Clinical Signs and Blood Test Results Among Humans Infected With Zoonotic Simian Foamy Virus: A Case-Control Study

Clinical Signs and Blood Test Results Among Humans Infected With Zoonotic Simian Foamy Virus: A... Abstract Background A spillover of simian foamy virus (SFV) to humans, following bites from infected nonhuman primates (NHPs), is ongoing in exposed populations. These retroviruses establish persistent infections of unknown physiological consequences to the human host. Methods We performed a case-control study to compare 24 Cameroonian hunters infected with gorilla SFV and 24 controls matched for age and ethnicity. A complete physical examination and blood test were performed for all participants. Logistic regression and Wilcoxon signed rank tests were used to compare cases and controls. Results The cases had significantly lower levels of hemoglobin than the controls (median, 12.7 vs 14.4 g/dL; P = .01). Basophil levels were also significantly lower in cases than controls, with no differences for other leukocyte subsets. Cases had significantly higher urea, creatinine, protein, creatinine phosphokinase, and lactate dehydrogenase levels and lower bilirubin levels than controls. Cases and controls had similar frequencies of general, cutaneous, gastrointestinal, neurological, and cardiorespiratory signs. Conclusions The first case-control study of apparently healthy SFV-infected Cameroonian hunters showed the presence of hematological abnormalities. A thorough clinical and laboratory workup is now needed to establish the medical relevance of these observations because more than half of cases had mild or moderate anemia. Clinical Trials Registration NCT03225794. Retrovirus, zoonosis, hemoglobin, simian foamy virus Human diseases of zoonotic origin are a major and increasing public health problem [1]. Retroviruses are of particular concern because of their ability to integrate into the host DNA and persist in infected hosts. Human immunodeficiency virus type 1 (HIV-1) and human T-lymphotropic virus type 1 (HTLV-1) emerged from a simian reservoir through interspecies transmission from nonhuman primates (NHPs) infected with simian immunodeficiency virus and simian T-lymphotropic virus type 1, respectively [2, 3]. The initial cross-species transmission events occurred mainly in Central Africa. Currently, HIV-1 infects approximately 30 million people worldwide, whereas HTLV-1 infects at least 5–10 million people [2, 4], and both viruses are etiological agents of very severe diseases. Simian foamy viruses (SFVs) constitute a third group of complex retroviruses. SFVs replicate in the oral mucosa of NHPs [5] and are mostly transmitted to humans through bites [6, 7], establishing persistent infection in their new host. Such SFV spillover occurs frequently in individuals living in African and Southeast Asian countries in contact with NHPs [8–13] or in individuals exposed in a professional context [6, 7, 14–19]. Clinical examination of 8 SFV-infected people in Germany and North America did not reveal any common pathological features associated with this viral infection [14, 20]. However, these studies lacked uninfected controls. We recently reported that hunters of monkeys and apes living close to the rainforests of Central Africa constitute a high-risk group for zoonotic SFV infection, especially from gorillas [9, 10, 17, 21]. The objective of this case-control study was to compare clinical signs and hematological markers between SFV-infected and uninfected humans. Here, we show that the levels of several hematological markers differ between SFV-infected and uninfected individuals. METHODS Study Design and Participants The research was conducted in accordance with the Helsinki declaration. Ethics approval was obtained from the relevant authorities in Cameroon (National Ethics Committee and Ministry of Health) and France (Commission Nationale de l’Informatique et des Libertés, and Comité de protection des personnes Ile de France IV). This study was registered at Clinicaltrials.gov (available at: https://clinicaltrials.gov/ct2/show/NCT03225794/). All participants gave written informed consent. All participants were Cameroonian men. We previously performed a serological and molecular survey to assess the prevalence of foamy virus infection in people who reported contact with NHPs with a resulting wound [10]. For the present study, cases consisted of individuals who were infected with a gorilla SFV; all were recruited from participants of our former survey. Twenty-four of 33 potentially eligible participants [10] were present in their village at the time of the visit of the medical team and agreed to participate in the study. Other eligible participants had died or moved away. Each case was matched individually for age (±10 years) and ethnicity to 1 SFV-uninfected control, recruited from hunters who participated in the same survey and who lived in same or neighboring villages as cases [10]. SFV infection was defined by positive results of both Western blots (defined as detection of the p70-p74 Gag doublet) and polymerase chain reaction assays (defined as detection of the gene encoding integrase and/or the associated long terminal repeat) performed at the time of the epidemiological survey [10]. Results of SFV diagnostic tests were confirmed at least twice to be positive or negative. Anti–hepatitis B virus (HBV) core antibodies and HBV surface antigen were assessed with the Monolisa kits (catalog numbers 72315 and 72346; Bio-Rad, Marnes-La-Coquette, France). Immunoblot assays were used for the diagnosis of HIV-1 and HTLV-1/2 infection (LAV Blot1; catalog No. 72251, Biorad and HTLV Blot 2.4, MP Diagnostics). One HIV-1–infected control was excluded, and no case was infected with HIV-1. Ten cases and 3 controls were infected with HTLV-1. Clinical and Biological Evaluations A complete clinical examination by a board-certified physician was performed for each case and control at the Centre Pasteur du Cameroun (CPC), in Yaoundé. Blood tests were performed by the medical analysis laboratory at the CPC and are listed in Supplementary Table 1. In addition, blood specimens from 24 participants (12 cases and 12 age- and ethnicity-matched controls) were collected into tubes containing ethylenediaminetetraacetic acid. Plasma was isolated and stored at −80°C. Protein electrophoresis was performed by the Laboratoire de Biologie Médicale Volontaires-Cerballiance in Paris. Ferritin, transferrin, soluble transferrin receptor, haptoglobin, erythropoietin, hepcidin, C-reactive protein, interleukin 6, and interleukin 18 binding protein A levels were quantified by enzyme-linked immunosorbent assay (Supplementary Table 2). Statistical Analysis Logistic regression was performed to study differences in clinical signs between cases and controls. The levels of blood and plasma parameters were treated as quantitative variables, and the paired Wilcoxon signed-rank test was used to compare cases and controls. We used the Fisher exact test to compare frequencies of cases and controls with values outside the normal range, based on sex-specific reference values used in Cameroon (Supplementary Table 1). We used logistic regression to study the effect of confounders (HTLV-1 infection and sampling during the rainy season) on the associations between blood parameters and SFV infection status, with blood parameters transformed into binary variables (based on whether the value was below versus above the median value). We studied associations between hemoglobin levels and plasma biomarkers with the Spearman rank test. Data were analyzed with Stata 12.1 software (StataCorp). RESULTS Frequencies of Clinical Signs do not Differ Between Cases and Controls Cases and controls were bush-meat hunters who reported injuries, mostly bites inflicted during hunting activities (Table 1). Ages ranged from 22 to 67 years. All participants in the study were apparently healthy at the time of the analysis. We performed a complete clinical examination of all study participants. Overall, 65% had at least 1 clinical sign, and 42% had at least 2 clinical signs. General and cutaneous signs were each observed in 38% of participants; high blood pressure, in 31%; other cardiorespiratory signs, in 29%; gastrointestinal signs, in 21%; and neurologic signs, in 8%. Cases and controls had similar frequencies of clinical signs, whether considered individually, by affected organ, or globally (Table 2). Table 1. Demographic Characteristics of Study Participants   Cases  Controls  Pa  Ethnicity      1.00   Bantu  8  8     Pygmy  16  16    Age at sampling, y   Median (IQR)  52 (40–60)  52 (42–59)  .12   Range  27–75  22–67    Duration of infection, mob   Median (IQR)  15 (8–28)       Range  1–40      Wound      .23c   Bite  24  21     Scratch  0  1     Don’t remember  0  2    NHP      .001d   Gorilla  24  12     Chimpanzee  0  2     Monkey  0  7     Don’t remember  0  3      Cases  Controls  Pa  Ethnicity      1.00   Bantu  8  8     Pygmy  16  16    Age at sampling, y   Median (IQR)  52 (40–60)  52 (42–59)  .12   Range  27–75  22–67    Duration of infection, mob   Median (IQR)  15 (8–28)       Range  1–40      Wound      .23c   Bite  24  21     Scratch  0  1     Don’t remember  0  2    NHP      .001d   Gorilla  24  12     Chimpanzee  0  2     Monkey  0  7     Don’t remember  0  3    Data are no. of participants, unless otherwise indicated. Abbreviations: IQR, interquartile range; NHP, nonhuman primate. aBy the χ2 or Wilcoxon signed rank test. The value ≤.05 denotes a statistically significant difference. bEstimated as the time between the reported date of the wound inflicted by the gorilla and the sampling date. cFor comparison of bite vs no bite. dFor comparison of gorilla vs other NHPs. View Large Table 1. Demographic Characteristics of Study Participants   Cases  Controls  Pa  Ethnicity      1.00   Bantu  8  8     Pygmy  16  16    Age at sampling, y   Median (IQR)  52 (40–60)  52 (42–59)  .12   Range  27–75  22–67    Duration of infection, mob   Median (IQR)  15 (8–28)       Range  1–40      Wound      .23c   Bite  24  21     Scratch  0  1     Don’t remember  0  2    NHP      .001d   Gorilla  24  12     Chimpanzee  0  2     Monkey  0  7     Don’t remember  0  3      Cases  Controls  Pa  Ethnicity      1.00   