of both combined was higher than the sum of each, particularly among women.

Cardiovascular Health in Pediatric Rheumatologic Diseases

Cardiovascular disease risk is evident during childhood for patients with juvenile systemic lupus erythematosus, juvenile dermatomyositis, and juvenile idiopathic arthritis. The American Heart Association defines cardiovascular health as a positive health construct reflecting the sum of protective factors against cardiovascular disease. Disease-related factors such as chronic inflammation and endothelial dysfunction increase cardiovascular disease risk directly and through bidirectional relationships with poor cardiovascular health factors. Pharmacologic and nonpharmacologic interventions to improve cardiovascular health and long-term cardiovascular outcomes in children with rheumatic disease are needed.

Abnormal Mitochondrial Physiology in the Pathogenesis of Systemic Lupus Erythematosus

Systemic lupus erythematosus (SLE) is an autoimmune disorder characterized by abnormalities within the innate and adaptive immune systems. Activation and proliferation of a wide array of immune cells require significant up-regulation in cellular energy metabolism, with the mitochondria playing an essential role in the initiation and maintenance of this response. This article highlights how abnormal mitochondrial function may occur in SLE and focuses on how energy metabolism, oxidative stress, and impaired mitochondrial repair play a role in the pathogenesis of the disease. How this may represent an appealing novel therapeutic target for future drug therapy in SLE also is discussed.

Hidden mysteries behind genome, epigenome, and exposome of lupus erythematosus

As an extremely heterogeneous disease, lupus erythematosus (LE) occurs when a genetically susceptible individual encounters specific environmental triggers. Epigenetics bridges genetics and environment and helps explain their interactions. Comprehensively understanding the hidden mysteries behind the genome, epigenome, and exposome of lupus could contribute to precision prevention and treatment of lupus.

Interaction between the STAT4 rs11889341(T) risk allele and smoking confers increased risk of myocardial infarction and nephritis in patients with systemic lupus erythematosus

Objective

To investigate how genetics influence the risk of smoking-related systemic lupus erythematosus (SLE) manifestations.

Methods

Patients with SLE (n_{discovery cohort}=776, n_{replication cohort}=836) were genotyped using the 200K Immunochip single nucleotide polymorphisms (SNP) Array (Illumina) and a custom array. Sixty SNPs with SLE association (p<5.0×10⁻⁸) were analysed. Signal transducer and activator of transcription 4 (STAT4) activation was assessed in in vitro stimulated peripheral blood mononuclear cells from healthy controls (n=45).

Results

In the discovery cohort, smoking was associated with myocardial infarction (MI) (OR 1.96 (95% CI 1.09 to 3.55)), with a greater effect in patients carrying any rs11889341 STAT4 risk allele (OR 2.72 (95% CI 1.24 to 6.00)) or two risk alleles (OR 8.27 (95% CI 1.48 to 46.27)).

Smokers carrying the risk allele also displayed an increased risk of nephritis (OR 1.47 (95% CI 1.06 to 2.03)). In the replication cohort, the high risk of MI in smokers carrying the risk allele and the association between the STAT4 risk allele and nephritis in smokers were confirmed (OR 6.19 (95% CI 1.29 to 29.79) and 1.84 (95% CI 1.05 to 3.29), respectively).

The interaction between smoking and the STAT4 risk allele resulted in further increase in the risk of MI (OR 2.14 (95% CI 1.01 to 4.62)) and nephritis (OR 1.53 (95% CI 1.08 to 2.17)), with 54% (MI) and 34% (nephritis) of the risk attributable to the interaction. Levels of interleukin-12-induced phosphorylation of STAT4 in CD8+ T cells were higher in smokers than in non-smokers (mean geometric fluorescence intensity 1063 vs 565, p=0.0063).

Lastly, the IL12A rs564799 risk allele displayed association with MI in both cohorts (OR 1.53 (95% CI 1.01 to 2.31) and 2.15 (95% CI 1.08 to 4.26), respectively).

Conclusions

Smoking in the presence of the STAT4 risk gene variant appears to increase the risk of MI and nephritis in SLE. Our results also highlight the role of the IL12−STAT4 pathway in SLE-cardiovascular morbidity.