This prospective cohort study was compliant with the Declaration of Helsinki and approved by the Institutional Review Boards of the University of Ghana School of Medicine and Korle bu Teaching Hospital in Accra, Ghana prior to initiation. Enrollment was initiated at Korle bu Teaching Hospital outpatient rheumatology clinics between 2015 and 2017. All patients provided informed consent prior to enrollment. A consecutive sample of 100 individuals seeking clinical rheumatology care was invited to participate. Inclusion criteria were as follows: 1) ≥ 18 years of age, 2) self-reported Ghanaian ancestry, and 3) fulfilling at least four 1982 American College of Rheumatology (ACR) criteria for SLE (23). Patients who were unwilling or unable to sign consent were excluded.
Initial Enrollment: Each study participant completed a survey to assess demographics, medication lists, and ACR SLE criteria. The Systemic Lupus International Collaborating Clinics (SLICC) damage index and SLE activity index using the modified clinical SELENA-SLEDAI was taken during a routine clinical visit (24, 25). Complete physical examination, vital signs, and anthropometric measurements were recorded. SLE serologic results including anti-nuclear antibodies (ANA), anti-double stranded DNA antibodies (dsDNA), anti-SSA/Ro antibodies (Ro), anti-SSB/La antibodies (La), anti-smith antibodies (smith), anti-U1/RNP antibodies (RNP), and antiphospholipid antibodies (APS) were recorded. Baseline urine samples were evaluated by dipstick and urinalysis where available (Acon Laboratories Inc, San Diego CA), and saliva and whole blood were collected for DNA extraction (Oragene saliva collection kits DNA Genotek, Ottawa, Ontario, Canada) and serum and plasma (Becton, Dickinson and Company, Franklin Lakes, NJ, USA), respectively. Serum, plasma, and saliva samples were dated, batched, and shipped to the New York University (NYU) laboratory for processing.
Follow up: Each patient was followed longitudinally at 6-month intervals for 1 year. During follow up visits, physical exams, SELENA-SLEDAI, laboratory review, and medication lists were recorded. Serum, plasma, and urine dipstick were again taken or recorded.
Apolipoprotein L1 Genotyping: Study patients’ genomic DNA was isolated from saliva using Oragene reagents according to the manufacturer’s instructions (DNA Genotek, Ottawa, Ontario, Canada). As described previously, DNA isolates were stored at -80°C, and quantitated using a Nanodrop-1000 spectrophotometer (Nanodrop Products, Wilmington, DE). DNA templates (100ng) were used for conventional polymerase chain reaction (PCR) as previously described (14). A single 300-base pair DNA segment containing the APOL1 gene, including reference G0 allele and polymorphisms G1 (rs73885319 and rs60910145) and G2 (rs71785313), was amplified using ApliTaq Gold 360 DNA Polymerase (Applied Biosystems, Foster City, CA). For quality control, DNA was elongated in both forward and reverse directions. Genotypes were analyzed using the Genewiz online platform as previously described (14).
Autoantibody Serologic Screening: Batched and shipped serum samples were screened for ANAs using the BioPlexTM 220 ANA Screen in the NYU Langone Hospitals clinical lab. This automated system utilizes multiplex technology to measure 13 antibodies including SLE-relevant antigens dsDNA, chromatin, RNP-68 kDa, SSA-52 kDa, SSA-60 kDa, and Sm/RNP as described (26). A mixture of antigen-coated beads was combined with patient sample and diluent for an incubation period of 20 minutes at 37°C. Beads were washed and treated with anti-human IgG antibody conjugated to phycoerthrin (PE) dye for a 10-minute incubation. Excess conjugate was removed, and the mixture was passed through a detector. The bead type and amount detected were read and reported.
To test the association between APOL1 genotype and SLE outcomes, we treated APOL1 genotype as the predictive variable (coded Number_Alleles) and composite SLICC damage index (SDI), renal function, and case fatality as outcome variables (coded SLICC, Renal, and Mort, respectively). The associations between primary predictor and outcome variables were tested using Poisson regression for the SLICC damage and generalized estimation equations (GEE) to identify factors associated with the composite renal function measure overtime. The distribution of sex, age, SELENA-SLEDAI score, disease duration, kidney function and SLE criteria were assessed across genotype. Factors that associated with the outcome variables at a significance level less than 0.10 were treated as co-variates; they included the clinical SELENA-SLEDAI score, disease duration, and a renal function composite score obtained by performing a factor analysis of mean arterial pressure, eGFR, and proteinuria on urine dipstick followed by a varimax rotation (27, 28). The factor analysis was performed using data from the first visit. The eigenvector and the rotation matrix from this analysis was then used to derive the composite scores at the second and third visit.
Complete case and analyses are presented as the primary result; however, given the high rates of missing data, especially at the third visit, results from analyses using multiple imputations are also presented. Multiple imputation was performed assuming missing at random and monotone missingness. Results from the imputed datasets were combined using Rubin’s approach as implemented in the SAS v9.4 MIANALYZE procedure (29, 30). Analyses were performed using R 3.6.3 or SAS version 9.4.