Bantu  8  8     Pygmy  16  16    Age at sampling, y   Median (IQR)  52 (40–60)  52 (42–59)  .12   Range  27–75  22–67    Duration of infection, mob   Median (IQR)  15 (8–28)       Range  1–40      Wound      .23c   Bite  24  21     Scratch  0  1     Don’t remember  0  2    NHP      .001d   Gorilla  24  12     Chimpanzee  0  2     Monkey  0  7     Don’t remember  0  3    Data are no. of participants, unless otherwise indicated. Abbreviations: IQR, interquartile range; NHP, nonhuman primate. aBy the χ2 or Wilcoxon signed rank test. The value ≤.05 denotes a statistically significant difference. bEstimated as the time between the reported date of the wound inflicted by the gorilla and the sampling date. cFor comparison of bite vs no bite. dFor comparison of gorilla vs other NHPs. View Large Table 2. Clinical Signs Among Cases and Controls Sign  Cases, No. (%) (n = 24)  Controls, No. (%) (n = 24)  OR (95% CI)a  Pa  General           Pallor  1 (4)  3 (13)  0.30 (.03–3.16)  .32   Asthenia  2 (8)  2 (8)  1.00 (.13–7.75)  1.00   Icterus  0 (0)  1 (4)  …     Edema  2 (8)  1 (4)  2.09 (.18–24.7)  .56   Adenopathy  6 (25)  4 (17)  1.67 (.40–6.87)  .48   Any  10 (42)  8 (33)  1.43 (.44–4.62)  .55  Cutaneous           Tumefaction  3 (13)  2 (8)  1.57 (.24–10.4)  .64   Maculae, papulae, spots  8 (33)  6 (25)  1.50 (.43–5.26)  .53   Ulceration  1 (4)  0 (0)  …     Any cutaneous sign  11 (46)  7 (29)  2.05 (.62–6.76)  .24  Gastrointestinal           Hepatic pain  2 (8)  5 (21)  0.35 (.06–1.99)  .23   Hepatomegaly  2 (8)  3 (13)  0.64 (.10–4.20)  .64   Splenomegaly  3 (13)  2 (8)  1.57 (.24–10.4)  .64   Any  4 (17)  6 (25)  0.60 (.15–2.47)  .48  Neurological           Muscle weakness  2 (8)  1 (4)  2.09 (.18–24.73)  .56   Areflexia  1 (4)  0 (0)  …     Paralysis  0 (0)  0 (0)  …     Paresthesia/ dysesthesia  1 (4)  0 (0)  …     Any  3 (13)  1 (4)  3.29 (.32–34.08)  .32  Cardiorespiratory           Arrhythmia  1 (4)  0 (0)  …     Cardiac insufficiency  0 (0)  1 (4)  …     Cough  6 (25)  4 (17)  1.67 (.40–6.87)  .48   Other  5 (21)  2 (8)  2.89 (.50–16.7)  .23   High systolic pressureb,c  5 (23)  9 (39)  0.46 (.12–1.68)  .24   High diastolic pressureb,c  3 (14)  4 (17)  0.75 (.15–3.81)  .73   Anyc  12 (55)  12 (52)  1.10 (.34–3.55)  .87  Overall no.           ≥1  17 (71)  14 (58)  1.73 (.52–5.74)  .37   ≥2  12 (50)  8 (33)  2.00 (.62–6.42)  .24  Sign  Cases, No. (%) (n = 24)  Controls, No. (%) (n = 24)  OR (95% CI)a  Pa  General           Pallor  1 (4)  3 (13)  0.30 (.03–3.16)  .32   Asthenia  2 (8)  2 (8)  1.00 (.13–7.75)  1.00   Icterus  0 (0)  1 (4)  …     Edema  2 (8)  1 (4)  2.09 (.18–24.7)  .56   Adenopathy  6 (25)  4 (17)  1.67 (.40–6.87)  .48   Any  10 (42)  8 (33)  1.43 (.44–4.62)  .55  Cutaneous           Tumefaction  3 (13)  2 (8)  1.57 (.24–10.4)  .64   Maculae, papulae, spots  8 (33)  6 (25)  1.50 (.43–5.26)  .53   Ulceration  1 (4)  0 (0)  …     Any cutaneous sign  11 (46)  7 (29)  2.05 (.62–6.76)  .24  Gastrointestinal           Hepatic pain  2 (8)  5 (21)  0.35 (.06–1.99)  .23   Hepatomegaly  2 (8)  3 (13)  0.64 (.10–4.20)  .64   Splenomegaly  3 (13)  2 (8)  1.57 (.24–10.4)  .64   Any  4 (17)  6 (25)  0.60 (.15–2.47)  .48  Neurological           Muscle weakness  2 (8)  1 (4)  2.09 (.18–24.73)  .56   Areflexia  1 (4)  0 (0)  …     Paralysis  0 (0)  0 (0)  …     Paresthesia/ dysesthesia  1 (4)  0 (0)  …     Any  3 (13)  1 (4)  3.29 (.32–34.08)  .32  Cardiorespiratory           Arrhythmia  1 (4)  0 (0)  …     Cardiac insufficiency  0 (0)  1 (4)  …     Cough  6 (25)  4 (17)  1.67 (.40–6.87)  .48   Other  5 (21)  2 (8)  2.89 (.50–16.7)  .23   High systolic pressureb,c  5 (23)  9 (39)  0.46 (.12–1.68)  .24   High diastolic pressureb,c  3 (14)  4 (17)  0.75 (.15–3.81)  .73   Anyc  12 (55)  12 (52)  1.10 (.34–3.55)  .87  Overall no.           ≥1  17 (71)  14 (58)  1.73 (.52–5.74)  .37   ≥2  12 (50)  8 (33)  2.00 (.62–6.42)  .24  Abbreviations: CI, confidence interval; OR, odds ratio. aThe association between simian foamy virus infection and clinical signs was analyzed by logistic regression. bHigh systolic and diastolic pressure was defined as >140 mm Hg and >90 mmHg, respectively. cData were missing for 2 cases and 1 control. View Large Table 2. Clinical Signs Among Cases and Controls Sign  Cases, No. (%) (n = 24)  Controls, No. (%) (n = 24)  OR (95% CI)a  Pa  General           Pallor  1 (4)  3 (13)  0.30 (.03–3.16)  .32   Asthenia  2 (8)  2 (8)  1.00 (.13–7.75)  1.00   Icterus  0 (0)  1 (4)  …     Edema  2 (8)  1 (4)  2.09 (.18–24.7)  .56   Adenopathy  6 (25)  4 (17)  1.67 (.40–6.87)  .48   Any  10 (42)  8 (33)  1.43 (.44–4.62)  .55  Cutaneous           Tumefaction  3 (13)  2 (8)  1.57 (.24–10.4)  .64   Maculae, papulae, spots  8 (33)  6 (25)  1.50 (.43–5.26)  .53   Ulceration  1 (4)  0 (0)  …     Any cutaneous sign  11 (46)  7 (29)  2.05 (.62–6.76)  .24  Gastrointestinal           Hepatic pain  2 (8)  5 (21)  0.35 (.06–1.99)  .23   Hepatomegaly  2 (8)  3 (13)  0.64 (.10–4.20)  .64   Splenomegaly  3 (13)  2 (8)  1.57 (.24–10.4)  .64   Any  4 (17)  6 (25)  0.60 (.15–2.47)  .48  Neurological           Muscle weakness  2 (8)  1 (4)  2.09 (.18–24.73)  .56   Areflexia  1 (4)  0 (0)  …     Paralysis  0 (0)  0 (0)  …     Paresthesia/ dysesthesia  1 (4)  0 (0)  …     Any  3 (13)  1 (4)  3.29 (.32–34.08)  .32  Cardiorespiratory           Arrhythmia  1 (4)  0 (0)  …     Cardiac insufficiency  0 (0)  1 (4)  …     Cough  6 (25)  4 (17)  1.67 (.40–6.87)  .48   Other  5 (21)  2 (8)  2.89 (.50–16.7)  .23   High systolic pressureb,c  5 (23)  9 (39)  0.46 (.12–1.68)  .24   High diastolic pressureb,c  3 (14)  4 (17)  0.75 (.15–3.81)  .73   Anyc  12 (55)  12 (52)  1.10 (.34–3.55)  .87  Overall no.           ≥1  17 (71)  14 (58)  1.73 (.52–5.74)  .37   ≥2  12 (50)  8 (33)  2.00 (.62–6.42)  .24  Sign  Cases, No. (%) (n = 24)  Controls, No. (%) (n = 24)  OR (95% CI)a  Pa  General           Pallor  1 (4)  3 (13)  0.30 (.03–3.16)  .32   Asthenia  2 (8)  2 (8)  1.00 (.13–7.75)  1.00   Icterus  0 (0)  1 (4)  …     Edema  2 (8)  1 (4)  2.09 (.18–24.7)  .56   Adenopathy  6 (25)  4 (17)  1.67 (.40–6.87)  .48   Any  10 (42)  8 (33)  1.43 (.44–4.62)  .55  Cutaneous           Tumefaction  3 (13)  2 (8)  1.57 (.24–10.4)  .64   Maculae, papulae, spots  8 (33)  6 (25)  1.50 (.43–5.26)  .53   Ulceration  1 (4)  0 (0)  …     Any cutaneous sign  11 (46)  7 (29)  2.05 (.62–6.76)  .24  Gastrointestinal           Hepatic pain  2 (8)  5 (21)  0.35 (.06–1.99)  .23   Hepatomegaly  2 (8)  3 (13)  0.64 (.10–4.20)  .64   Splenomegaly  3 (13)  2 (8)  1.57 (.24–10.4)  .64   Any  4 (17)  6 (25)  0.60 (.15–2.47)  .48  Neurological           Muscle weakness  2 (8)  1 (4)  2.09 (.18–24.73)  .56   Areflexia  1 (4)  0 (0)  …     Paralysis  0 (0)  0 (0)  …     Paresthesia/ dysesthesia  1 (4)  0 (0)  …     Any  3 (13)  1 (4)  3.29 (.32–34.08)  .32  Cardiorespiratory           Arrhythmia  1 (4)  0 (0)  …     Cardiac insufficiency  0 (0)  1 (4)  …     Cough  6 (25)  4 (17)  1.67 (.40–6.87)  .48   Other  5 (21)  2 (8)  2.89 (.50–16.7)  .23   High systolic pressureb,c  5 (23)  9 (39)  0.46 (.12–1.68)  .24   High diastolic pressureb,c  3 (14)  4 (17)  0.75 (.15–3.81)  .73   Anyc  12 (55)  12 (52)  1.10 (.34–3.55)  .87  Overall no.           ≥1  17 (71)  14 (58)  1.73 (.52–5.74)  .37   ≥2  12 (50)  8 (33)  2.00 (.62–6.42)  .24  Abbreviations: CI, confidence interval; OR, odds ratio. aThe association between simian foamy virus infection and clinical signs was analyzed by logistic regression. bHigh systolic and diastolic pressure was defined as >140 mm Hg and >90 mmHg, respectively. cData were missing for 2 cases and 1 control. View Large Cases Had Lower Hemoglobin Levels Than Controls Blood counts were performed for all participants (Table 3). Cases had significantly lower levels of hemoglobin than controls (median values, 12.7 g/dL in cases and 14.4 g/dL in controls; P = .01). The hematocrit was significantly lower in cases than controls (median, 37 vs 42%; P = .009). Erythrocyte counts were similar in cases and controls (4.2 × 103 cells/µL and 4.6 × 103 cells/µL, respectively; P = .14). Furthermore, cases had a lower mean corpuscular volume (88 vs 92 fL; P = .03) and mean corpuscular hemoglobin level (29.7 vs 31.1 pg; P = .03) than controls despite a similar mean corpuscular hemoglobin concentration (34.2% and 34.0%, respectively; P = .36). Cases had significantly lower median basophil counts (20 vs 46 cells/µL; P = .03) and percentages (0.3% vs 0.7%; P = .01) than controls. Other leukocyte counts and proportions were similar between the 2 groups (Table 3). Eosinophilia, defined as an eosinophil count >0.6 × 103 cells/µL, occurred in 42 of 48 participants. One case had a very high eosinophil count (9.4 × 103 cells/µL), and 1 case and 1 control had neutrophil to lymphocyte ratios >2.5. Table 3. Hematological Parameters Among Cases and Controls Parameter  Cases, Median (IQR) (n = 24)  Controls, Median (IQR) (n = 24)  Pa  Blood count analysis         Erythrocyte count, ×103 cells/µL  4.2 (3.9–4.6)  4.6 (4.3–4.9)  .14   Hemoglobin level, g/dL  12.7 (11.8–13.9)  14.4 (13.6–15.1)  .01   Hematocrit, %  37 (35–41)  42 (40–44)  .009   MCV, fL  88 (82–90)  92 (88–94)  .03   MCH          Level, pg  29.7 (27.4–30.8)  31.1 (29.9–31.9)  .03    Percentage of RBCs  34.2 (33.6–34.4)  34.0 (33.0–35.2)  .36   Leukocyte count, ×103 cells/µL  7.1 (6.2–8.4)  6.9 (6.1–8.5)  .72   Polynuclear cells          Count, ×103 cells/µL  2.4 (1.9–3.3)  2.5 (2.0–3.7)  .71    Percentage of WBCs  34.1 (26.3–38.3)  38.1 (29.8–47.2)  .11   Eosinophils          Count, ×103 cells/µL  1.6 (1.1–2.0)  1.5 (0.7–1.9)  .35    Percentage of WBCs  21.0 (15.0–27.2)  19.9 (9.2–23.4)  .12   Basophils          Count, cells/µL  20 (0–46)  46 (27–62)  .03    Percentage of WBCs  0.3 (0.0–0.7)  0.7 (0.3–1.1)  .01   Lymphocytes          Count, ×103 cells/µL  2.4 (1.9–3.6)  2.2 (1.9–2.9)  .12    Percentage of WBCs  35.0 (27.1–42.6)  33.8 (27.1–39.2)  .88   Monocytes          Count, ×103 cells/µL  0.5 (0.4–0.6)  0.5 (0.3–0.6)  .42    Percentage of WBCs  7.3 (5.7–8.0)  6.5 (5.1–7.9)  .41   Thrombocyte count, ×103 thrombocytes/µL  183 (158–234)  197 (141–237)  .26  Electrolyte analysis         Na+ level, mmol/L  140 (139–143)  142 (140–143)  .36   K+ level, mmol/L  4.2 (4.0–4.5)  4.1 (4.0–4.3)  .11   Cl− level, mmol/L  104 (101–106)  103 (100–105)  .23  Blood chemistry analysis         Glycemia level, g/L  0.79 (0.71–0.86)  0.82 (0.77–0.87)  .09   Urea level, g/L  0.31 (0.27–0.36)  0.23 (0.18–0.30)  .01   Creatinine level, mg/L  10 (9–12)  9 (8–9)  .03   Protein level, g/L  88 (76–93)  78 (74–82)  .05   Albumin level, g/L  44 (35–48)  38 (36–40)  .13   CPK level, IU/L  117 (78–202)  64 (48–107)  .02   Amylase level, IU/L  87 (59–123)  105 (77–130)  .33   Total bilirubin level, mg/L  5.0 (3.0–8.0)  10.6 (7.1–15.0)  .002   LDH level, IU/L  751 (655–844)  637 (561–770)  .03   AST level, IU/L  25 (22–33)  27 (24–36)  .32   ALT level, IU/l  19 (15–26)  26 (19–34)  .16  Parameter  Cases, Median (IQR) (n = 24)  Controls, Median (IQR) (n = 24)  Pa  Blood count analysis         Erythrocyte count, ×103 cells/µL  4.2 (3.9–4.6)  4.6 (4.3–4.9)  .14   Hemoglobin level, g/dL  12.7 (11.8–13.9)  14.4 (13.6–15.1)  .01   Hematocrit, %  37 (35–41)  42 (40–44)  .009   MCV, fL  88 (82–90)  92 (88–94)  .03   MCH          Level, pg  29.7 (27.4–30.8)  31.1 (29.9–31.9)  .03    Percentage of RBCs  34.2 (33.6–34.4)  34.0 (33.0–35.2)  .36   Leukocyte count, ×103 cells/µL  7.1 (6.2–8.4)  6.9 (6.1–8.5)  .72   Polynuclear cells          Count, ×103 cells/µL  2.4 (1.9–3.3)  2.5 (2.0–3.7)  .71    Percentage of WBCs  34.1 (26.3–38.3)  38.1 (29.8–47.2)  .11   Eosinophils          Count, ×103 cells/µL  1.6 (1.1–2.0)  1.5 (0.7–1.9)  .35    Percentage of WBCs  21.0 (15.0–27.2)  19.9 (9.2–23.4)  .12   Basophils          Count, cells/µL  20 (0–46)  46 (27–62)  .03    Percentage of WBCs  0.3 (0.0–0.7)  0.7 (0.3–1.1)  .01   Lymphocytes          Count, ×103 cells/µL  2.4 (1.9–3.6)  2.2 (1.9–2.9)  .12    Percentage of WBCs  35.0 (27.1–42.6)  33.8 (27.1–39.2)  .88   Monocytes          Count, ×103 cells/µL  0.5 (0.4–0.6)  0.5 (0.3–0.6)  .42    Percentage of WBCs  7.3 (5.7–8.0)  6.5 (5.1–7.9)  .41   Thrombocyte count, ×103 thrombocytes/µL  183 (158–234)  197 (141–237)  .26  Electrolyte analysis         Na+ level, mmol/L  140 (139–143)  142 (140–143)  .36   K+ level, mmol/L  4.2 (4.0–4.5)  4.1 (4.0–4.3)  .11   Cl− level, mmol/L  104 (101–106)  103 (100–105)  .23  Blood chemistry analysis         Glycemia level, g/L  0.79 (0.71–0.86)  0.82 (0.77–0.87)  .09   Urea level, g/L  0.31 (0.27–0.36)  0.23 (0.18–0.30)  .01   Creatinine level, mg/L  10 (9–12)  9 (8–9)  .03   Protein level, g/L  88 (76–93)  78 (74–82)  .05   Albumin level, g/L  44 (35–48)  38 (36–40)  .13   CPK level, IU/L  117 (78–202)  64 (48–107)  .02   Amylase level, IU/L  87 (59–123)  105 (77–130)  .33   Total bilirubin level, mg/L  5.0 (3.0–8.0)  10.6 (7.1–15.0)  .002   LDH level, IU/L  751 (655–844)  637 (561–770)  .03   AST level, IU/L  25 (22–33)  27 (24–36)  .32   ALT level, IU/l  19 (15–26)  26 (19–34)  .16  Abbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; CPK, creatine phosphokinase; IQR, interquartile range; LDH, lactate dehydrogenase; MCH, mean corpuscular hemoglobin; MCV, mean corpuscular volume; RBC, red blood cell; WBC, white blood cell. aBy the paired Wilcoxon signed rank test. Values ≤.05 denote statistically significant differences. View Large Table 3. Hematological Parameters Among Cases and Controls Parameter  Cases, Median (IQR) (n = 24)  Controls, Median (IQR) (n = 24)  Pa  Blood count analysis         Erythrocyte count, ×103 cells/µL  4.2 (3.9–4.6)  4.6 (4.3–4.9)  .14   Hemoglobin level, g/dL  12.7 (11.8–13.9)  14.4 (13.6–15.1)  .01   Hematocrit, %  37 (35–41)  42 (40–44)  .009   MCV, fL  88 (82–90)  92 (88–94)  .03   MCH          Level, pg  29.7 (27.4–30.8)  31.1 (29.9–31.9)  .03    Percentage of RBCs  34.2 (33.6–34.4)  34.0 (33.0–35.2)  .36   Leukocyte count, ×103 cells/µL  7.1 (6.2–8.4)  6.9 (6.1–8.5)  .72   Polynuclear cells          Count, ×103 cells/µL  2.4 (1.9–3.3)  2.5 (2.0–3.7)  .71    Percentage of WBCs  34.1 (26.3–38.3)  38.1 (29.8–47.2)  .11   Eosinophils          Count, ×103 cells/µL  1.6 (1.1–2.0)  1.5 (0.7–1.9)  .35    Percentage of WBCs  21.0 (15.0–27.2)  19.9 (9.2–23.4)  .12   Basophils          Count, cells/µL  20 (0–46)  46 (27–62)  .03    Percentage of WBCs  0.3 (0.0–0.7)  0.7 (0.3–1.1)  .01   Lymphocytes          Count, ×103 cells/µL  2.4 (1.9–3.6)  2.2 (1.9–2.9)  .12    Percentage of WBCs  35.0 (27.1–42.6)  33.8 (27.1–39.2)  .88   Monocytes          Count, ×103 cells/µL  0.5 (0.4–0.6)  0.5 (0.3–0.6)  .42    Percentage of WBCs  7.3 (5.7–8.0)  6.5 (5.1–7.9)  .41   Thrombocyte count, ×103 thrombocytes/µL  183 (158–234)  197 (141–237)  .26  Electrolyte analysis         Na+ level, mmol/L  140 (139–143)  142 (140–143)  .36   K+ level, mmol/L  4.2 (4.0–4.5)  4.1 (4.0–4.3)  .11   Cl− level, mmol/L  104 (101–106)  103 (100–105)  .23  Blood chemistry analysis         Glycemia level, g/L  0.79 (0.71–0.86)  0.82 (0.77–0.87)  .09   Urea level, g/L  0.31 (0.27–0.36)  0.23 (0.18–0.30)  .01   Creatinine level, mg/L  10 (9–12)  9 (8–9)  .03   Protein level, g/L  88 (76–93)  78 (74–82)  .05   Albumin level, g/L  44 (35–48)  38 (36–40)  .13   CPK level, IU/L  117 (78–202)  64 (48–107)  .02   Amylase level, IU/L  87 (59–123)  105 (77–130)  .33   Total bilirubin level, mg/L  5.0 (3.0–8.0)  10.6 (7.1–15.0)  .002   LDH level, IU/L  751 (655–844)  637 (561–770)  .03   AST level, IU/L  25 (22–33)  27 (24–36)  .32   ALT level, IU/l  19 (15–26)  26 (19–34)  .16  Parameter  Cases, Median (IQR) (n = 24)  Controls, Median (IQR) (n = 24)  Pa  Blood count analysis         Erythrocyte count, ×103 cells/µL  4.2 (3.9–4.6)  4.6 (4.3–4.9)  .14   Hemoglobin level, g/dL  12.7 (11.8–13.9)  14.4 (13.6–15.1)  .01   Hematocrit, %  37 (35–41)  42 (40–44)  .009   MCV, fL  88 (82–90)  92 (88–94)  .03   MCH          Level, pg  29.7 (27.4–30.8)  31.1 (29.9–31.9)  .03    Percentage of RBCs  34.2 (33.6–34.4)  34.0 (33.0–35.2)  .36   Leukocyte count, ×103 cells/µL  7.1 (6.2–8.4)  6.9 (6.1–8.5)  .72   Polynuclear cells          Count, ×103 cells/µL  2.4 (1.9–3.3)  2.5 (2.0–3.7)  .71    Percentage of WBCs  34.1 (26.3–38.3)  38.1 (29.8–47.2)  .11   Eosinophils          Count, ×103 cells/µL  1.6 (1.1–2.0)  1.5 (0.7–1.9)  .35    Percentage of WBCs  21.0 (15.0–27.2)  19.9 (9.2–23.4)  .12   Basophils          Count, cells/µL  20 (0–46)  46 (27–62)  .03    Percentage of WBCs  0.3 (0.0–0.7)  0.7 (0.3–1.1)  .01   Lymphocytes          Count, ×103 cells/µL  2.4 (1.9–3.6)  2.2 (1.9–2.9)  .12    Percentage of WBCs  35.0 (27.1–42.6)  33.8 (27.1–39.2)  .88   Monocytes          Count, ×103 cells/µL  0.5 (0.4–0.6)  0.5 (0.3–0.6)  .42    Percentage of WBCs  7.3 (5.7–8.0)  6.5 (5.1–7.9)  .41   Thrombocyte count, ×103 thrombocytes/µL  183 (158–234)  197 (141–237)  .26  Electrolyte analysis         Na+ level, mmol/L  140 (139–143)  142 (140–143)  .36   K+ level, mmol/L  4.2 (4.0–4.5)  4.1 (4.0–4.3)  .11   Cl− level, mmol/L  104 (101–106)  103 (100–105)  .23  Blood chemistry analysis         Glycemia level, g/L  0.79 (0.71–0.86)  0.82 (0.77–0.87)  .09   Urea level, g/L  0.31 (0.27–0.36)  0.23 (0.18–0.30)  .01   Creatinine level, mg/L  10 (9–12)  9 (8–9)  .03   Protein level, g/L  88 (76–93)  78 (74–82)  .05   Albumin level, g/L  44 (35–48)  38 (36–40)  .13   CPK level, IU/L  117 (78–202)  64 (48–107)  .02   Amylase level, IU/L  87 (59–123)  105 (77–130)  .33   Total bilirubin level, mg/L  5.0 (3.0–8.0)  10.6 (7.1–15.0)  .002   LDH level, IU/L  751 (655–844)  637 (561–770)  .03   AST level, IU/L  25 (22–33)  27 (24–36)  .32   ALT level, IU/l  19 (15–26)  26 (19–34)  .16  Abbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; CPK, creatine phosphokinase; IQR, interquartile range; LDH, lactate dehydrogenase; MCH, mean corpuscular hemoglobin; MCV, mean corpuscular volume; RBC, red blood cell; WBC, white blood cell. aBy the paired Wilcoxon signed rank test. Values ≤.05 denote statistically significant differences. View Large Levels of Several Biochemical Parameters Differed Between Cases and Controls Blood tests were performed to explore biochemical markers of the principal functions of the liver, kidneys, and blood. Cases had significantly higher median levels of urea (median, 0.31 vs 0.23 g/L; P = .01), creatinine (10.0 vs 9.0 mg/L; P = .05), protein (88 vs. 78 g/L; P = .05), creatine phosphokinase (117 vs 64 IU/L; P = .02), and lactate dehydrogenase (751 vs 637 IU/L; P = .03) than controls, whereas their total bilirubin levels were significantly lower (5.0 vs 10.6 mg/mL; P = .002). There were no differences in the levels of electrolytes, glycemia, hepatic, or pancreatic markers between cases and controls (Table 3). Hematological Parameters and SFV Infection: No Obvious Effect of Coinfection With Other Pathogens Within each ethnic group, all participants had similar living environments, occupations, modes of subsistence, and dietary habits. Most were probably chronic carriers of several pathogens. The possible confounding factors considered were common chronic viral infections (ie, HTLV-1 and HBV infection) and the rainy season, during which carriage of parasites, including malaria parasites, increases, with possible effects on hematological and biochemical parameters. HTLV-1 can be transmitted from NHPs to humans through bites [21]. In this study, 10 cases and 3 controls were infected with HTLV-1 (42% and 13%, respectively; adjusted odds ratio, 5.00 [95% confidence interval, 1.17–21.46]; P = .03). Adjustment for HTLV-1 infection did not modify the associations observed in univariate analyses, except that the association with lactate dehydrogenase level was slightly weaker and that with MCV was slightly stronger (Figure 1). Among cases, HTLV-1 infection was associated with a higher MCV (Supplementary Table 3) and may have diminished the association between a lower MCV and SFV infection. All but 1 participant was positive for anti-HBV core antibody, and 2 cases were positive for HBV surface antigen, ruling out HBV coinfection as a major bias in this study. Collection and analysis of blood was performed during the rainy season (August–October) for one third of participants (ie, 9 cases and 7 controls). Adjustment for sampling period did not modify the associations observed in univariate analyses (Figure 1). None of the participants had clinical signs of malaria at sampling. Most importantly, cases had lower bilirubin levels than controls, indicating that parasitic hemolysis was unlikely to be responsible for the lower hemoglobin levels observed. In conclusion, hematological differences between cases and controls were probably not affected by coinfection with pathogens commonly found in the studied population. Figure 1. View largeDownload slide Lack of impact of confounders on differences in hematological parameters between cases and controls. Separate logistic analyses were performed for the 11 blood parameters associated with simian foamy virus infection. Blood parameter levels were transformed into binary variables (based on whether the value was below versus above the median value). Results are presented as adjusted odds ratios (aORs) and 95% confidence intervals (CIs). The results from univariate analysis performed on the whole group are shown in black; those from multivariate analysis, including human T lymphotropic virus type 1 (HTLV-1) infection, are shown in dark grey; and those from multivariate analysis, including the rainy season, are shown in light grey. CPK, creatine phosphokinase; LDH, lactate dehydrogenase; MCH, mean corpuscular hemoglobin; MCV, mean corpuscular volume. Figure 1. View largeDownload slide Lack of impact of confounders on differences in hematological parameters between cases and controls. Separate logistic analyses were performed for the 11 blood parameters associated with simian foamy virus infection. Blood parameter levels were transformed into binary variables (based on whether the value was below versus above the median value). Results are presented as adjusted odds ratios (aORs) and 95% confidence intervals (CIs). The results from univariate analysis performed on the whole group are shown in black; those from multivariate analysis, including human T lymphotropic virus type 1 (HTLV-1) infection, are shown in dark grey; and those from multivariate analysis, including the rainy season, are shown in light grey. CPK, creatine phosphokinase; LDH, lactate dehydrogenase; MCH, mean corpuscular hemoglobin; MCV, mean corpuscular volume. Relevance of Observed Differences We performed additional analyses to investigate whether differences in blood markers between cases and controls could be medically relevant. First, we compared the frequencies of people in each group who had values outside of the normal range (reference ranges for men in Cameroon are presented in Supplementary Table 1). We observed significant differences between cases and controls for hemoglobin (<13.4 g/dL: 67% vs 17%, respectively; P = .001), bilirubin (>15 mg/L: 0% and 29%, respectively; P = .009), and protein (>85 g/L: 54% and 21%, respectively; P = .04) levels and basophil counts (<10 cells/µL: 42% and 4%, respectively; P = .004). We also observed mild anemia, according to the World Health Organization threshold (defined as a hemoglobin level of <13 g/dL), in 58% of cases and 17% of controls (P = .007). Three cases and 1 control had moderate anemia (defined as <11 g/dL), and no participant had severe anemia (<8 g/dL). The power of the statistical analyses was calculated for variables that differed significantly between cases and controls, with an α of 0.05 for a 2-sided test (Supplementary Table 4). The power of the tests performed for hematocrit and for hemoglobin, urea, and bilirubin levels were >0.85, and the power of the test performed for lactate dehydrogenase level was >0.70. We repeated the case-control analysis for participants aged <65 years, to exclude an age-related effect on the observed differences, and obtained results similar to those for the entire group (data not shown). Importantly, cases had still significantly lower hemoglobin levels than controls (median, 12.6 vs 14.4 g/dL; P < .01). For the 24 cases, the estimated duration of infection negatively correlated with total bilirubin levels (Spearman rho = −0.459; P = .02). However, bilirubin levels tended to negatively correlate with age, as well (Spearman rho = −0.375; P = .07). Blood SFV DNA levels were quantified for 23 cases [10] and did not correlate with hemoglobin, bilirubin, protein, or basophil levels (Supplementary Figure 1). Cases Had Higher Gamma Globulin and Lower Transferrin Levels Than Controls Blood tests and blood sampling for the biobank were performed on the same day for 12 cases, providing an opportunity to quantify additional biomarkers (Table 4). We selected 12 controls, matched for age and ethnicity. Gamma globulin levels were significantly higher in cases than controls (median, 25.0 vs 21.1 g/L; P = .004), as were beta-2 globulin levels (9.5 vs 8.8 g/L; P = .045). Transferrin levels were significantly lower in cases than in controls (median, 3.3 vs 3.6 g/L; P = .049). The powers of analyses were >0.80 and >0.90 for gamma globulin and transferrin levels, respectively (Supplementary Table 4). The levels of other plasma globulins, iron status–related molecules, and inflammatory markers did not differ significantly between cases and controls. Table 4. Plasma Analyte Levels Among Cases and Controls Analysis, Analytea  Cases, Median (IQR) (n = 12)  Controls, Median (IQR) (n = 12)  Pa  Protein electrophoresis         Alpha-1 globulin level, g/L  3.2 (3.0–3.8)  3.0 (2.8–3.5)  .50   Alpha-2 globulin level , g/L  5.9 (5.4–7.3)  6.1 (5.4–6.4)  .72   Beta-1 globulin level, g/L  4.5 (4.0–5.0)  4.2 (3.9–4.5)  .39   Beta-2 globulin level, g/L  9.5 (8.7–10.7)  8.8 (7.9–9.2)  .05   Gamma globulin level, g/L  25.0 (22.9–29.0)  21.1 (15.4–24.0)  .004  Iron regulation, organ function, inflammation   Ferritin level, ng/mL  108 (75–160)  112 (84–214)  .53   Transferrin level, g/L  3.3 (3.0–3.5)  3.6 (3.5–3.9)  .05   sTfR level, pg/mL  991 (702–1962)  1191 (975–1434)  1.00   Haptoglobin level, g/L  1.08 (0.24–3.16)  0.69 (0.22–1.69)  .65   Erythropoietin level, mIU/ mL  14.7 (8.3–17.4)  10.4 (7.4–11.7)  .15   Hepcidin level, ng/mL  1.6 (1.3–6.9)  1.8 (1.7–3.7)  .87   CRP level, mg/mL  2.1 (1.3–2.4)  0.7 (0.4–5.2)  .78   IL-6 level, pg/mL  2.3 (1.6–2.9)  1.9 (1.0–3.3)  .78   IL-18BPA level, ng/mL  52.1 (37.4–81.1)  47.1 (31.3–64.7)  .35  Analysis, Analytea  Cases, Median (IQR) (n = 12)  Controls, Median (IQR) (n = 12)  Pa  Protein electrophoresis         Alpha-1 globulin level, g/L  3.2 (3.0–3.8)  3.0 (2.8–3.5)  .50   Alpha-2 globulin level , g/L  5.9 (5.4–7.3)  6.1 (5.4–6.4)  .72   Beta-1 globulin level, g/L  4.5 (4.0–5.0)  4.2 (3.9–4.5)  .39   Beta-2 globulin level, g/L  9.5 (8.7–10.7)  8.8 (7.9–9.2)  .05   Gamma globulin level, g/L  25.0 (22.9–29.0)  21.1 (15.4–24.0)  .004  Iron regulation, organ function, inflammation   Ferritin level, ng/mL  108 (75–160)  112 (84–214)  .53   Transferrin level, g/L  3.3 (3.0–3.5)  3.6 (3.5–3.9)  .05   sTfR level, pg/mL  991 (702–1962)  1191 (975–1434)  1.00   Haptoglobin level, g/L  1.08 (0.24–3.16)  0.69 (0.22–1.69)  .65   Erythropoietin level, mIU/ mL  14.7 (8.3–17.4)  10.4 (7.4–11.7)  .15   Hepcidin level, ng/mL  1.6 (1.3–6.9)  1.8 (1.7–3.7)  .87   CRP level, mg/mL  2.1 (1.3–2.4)  0.7 (0.4–5.2)  .78   IL-6 level, pg/mL  2.3 (1.6–2.9)  1.9 (1.0–3.3)  .78   IL-18BPA level, ng/mL  52.1 (37.4–81.1)  47.1 (31.3–64.7)  .35  Measurements were performed on plasma samples stored at −80°C. Enzyme-linked immunosorbent assays are described in Supplementary Table 2 and were performed at the Unité d’épidémiologie et Physiopathologie des Virus Oncogènes, Institut Pasteur (Paris, France). Protein electrophoresis was performed at the the Laboratoire de Biologie Médicale Volontaires-Cerballiance (Paris). Cases and controls were matched for age and ethnicity, with plasma specimens collected and blood tests performed on the same day for each member of the pair. Abbreviations: CRP, C-reactive protein; IL-6, interleukin 6; IL-18BPA, IL-18 binding protein A; IQR, interquartile range; sTfR, soluble transferrin receptor. aBy the paired Wilcoxon signed rank test. Values ≤.05 denote statistically significant differences. View Large Table 4. Plasma Analyte Levels Among Cases and Controls Analysis, Analytea  Cases, Median (IQR) (n = 12)  Controls, Median (IQR) (n = 12)  Pa  Protein electrophoresis         Alpha-1 globulin level, g/L  3.2 (3.0–3.8)  3.0 (2.8–3.5)  .50   Alpha-2 globulin level , g/L  5.9 (5.4–7.3)  6.1 (5.4–6.4)  .72   Beta-1 globulin level, g/L  4.5 (4.0–5.0)  4.2 (3.9–4.5)  .39   Beta-2 globulin level, g/L  9.5 (8.7–10.7)  8.8 (7.9–9.2)  .05   Gamma globulin level, g/L  25.0 (22.9–29.0)  21.1 (15.4–24.0)  .004  Iron regulation, organ function, inflammation   Ferritin level, ng/mL  108 (75–160)  112 (84–214)  .53   Transferrin level, g/L  3.3 (3.0–3.5)  3.6 (3.5–3.9)  .05   sTfR level, pg/mL  991 (702–1962)  1191 (975–1434)  1.00   Haptoglobin level, g/L  1.08 (0.24–3.16)  0.69 (0.22–1.69)  .65   Erythropoietin level, mIU/ mL  14.7 (8.3–17.4)  10.4 (7.4–11.7)  .15   Hepcidin level, ng/mL  1.6 (1.3–6.9)  1.8 (1.7–3.7)  .87   CRP level, mg/mL  2.1 (1.3–2.4)  0.7 (0.4–5.2)  .78   IL-6 level, pg/mL  2.3 (1.6–2.9)  1.9 (1.0–3.3)  .78   IL-18BPA level, ng/mL  52.1 (37.4–81.1)  47.1 (31.3–64.7)  .35  Analysis, Analytea  Cases, Median (IQR) (n = 12)  Controls, Median (IQR) (n = 12)  Pa  Protein electrophoresis         Alpha-1 globulin level, g/L  3.2 (3.0–3.8)  3.0 (2.8–3.5)  .50   Alpha-2 globulin level , g/L  5.9 (5.4–7.3)  6.1 (5.4–6.4)  .72   Beta-1 globulin level, g/L  4.5 (4.0–5.0)  4.2 (3.9–4.5)  .39   Beta-2 globulin level, g/L  9.5 (8.7–10.7)  8.8 (7.9–9.2)  .05   Gamma globulin level, g/L  25.0 (22.9–29.0)  21.1 (15.4–24.0)  .004  Iron regulation, organ function, inflammation   Ferritin level, ng/mL  108 (75–160)  112 (84–214)  .53   Transferrin level, g/L  3.3 (3.0–3.5)  3.6 (3.5–3.9)  .05   sTfR level, pg/mL  991 (702–1962)  1191 (975–1434)  1.00   Haptoglobin level, g/L  1.08 (0.24–3.16)  0.69 (0.22–1.69)  .65   Erythropoietin level, mIU/ mL  14.7 (8.3–17.4)  10.4 (7.4–11.7)  .15   Hepcidin level, ng/mL  1.6 (1.3–6.9)  1.8 (1.7–3.7)  .87   CRP level, mg/mL  2.1 (1.3–2.4)  0.7 (0.4–5.2)  .78   IL-6 level, pg/mL  2.3 (1.6–2.9)  1.9 (1.0–3.3)  .78   IL-18BPA level, ng/mL  52.1 (37.4–81.1)  47.1 (31.3–64.7)  .35  Measurements were performed on plasma samples stored at −80°C. Enzyme-linked immunosorbent assays are described in Supplementary Table 2 and were performed at the Unité d’épidémiologie et Physiopathologie des Virus Oncogènes, Institut Pasteur (Paris, France). Protein electrophoresis was performed at the the Laboratoire de Biologie Médicale Volontaires-Cerballiance (Paris). Cases and controls were matched for age and ethnicity, with plasma specimens collected and blood tests performed on the same day for each member of the pair. Abbreviations: CRP, C-reactive protein; IL-6, interleukin 6; IL-18BPA, IL-18 binding protein A; IQR, interquartile range; sTfR, soluble transferrin receptor. aBy the paired Wilcoxon signed rank test. Values ≤.05 denote statistically significant differences. View Large DISCUSSION Foamy viruses were first recognized as a distinct subfamily among retroviruses almost 30 years ago [22]. Foamy viruses have been reported to be nonpathogenic, with rare exceptions in animal studies [23–25]. The search for associated diseases after isolation of a foamy virus from a human sample in 1971 led to conflicting results, most likely due to the lack of specificity of diagnostic assays [13, 26, 27]. Over the last 20 years, the assessment of infection in humans has been performed using validated diagnostic assays, and zoonotic transmission of SFV has been firmly demonstrated [12, 13, 28]. In our study, the infection status was established by the detection of both plasma SFV-specific antibodies and SFV DNA in blood cells. Having identified a series of SFV-infected humans in South Cameroon, we set up a case-control study investigating key human physiological functions. Cases and controls consisted of men who reported NHP-inflicted injuries during hunting, lived in the same or neighboring villages of South Cameroon, and were matched for age and ethnicity. We selected individuals infected with gorilla SFV, because this host-specific viral clade was the most frequently found among Central African hunters and because of the phylogenetic relatedness between humans and apes. There was no statistical association between clinical signs and SFV infection. However, levels of hemoglobin, basophils, urea, creatinine, protein, creatine phosphokinase, bilirubin, and lactate dehydrogenase differed significantly between cases and controls. One of these observations may be medically significant: 11 of 24 individuals infected with SFV had mild anemia (hemoglobin level, <13 g/dL), and 3 had moderate anemia (<11 g/dL). Our results raise several questions, including whether the differences between cases and controls are a consequence of SFV infection and, if so, what the underlying mechanism is for these di fferences, as well as how to evaluate the clinical implications of our findings in SFV-infected humans. The critical issue regarding the identification of biological markers that differ between SFV-infected and noninfected hunters is whether they are a consequence of SFV infection or another health-related factor. We performed a case-control study because it is the best-adapted method to study rare diseases with a long incubation period. We carefully considered selection biases during the recruitment of controls. Indeed, cases and controls shared high-risk exposure to NHP body fluids that are a source of SFV, and their SFV infections status was unknown at the time of inclusion in the initial epidemiological survey. For the present study, controls were matched to cases on the basis of age, ethnicity, and geographical proximity. Nevertheless, the statistical association between hemoglobin levels and SFV infection should be interpreted with caution because of the high rate of possible concurrent infections and comorbidities in the study population and because hemoglobin levels are affected by multiple factors. The aim of this study was to search for medical features associated with SFV infection and not to define the causes of an a priori unknown condition. Thus, we can only check for the absence of obvious causes of reduced hemoglobin levels. There was no evidence of chronic bleeding (ie, soluble transferrin receptor and ferritin values were normal, transferrin levels were low, and bleeding was not observed during clinical examination) or hemolysis (ie, bilirubin levels did not increase, and haptoglobin levels did not decrease). Some parasitic infections decrease hemoglobin levels by lysing red blood cells. However, cases had low levels of both hemoglobin and bilirubin, arguing against parasite-induced hemolysis. We found no differences in eosinophil levels between cases and controls, and the inclusion of rainy season as a confounder did not modify the statistical associations between blood parameters and SFV infection status (Figure 1). These results are consistent with adequate matching of cases and controls for environmental antigens. An alternative explanation for the low hemoglobin levels observed in cases is that genetic polymorphisms associated with hemoglobin synthesis also control susceptibility to SFV infection. Such polymorphisms are highly prevalent in Africa and affect the susceptibility to infection by several Plasmodium species and viruses, including HIV-1, or their severity [29–31]. Our preliminary data suggest 2 possible mechanisms by which SFV infection could cause anemia: inflammation and/or reduced red blood cell production. Anemia related to chronic disease is supported by low transferrin levels and high gamma globulin levels in cases and could be explained by the persistence of immune stimulation, because SFV is a potent inducer of type I interferon production in vitro [32]. Plasma soluble transferrin receptor levels, a marker of erythropoiesis, were similar for cases and controls, supporting a defect in red blood cell production. SFV infection of erythroid progenitors is a possible mechanism to account for lower levels of red blood cell production in cases. Furthermore, the prototype foamy virus of chimpanzee origin replicates in erythroblastoid cells [33]. Foamy virus can be recovered from most organs of infected animals [34]. Foamy virus DNA has been detected in bone marrow from infected monkeys [35] and cattle [36] but not cats [24]. The following retroviruses cause anemia by infecting cells of the erythroid lineage: feline leukemia virus [37], avian erythroblastosis virus [38], and Friend ecotropic murine leukemia virus [39]. Hemoglobin levels did not correlate with SFV DNA levels in blood cells. Such a correlation would have supported an effect of SFV infection on hemoglobin levels. However, the lack of such an association may reflect that the level of SFV DNA in blood cells is not an appropriate marker of SFV replication, owing to its ubiquitous cell and organ tropism [26], in contrast to HIV-1 and HTLV-1, for which the major target is CD4+ T lymphocytes. The clinical implications of our study for SFV-infected individuals are globally reassuring. One limitation of this study was the enrollment of apparently healthy individuals, which reduced the ability to detect potential cases of acute and/or severe SFV-associated disease. In addition, identifying symptoms associated with a chronic retroviral infection is challenging: for people infected with HIV-1 and HTLV-1, symptoms appear after year-long or decade-long incubation periods, are diverse, and affect only a fraction of infected individuals. This was a particular challenge in our study population, which came from rural areas and was of a low socioeconomic status, with limited access to healthcare, and which exhibited multiple clinical signs. Alcohol and tobacco consumption were frequent and not significantly different between cases and controls (data not shown). However, medications were rarely reported by participants in the study. In conclusion, we did not identify clinical signs specific to cases, but our results do not rule out the existence of pathological consequences of SFV infection, because we only studied apparently healthy individuals. The low prevalence of SFV infection and logistical challenges of such field studies are major obstacles to the replication of our results. The number of participants was also limited. Nevertheless, the power of analysis was >0.85 for 4 variables (hemoglobin, hematocrit, bilirubin, and urea levels). Recruiting 2 controls per case would still not have provided sufficient power to strengthen the conclusions for the other variables (Supplementary Table 4). These limitations are inherent to the situations in which SFV is transmitted to humans: ape bites are infrequent, and exposed hunters live in areas in which local healthcare structures are very modest and cannot provide medical or virological follow-up for such infections. In Western countries, blood tests have been performed for 7 SFV-infected individuals living in the United States, and no erythrocyte or hemoglobin values were reported to be out of the normal range [20]. However, the 7 individuals studied were infected with different SFV strains (chimpanzee and baboons) and were of different genetic background than those of our study population. Also, there was no control group. Thus, whether the hematological changes observed in gorilla SFV-infected individuals from Central Africa are due to infection with a SFV from a different NHP species, differences in living conditions, and/or differences in ethnic origin remains unknown. Several future studies based on our results are possible to establish the potential medical relevance of low hemoglobin levels and other hematological changes. First, the generic physical examination and blood tests initially performed in our study population could be repeated with the addition of anemia-specific investigations (eg, determination of reticulocyte counts, diagnostic testing for malaria parasites, and analysis of stool specimens for the presence of blood) and the assessment of comorbidities (eg, poor nutritional status and enteropathies). The prevalence of SFV infection could be investigated in specific groups, such as patients displaying clinical signs or anemia, in the context of a systematic hospital-based survey. Immunosuppressed individuals, specifically those infected with HIV-1, merit careful analysis, because coinfection with both retroviruses has been described in humans [40, 41] and because SFV worsen the consequences of simian immunodeficiency virus infection in macaques [25, 42]. Studies in animals infected with foamy viruses may allow the testing of some hypotheses, although pathogenesis may differ between natural and nonnatural hosts. In conclusion, the exposure of humans to SFV is ongoing worldwide, mostly in Central Africa, Asia, and South America. Our findings in Central African hunters may have implications for infected individuals. Further studies are warranted in other SFV-infected populations. Supplementary Data Supplementary materials are available at The Journal of Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author. Notes Acknowledgments. We thank the study participants; the Institut de Recherche pour le Développement, for their support for the field work; the staff of the Centre Pasteur du Cameroun; Dr Bernard Metogo (Centre des Urgences de Yaoundé); Dr Irène Onana Metogo (Hôpital Jamot de Yaoundé); Agnès Durand (Laboratoire de Biologie Médicale Volontaires-Cerballiance); and A. Fontanet, for statistical analysis. This text has been verified by a native English speaker. Disclaimer. The funding agencies had no role in the study design, generation of results, or writing of the manuscript. Financial support. This work was supported by the Institut Pasteur; the Programme Transversal de Recherche, Institut Pasteur (PTR 437); and the Agence Nationale de la Recherche (grant ANR-10-LABX-62-IBEID and REEMFOAMY project ANR-15-CE-15-0008-01). Potential conflicts of interest. O. H. reports receiving grants, personal fees, and other support from Ab science; grants from Celgene; personal fees from LFB; and grants from Inatherys, Hybrigenics, and Novartis; all grants, fees, and support were received for activities outside the scope of the submitted work. All other authors report no potential conflicts. The authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed. References 1. Taylor LH, Latham SM, Woolhouse ME. Risk factors for human disease emergence. Philos Trans R Soc Lond B Biol Sci  2001; 356: 983– 9. Google Scholar CrossRef Search ADS PubMed  2. Sharp PM, Hahn BH. Origins of HIV and the AIDS pandemic. Cold Spring Harb Perspect Med  2011; 1: a006841. Google Scholar CrossRef Search ADS PubMed  3. Peeters M, D’Arc M, Delaporte E. Origin and diversity of human retroviruses. AIDS Rev  2014; 16: 23– 34. Google Scholar PubMed  4. Gessain A, Cassar O. Epidemiological aspects and world distribution of HTLV-1 infection. Front Microbiol  2012; 3: 388. Google Scholar CrossRef Search ADS PubMed  5. Murray SM, Picker LJ, Axthelm MK, Hudkins K, Alpers CE, Linial ML. Replication in a superficial epithelial cell niche explains the lack of pathogenicity of primate foamy virus infections. J Virol  2008; 82: 5981– 5. Google Scholar CrossRef Search ADS PubMed  6. Heneine W, Switzer WM, Sandstrom Pet al.   Identification of a human population infected with simian foamy viruses. Nat Med  1998; 4: 403– 7. Google Scholar CrossRef Search ADS PubMed  7. Switzer WM, Bhullar V, Shanmugam Vet al.   Frequent simian foamy virus infection in persons occupationally exposed to nonhuman primates. J Virol  2004; 78: 2780– 9. Google Scholar CrossRef Search ADS PubMed  8. Wolfe ND, Switzer WM, Carr JKet al.   Naturally acquired simian retrovirus infections in central African hunters. Lancet  2004; 363: 932– 7. Google Scholar CrossRef Search ADS PubMed  9. Calattini S, Betsem EB, Froment Aet al.   Simian foamy virus transmission from apes to humans, rural Cameroon. Emerg Infect Dis  2007; 13: 1314– 20. Google Scholar CrossRef Search ADS PubMed  10. Betsem E, Rua R, Tortevoye P, Froment A, Gessain A. Frequent and recent human acquisition of simian foamy viruses through apes’ bites in central Africa. PLoS Pathog  2011; 7: e1002306. Google Scholar CrossRef Search ADS PubMed  11. Engel GA, Small CT, Soliven Ket al.   Zoonotic simian foamy virus in Bangladesh reflects diverse patterns of transmission and co-infection. Emerg Microbes Infect  2013; 2: e58. Google Scholar CrossRef Search ADS PubMed  12. Gessain A, Rua R, Betsem E, Turpin J, Mahieux R. HTLV-3/4 and simian foamy retroviruses in humans: discovery, epidemiology, cross-species transmission and molecular virology. Virology  2013; 435: 187– 99. Google Scholar CrossRef Search ADS PubMed  13. Pinto-Santini DM, Stenbak CR, Linial ML. Foamy virus zoonotic infections. Retrovirology  2017; 14: 55. Google Scholar CrossRef Search ADS PubMed  14. Schweizer M, Falcone V, Gänge J, Turek R, Neumann-Haefelin D. Simian foamy virus isolated from an accidentally infected human individual. J Virol  1997; 71: 4821– 4. Google Scholar PubMed  15. Brooks JI, Rud EW, Pilon RG, Smith JM, Switzer WM, Sandstrom PA. Cross-species retroviral transmission from macaques to human beings. Lancet  2002; 360: 387– 8. Google Scholar CrossRef Search ADS PubMed  16. Sandstrom PA, Phan KO, Switzer WMet al.   Simian foamy virus infection among zoo keepers. Lancet  2000; 355: 551– 2. Google Scholar CrossRef Search ADS PubMed  17. Mouinga-Ondémé A, Caron M, Nkoghé Det al.   Cross-species transmission of simian foamy virus to humans in rural Gabon, Central Africa. J Virol  2012; 86: 1255– 60. Google Scholar CrossRef Search ADS PubMed  18. Huang F, Wang H, Jing S, Zeng W. Simian foamy virus prevalence in Macaca mulatta and zookeepers. AIDS Res Hum Retroviruses  2012; 28: 591– 3. Google Scholar CrossRef Search ADS PubMed  19. Muniz CP, Cavalcante LTF, Jia Het al.   Zoonotic infection of Brazilian primate workers with New World simian foamy virus. PLoS One  2017; 12: e0184502. Google Scholar CrossRef Search ADS PubMed  20. Boneva RS, Switzer WM, Spira TJet al.   Clinical and virological characterization of persistent human infection with simian foamy viruses. AIDS Res Hum Retroviruses  2007; 23: 1330– 7. Google Scholar CrossRef Search ADS PubMed  21. Filippone C, Betsem E, Tortevoye Pet al.   A severe bite from a nonhuman primate is a major risk factor for HTLV-1 infection in hunters from Central Africa. Clin Infect Dis  2015; 60: 1667– 76. Google Scholar CrossRef Search ADS PubMed  22. Flügel RM, Rethwilm A, Maurer B, Darai G. Nucleotide sequence analysis of the env gene and its flanking regions of the human spumaretrovirus reveals two novel genes. EMBO J  1987; 6: 2077– 84. Google Scholar PubMed  23. Aguzzi A, Wagner EF, Netzer KO, Bothe K, Anhauser I, Rethwilm A. Human foamy virus proteins accumulate in neurons and induce multinucleated giant cells in the brain of transgenic mice. Am J Pathol  1993; 142: 1061– 71. Google Scholar PubMed  24. German AC, Harbour DA, Helps CR, Gruffydd-Jones TJ. Is feline foamy virus really apathogenic? Vet Immunol Immunopathol  2008; 123: 114– 8. Google Scholar CrossRef Search ADS PubMed  25. Choudhary A, Galvin TA, Williams DK, Beren J, Bryant MA, Khan AS. Influence of naturally occurring simian foamy viruses (SFVs) on SIV disease progression in the rhesus macaque (Macaca mulatta) model. Viruses  2013; 5: 1414– 30. Google Scholar CrossRef Search ADS PubMed  26. Meiering CD, Linial ML. Historical perspective of foamy virus epidemiology and infection. Clin Microbiol Rev  2001; 14: 165– 76. Google Scholar CrossRef Search ADS PubMed  27. Heneine W, Schweizer M, Sandstrom P, Folks T. Human infection with foamy viruses. Curr Top Microbiol Immunol  2003; 277: 181– 96. Google Scholar PubMed  28. Khan AS. Simian foamy virus infection in humans: prevalence and management. Expert Rev Anti Infect Ther  2009; 7: 569– 80. Google Scholar CrossRef Search ADS PubMed  29. Allison AC. Genetic control of resistance to human malaria. Curr Opin Immunol  2009; 21: 499– 505. Google Scholar CrossRef Search ADS PubMed  30. Taylor SM, Parobek CM, Fairhurst RM. Haemoglobinopathies and the clinical epidemiology of malaria: a systematic review and meta-analysis. Lancet Infect Dis  2012; 12: 457– 68. Google Scholar CrossRef Search ADS PubMed  31. Cooling L. Blood groups in infection and host susceptibility. Clin Microbiol Rev  2015; 28: 801– 70. Google Scholar CrossRef Search ADS PubMed  32. Rua R, Lepelley A, Gessain A, Schwartz O. Innate sensing of foamy viruses by human hematopoietic cells. J Virol  2012; 86: 909– 18. Google Scholar CrossRef Search ADS PubMed  33. Yu SF, Stone J, Linial ML. Productive persistent infection of hematopoietic cells by human foamy virus. J Virol  1996; 70: 1250– 4. Google Scholar PubMed  34. Mergia A, Heinkelein M. Foamy virus vectors. Curr Top Microbiol Immunol  2003; 277: 131– 59. Google Scholar PubMed  35. Falcone V, Leupold J, Clotten Jet al.   Sites of simian foamy virus persistence in naturally infected African green monkeys: latent provirus is ubiquitous, whereas viral replication is restricted to the oral mucosa. Virology  1999; 257: 7– 14. Google Scholar CrossRef Search ADS PubMed  36. Materniak M, Hechler T, Löchelt M, Kuzmak J. Similar patterns of infection with bovine foamy virus in experimentally inoculated calves and sheep. J Virol  2013; 87: 3516– 25. Google Scholar CrossRef Search ADS PubMed  37. Willett BJ, Hosie MJ. Feline leukaemia virus: half a century since its discovery. Vet J  2013; 195: 16– 23. Google Scholar CrossRef Search ADS PubMed  38. Graf T, Ade N, Beug H. Temperature-sensitive mutant of avian erythroblastosis virus suggests a block of differentiation as mechanism of leukaemogenesis. Nature  1978; 275: 496– 501. Google Scholar CrossRef Search ADS PubMed  39. Sitbon M, Sola B, Evans Let al.   Hemolytic anemia and erythroleukemia, two distinct pathogenic effects of Friend MuLV: mapping of the effects to different regions of the viral genome. Cell  1986; 47: 851– 9. Google Scholar CrossRef Search ADS PubMed  40. Switzer WM, Garcia AD, Yang Cet al.   Coinfection with HIV-1 and simian foamy virus in West Central Africans. J Infect Dis  2008; 197: 1389– 93. Google Scholar CrossRef Search ADS PubMed  41. Switzer WM, Tang S, Zheng Het al.   Dual simian foamy virus/human immunodeficiency virus type 1 infections in persons from Côte d’Ivoire. PLoS One  2016; 11: e0157709. Google Scholar CrossRef Search ADS PubMed  42. Murray SM, Picker LJ, Axthelm MK, Linial ML. Expanded tissue targets for foamy virus replication with simian immunodeficiency virus-induced immunosuppression. J Virol  2006; 80: 663– 70. Google Scholar CrossRef Search ADS PubMed  © The Author(s) 2018. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Journal of Infectious Diseases Oxford University Press

Clinical Signs and Blood Test Results Among Humans Infected With Zoonotic Simian Foamy Virus: A Case-Control Study

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© The Author(s) 2018. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.
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0022-1899
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10.1093/infdis/jiy181
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Abstract

Abstract Background A spillover of simian foamy virus (SFV) to humans, following bites from infected nonhuman primates (NHPs), is ongoing in exposed populations. These retroviruses establish persistent infections of unknown physiological consequences to the human host. Methods We performed a case-control study to compare 24 Cameroonian hunters infected with gorilla SFV and 24 controls matched for age and ethnicity. A complete physical examination and blood test were performed for all participants. Logistic regression and Wilcoxon signed rank tests were used to compare cases and controls. Results The cases had significantly lower levels of hemoglobin than the controls (median, 12.7 vs 14.4 g/dL; P = .01). Basophil levels were also significantly lower in cases than controls, with no differences for other leukocyte subsets. Cases had significantly higher urea, creatinine, protein, creatinine phosphokinase, and lactate dehydrogenase levels and lower bilirubin levels than controls. Cases and controls had similar frequencies of general, cutaneous, gastrointestinal, neurological, and cardiorespiratory signs. Conclusions The first case-control study of apparently healthy SFV-infected Cameroonian hunters showed the presence of hematological abnormalities. A thorough clinical and laboratory workup is now needed to establish the medical relevance of these observations because more than half of cases had mild or moderate anemia. Clinical Trials Registration NCT03225794. Retrovirus, zoonosis, hemoglobin, simian foamy virus Human diseases of zoonotic origin are a major and increasing public health problem [1]. Retroviruses are of particular concern because of their ability to integrate into the host DNA and persist in infected hosts. Human immunodeficiency virus type 1 (HIV-1) and human T-lymphotropic virus type 1 (HTLV-1) emerged from a simian reservoir through interspecies transmission from nonhuman primates (NHPs) infected with simian immunodeficiency virus and simian T-lymphotropic virus type 1, respectively [2, 3]. The initial cross-species transmission events occurred mainly in Central Africa. Currently, HIV-1 infects approximately 30 million people worldwide, whereas HTLV-1 infects at least 5–10 million people [2, 4], and both viruses are etiological agents of very severe diseases. Simian foamy viruses (SFVs) constitute a third group of complex retroviruses. SFVs replicate in the oral mucosa of NHPs [5] and are mostly transmitted to humans through bites [6, 7], establishing persistent infection in their new host. Such SFV spillover occurs frequently in individuals living in African and Southeast Asian countries in contact with NHPs [8–13] or in individuals exposed in a professional context [6, 7, 14–19]. Clinical examination of 8 SFV-infected people in Germany and North America did not reveal any common pathological features associated with this viral infection [14, 20]. However, these studies lacked uninfected controls. We recently reported that hunters of monkeys and apes living close to the rainforests of Central Africa constitute a high-risk group for zoonotic SFV infection, especially from gorillas [9, 10, 17, 21]. The objective of this case-control study was to compare clinical signs and hematological markers between SFV-infected and uninfected humans. Here, we show that the levels of several hematological markers differ between SFV-infected and uninfected individuals. METHODS Study Design and Participants The research was conducted in accordance with the Helsinki declaration. Ethics approval was obtained from the relevant authorities in Cameroon (National Ethics Committee and Ministry of Health) and France (Commission Nationale de l’Informatique et des Libertés, and Comité de protection des personnes Ile de France IV). This study was registered at Clinicaltrials.gov (available at: https://clinicaltrials.gov/ct2/show/NCT03225794/). All participants gave written informed consent. All participants were Cameroonian men. We previously performed a serological and molecular survey to assess the prevalence of foamy virus infection in people who reported contact with NHPs with a resulting wound [10]. For the present study, cases consisted of individuals who were infected with a gorilla SFV; all were recruited from participants of our former survey. Twenty-four of 33 potentially eligible participants [10] were present in their village at the time of the visit of the medical team and agreed to participate in the study. Other eligible participants had died or moved away. Each case was matched individually for age (±10 years) and ethnicity to 1 SFV-uninfected control, recruited from hunters who participated in the same survey and who lived in same or neighboring villages as cases [10]. SFV infection was defined by positive results of both Western blots (defined as detection of the p70-p74 Gag doublet) and polymerase chain reaction assays (defined as detection of the gene encoding integrase and/or the associated long terminal repeat) performed at the time of the epidemiological survey [10]. Results of SFV diagnostic tests were confirmed at least twice to be positive or negative. Anti–hepatitis B virus (HBV) core antibodies and HBV surface antigen were assessed with the Monolisa kits (catalog numbers 72315 and 72346; Bio-Rad, Marnes-La-Coquette, France). Immunoblot assays were used for the diagnosis of HIV-1 and HTLV-1/2 infection (LAV Blot1; catalog No. 72251, Biorad and HTLV Blot 2.4, MP Diagnostics). One HIV-1–infected control was excluded, and no case was infected with HIV-1. Ten cases and 3 controls were infected with HTLV-1. Clinical and Biological Evaluations A complete clinical examination by a board-certified physician was performed for each case and control at the Centre Pasteur du Cameroun (CPC), in Yaoundé. Blood tests were performed by the medical analysis laboratory at the CPC and are listed in Supplementary Table 1. In addition, blood specimens from 24 participants (12 cases and 12 age- and ethnicity-matched controls) were collected into tubes containing ethylenediaminetetraacetic acid. Plasma was isolated and stored at −80°C. Protein electrophoresis was performed by the Laboratoire de Biologie Médicale Volontaires-Cerballiance in Paris. Ferritin, transferrin, soluble transferrin receptor, haptoglobin, erythropoietin, hepcidin, C-reactive protein, interleukin 6, and interleukin 18 binding protein A levels were quantified by enzyme-linked immunosorbent assay (Supplementary Table 2). Statistical Analysis Logistic regression was performed to study differences in clinical signs between cases and controls. The levels of blood and plasma parameters were treated as quantitative variables, and the paired Wilcoxon signed-rank test was used to compare cases and controls. We used the Fisher exact test to compare frequencies of cases and controls with values outside the normal range, based on sex-specific reference values used in Cameroon (Supplementary Table 1). We used logistic regression to study the effect of confounders (HTLV-1 infection and sampling during the rainy season) on the associations between blood parameters and SFV infection status, with blood parameters transformed into binary variables (based on whether the value was below versus above the median value). We studied associations between hemoglobin levels and plasma biomarkers with the Spearman rank test. Data were analyzed with Stata 12.1 software (StataCorp). RESULTS Frequencies of Clinical Signs do not Differ Between Cases and Controls Cases and controls were bush-meat hunters who reported injuries, mostly bites inflicted during hunting activities (Table 1). Ages ranged from 22 to 67 years. All participants in the study were apparently healthy at the time of the analysis. We performed a complete clinical examination of all study participants. Overall, 65% had at least 1 clinical sign, and 42% had at least 2 clinical signs. General and cutaneous signs were each observed in 38% of participants; high blood pressure, in 31%; other cardiorespiratory signs, in 29%; gastrointestinal signs, in 21%; and neurologic signs, in 8%. Cases and controls had similar frequencies of clinical signs, whether considered individually, by affected organ, or globally (Table 2). Table 1. Demographic Characteristics of Study Participants   Cases  Controls  Pa  Ethnicity      1.00   Bantu  8  8     Pygmy  16  16    Age at sampling, y   Median (IQR)  52 (40–60)  52 (42–59)  .12   Range  27–75  22–67    Duration of infection, mob   Median (IQR)  15 (8–28)       Range  1–40      Wound      .23c   Bite  24  21     Scratch  0  1     Don’t remember  0  2    NHP      .001d   Gorilla  24  12     Chimpanzee  0  2     Monkey  0  7     Don’t remember  0  3      Cases  Controls  Pa  Ethnicity      1.00   Bantu  8  8     Pygmy  16  16    Age at sampling, y   Median (IQR)  52 (40–60)  52 (42–59)  .12   Range  27–75  22–67    Duration of infection, mob   Median (IQR)  15 (8–28)       Range  1–40      Wound      .23c   Bite  24  21     Scratch  0  1     Don’t remember  0  2    NHP      .001d   Gorilla  24  12     Chimpanzee  0  2     Monkey  0  7     Don’t remember  0  3    Data are no. of participants, unless otherwise indicated. Abbreviations: IQR, interquartile range; NHP, nonhuman primate. aBy the χ2 or Wilcoxon signed rank test. The value ≤.05 denotes a statistically significant difference. bEstimated as the time between the reported date of the wound inflicted by the gorilla and the sampling date. cFor comparison of bite vs no bite. dFor comparison of gorilla vs other NHPs. View Large Table 1. Demographic Characteristics of Study Participants   Cases  Controls  Pa  Ethnicity      1.00   Bantu  8  8     Pygmy  16  16    Age at sampling, y   Median (IQR)  52 (40–60)  52 (42–59)  .12   Range  27–75  22–67    Duration of infection, mob   Median (IQR)  15 (8–28)       Range  1–40      Wound      .23c   Bite  24  21     Scratch  0  1     Don’t remember  0  2    NHP      .001d   Gorilla  24  12     Chimpanzee  0  2     Monkey  0  7     Don’t remember  0  3      Cases  Controls  Pa  Ethnicity      1.00   Bantu  8  8     Pygmy  16  16    Age at sampling, y   Median (IQR)  52 (40–60)  52 (42–59)  .12   Range  27–75  22–67    Duration of infection, mob   Median (IQR)  15 (8–28)       Range  1–40      Wound      .23c   Bite  24  21     Scratch  0  1     Don’t remember  0  2    NHP      .001d   Gorilla  24  12     Chimpanzee  0  2     Monkey  0  7     Don’t remember  0  3    Data are no. of participants, unless otherwise indicated. Abbreviations: IQR, interquartile range; NHP, nonhuman primate. aBy the χ2 or Wilcoxon signed rank test. The value ≤.05 denotes a statistically significant difference. bEstimated as the time between the reported date of the wound inflicted by the gorilla and the sampling date. cFor comparison of bite vs no bite. dFor comparison of gorilla vs other NHPs. View Large Table 2. Clinical Signs Among Cases and Controls Sign  Cases, No. (%) (n = 24)  Controls, No. (%) (n = 24)  OR (95% CI)a  Pa  General           Pallor  1 (4)  3 (13)  0.30 (.03–3.16)  .32   Asthenia  2 (8)  2 (8)  1.00 (.13–7.75)  1.00   Icterus  0 (0)  1 (4)  …     Edema  2 (8)  1 (4)  2.09 (.18–24.7)  .56   Adenopathy  6 (25)  4 (17)  1.67 (.40–6.87)  .48   Any  10 (42)  8 (33)  1.43 (.44–4.62)  .55  Cutaneous           Tumefaction  3 (13)  2 (8)  1.57 (.24–10.4)  .64   Maculae, papulae, spots  8 (33)  6 (25)  1.50 (.43–5.26)  .53   Ulceration  1 (4)  0 (0)  …     Any cutaneous sign  11 (46)  7 (29)  2.05 (.62–6.76)  .24  Gastrointestinal           Hepatic pain  2 (8)  5 (21)  0.35 (.06–1.99)  .23   Hepatomegaly  2 (8)  3 (13)  0.64 (.10–4.20)  .64   Splenomegaly  3 (13)  2 (8)  1.57 (.24–10.4)  .64   Any  4 (17)  6 (25)  0.60 (.15–2.47)  .48  Neurological           Muscle weakness  2 (8)  1 (4)  2.09 (.18–24.73)  .56   Areflexia  1 (4)  0 (0)  …     Paralysis  0 (0)  0 (0)  …     Paresthesia/ dysesthesia  1 (4)  0 (0)  …     Any  3 (13)  1 (4)  3.29 (.32–34.08)  .32  Cardiorespiratory           Arrhythmia  1 (4)  0 (0)  …     Cardiac insufficiency  0 (0)  1 (4)  …     Cough  6 (25)  4 (17)  1.67 (.40–6.87)  .48   Other  5 (21)  2 (8)  2.89 (.50–16.7)  .23   High systolic pressureb,c  5 (23)  9 (39)  0.46 (.12–1.68)  .24   High diastolic pressureb,c  3 (14)  4 (17)  0.75 (.15–3.81)  .73   Anyc  12 (55)  12 (52)  1.10 (.34–3.55)  .87  Overall no.           ≥1  17 (71)  14 (58)  1.73 (.52–5.74)  .37   ≥2  12 (50)  8 (33)  2.00 (.62–6.42)  .24  Sign  Cases, No. (%) (n = 24)  Controls, No. (%) (n = 24)  OR (95% CI)a  Pa  General           Pallor  1 (4)  3 (13)  0.30 (.03–3.16)  .32   Asthenia  2 (8)  2 (8)  1.00 (.13–7.75)  1.00   Icterus  0 (0)  1 (4)  …     Edema  2 (8)  1 (4)  2.09 (.18–24.7)  .56   Adenopathy  6 (25)  4 (17)  1.67 (.40–6.87)  .48   Any  10 (42)  8 (33)  1.43 (.44–4.62)  .55  Cutaneous           Tumefaction  3 (13)  2 (8)  1.57 (.24–10.4)  .64   Maculae, papulae, spots  8 (33)  6 (25)  1.50 (.43–5.26)  .53   Ulceration  1 (4)  0 (0)  …     Any cutaneous sign  11 (46)  7 (29)  2.05 (.62–6.76)  .24  Gastrointestinal           Hepatic pain  2 (8)  5 (21)  0.35 (.06–1.99)  .23   Hepatomegaly  2 (8)  3 (13)  0.64 (.10–4.20)  .64   Splenomegaly  3 (13)  2 (8)  1.57 (.24–10.4)  .64   Any  4 (17)  6 (25)  0.60 (.15–2.47)  .48  Neurological           Muscle weakness  2 (8)  1 (4)  2.09 (.18–24.73)  .56   Areflexia  1 (4)  0 (0)  …     Paralysis  0 (0)  0 (0)  …     Paresthesia/ dysesthesia  1 (4)  0 (0)  …     Any  3 (13)  1 (4)  3.29 (.32–34.08)  .32  Cardiorespiratory           Arrhythmia  1 (4)  0 (0)  …     Cardiac insufficiency  0 (0)  1 (4)  …     Cough  6 (25)  4 (17)  1.67 (.40–6.87)  .48   Other  5 (21)  2 (8)  2.89 (.50–16.7)  .23   High systolic pressureb,c  5 (23)  9 (39)  0.46 (.12–1.68)  .24   High diastolic pressureb,c  3 (14)  4 (17)  0.75 (.15–3.81)  .73   Anyc  12 (55)  12 (52)  1.10 (.34–3.55)  .87  Overall no.           ≥1  17 (71)  14 (58)  1.73 (.52–5.74)  .37   ≥2  12 (50)  8 (33)  2.00 (.62–6.42)  .24  Abbreviations: CI, confidence interval; OR, odds ratio. aThe association between simian foamy virus infection and clinical signs was analyzed by logistic regression. bHigh systolic and diastolic pressure was defined as >140 mm Hg and >90 mmHg, respectively. cData were missing for 2 cases and 1 control. View Large Table 2. Clinical Signs Among Cases and Controls Sign  Cases, No. (%) (n = 24)  Controls, No. (%) (n = 24)  OR (95% CI)a  Pa  General           Pallor  1 (4)  3 (13)  0.30 (.03–3.16)  .32   Asthenia  2 (8)  2 (8)  1.00 (.13–7.75)  1.00   Icterus  0 (0)  1 (4)  …     Edema  2 (8)  1 (4)  2.09 (.18–24.7)  .56   Adenopathy  6 (25)  4 (17)  1.67 (.40–6.87)  .48   Any  10 (42)  8 (33)  1.43 (.44–4.62)  .55  Cutaneous           Tumefaction  3 (13)  2 (8)  1.57 (.24–10.4)  .64   Maculae, papulae, spots  8 (33)  6 (25)  1.50 (.43–5.26)  .53   Ulceration  1 (4)  0 (0)  …     Any cutaneous sign  11 (46)  7 (29)  2.05 (.62–6.76)  .24  Gastrointestinal           Hepatic pain  2 (8)  5 (21)  0.35 (.06–1.99)  .23   Hepatomegaly  2 (8)  3 (13)  0.64 (.10–4.20)  .64   Splenomegaly  3 (13)  2 (8)  1.57 (.24–10.4)  .64   Any  4 (17)  6 (25)  0.60 (.15–2.47)  .48  Neurological           Muscle weakness  2 (8)  1 (4)  2.09 (.18–24.73)  .56   Areflexia  1 (4)  0 (0)  …     Paralysis  0 (0)  0 (0)  …     Paresthesia/ dysesthesia  1 (4)  0 (0)  …     Any  3 (13)  1 (4)  3.29 (.32–34.08)  .32  Cardiorespiratory           Arrhythmia  1 (4)  0 (0)  …     Cardiac insufficiency  0 (0)  1 (4)  …     Cough  6 (25)  4 (17)  1.67 (.40–6.87)  .48   Other  5 (21)  2 (8)  2.89 (.50–16.7)  .23   High systolic pressureb,c  5 (23)  9 (39)  0.46 (.12–1.68)  .24   High diastolic pressureb,c  3 (14)  4 (17)  0.75 (.15–3.81)  .73   Anyc  12 (55)  12 (52)  1.10 (.34–3.55)  .87  Overall no.           ≥1  17 (71)  14 (58)  1.73 (.52–5.74)  .37   ≥2  12 (50)  8 (33)  2.00 (.62–6.42)  .24  Sign  Cases, No. (%) (n = 24)  Controls, No. (%) (n = 24)  OR (95% CI)a  Pa  General           Pallor  1 (4)  3 (13)  0.30 (.03–3.16)  .32   Asthenia  2 (8)  2 (8)  1.00 (.13–7.75)  1.00   Icterus  0 (0)  1 (4)  …     Edema  2 (8)  1 (4)  2.09 (.18–24.7)  .56   Adenopathy  6 (25)  4 (17)  1.67 (.40–6.87)  .48   Any  10 (42)  8 (33)  1.43 (.44–4.62)  .55  Cutaneous           Tumefaction  3 (13)  2 (8)  1.57 (.24–10.4)  .64   Maculae, papulae, spots  8 (33)  6 (25)  1.50 (.43–5.26)  .53   Ulceration  1 (4)  0 (0)  …     Any cutaneous sign  11 (46)  7 (29)  2.05 (.62–6.76)  .24  Gastrointestinal           Hepatic pain  2 (8)  5 (21)  0.35 (.06–1.99)  .23   Hepatomegaly  2 (8)  3 (13)  0.64 (.10–4.20)  .64   Splenomegaly  3 (13)  2 (8)  1.57 (.24–10.4)  .64   Any  4 (17)  6 (25)  0.60 (.15–2.47)  .48  Neurological           Muscle weakness  2 (8)  1 (4)  2.09 (.18–24.73)  .56   Areflexia  1 (4)  0 (0)  …     Paralysis  0 (0)  0 (0)  …     Paresthesia/ dysesthesia  1 (4)  0 (0)  …     Any  3 (13)  1 (4)  3.29 (.32–34.08)  .32  Cardiorespiratory           Arrhythmia  1 (4)  0 (0)  …     Cardiac insufficiency  0 (0)  1 (4)  …     Cough  6 (25)  4 (17)  1.67 (.40–6.87)  .48   Other  5 (21)  2 (8)  2.89 (.50–16.7)  .23   High systolic pressureb,c  5 (23)  9 (39)  0.46 (.12–1.68)  .24   High diastolic pressureb,c  3 (14)  4 (17)  0.75 (.15–3.81)  .73   Anyc  12 (55)  12 (52)  1.10 (.34–3.55)  .87  Overall no.           ≥1  17 (71)  14 (58)  1.73 (.52–5.74)  .37   ≥2  12 (50)  8 (33)  2.00 (.62–6.42)  .24  Abbreviations: CI, confidence interval; OR, odds ratio. aThe association between simian foamy virus infection and clinical signs was analyzed by logistic regression. bHigh systolic and diastolic pressure was defined as >140 mm Hg and >90 mmHg, respectively. cData were missing for 2 cases and 1 control. View Large Cases Had Lower Hemoglobin Levels Than Controls Blood counts were performed for all participants (Table 3). Cases had significantly lower levels of hemoglobin than controls (median values, 12.7 g/dL in cases and 14.4 g/dL in controls; P = .01). The hematocrit was significantly lower in cases than controls (median, 37 vs 42%; P = .009). Erythrocyte counts were similar in cases and controls (4.2 × 103 cells/µL and 4.6 × 103 cells/µL, respectively; P = .14). Furthermore, cases had a lower mean corpuscular volume (88 vs 92 fL; P = .03) and mean corpuscular hemoglobin level (29.7 vs 31.1 pg; P = .03) than controls despite a similar mean corpuscular hemoglobin concentration (34.2% and 34.0%, respectively; P = .36). Cases had significantly lower median basophil counts (20 vs 46 cells/µL; P = .03) and percentages (0.3% vs 0.7%; P = .01) than controls. Other leukocyte counts and proportions were similar between the 2 groups (Table 3). Eosinophilia, defined as an eosinophil count >0.6 × 103 cells/µL, occurred in 42 of 48 participants. One case had a very high eosinophil count (9.4 × 103 cells/µL), and 1 case and 1 control had neutrophil to lymphocyte ratios >2.5. Table 3. Hematological Parameters Among Cases and Controls Parameter  Cases, Median (IQR) (n = 24)  Controls, Median (IQR) (n = 24)  Pa  Blood count analysis         Erythrocyte count, ×103 cells/µL  4.2 (3.9–4.6)  4.6 (4.3–4.9)  .14   Hemoglobin level, g/dL  12.7 (11.8–13.9)  14.4 (13.6–15.1)  .01   Hematocrit, %  37 (35–41)  42 (40–44)  .009   MCV, fL  88 (82–90)  92 (88–94)  .03   MCH          Level, pg  29.7 (27.4–30.8)  31.1 (29.9–31.9)  .03    Percentage of RBCs  34.2 (33.6–34.4)  34.0 (33.0–35.2)  .36   Leukocyte count, ×103 cells/µL  7.1 (6.2–8.4)  6.9 (6.1–8.5)  .72   Polynuclear cells          Count, ×103 cells/µL  2.4 (1.9–3.3)  2.5 (2.0–3.7)  .71    Percentage of WBCs  34.1 (26.3–38.3)  38.1 (29.8–47.2)  .11   Eosinophils          Count, ×103 cells/µL  1.6 (1.1–2.0)  1.5 (0.7–1.9)  .35    Percentage of WBCs  21.0 (15.0–27.2)  19.9 (9.2–23.4)  .12   Basophils          Count, cells/µL  20 (0–46)  46 (27–62)  .03    Percentage of WBCs  0.3 (0.0–0.7)  0.7 (0.3–1.1)  .01   Lymphocytes          Count, ×103 cells/µL  2.4 (1.9–3.6)  2.2 (1.9–2.9)  .12    Percentage of WBCs  35.0 (27.1–42.6)  33.8 (27.1–39.2)  .88   Monocytes          Count, ×103 cells/µL  0.5 (0.4–0.6)  0.5 (0.3–0.6)  .42    Percentage of WBCs  7.3 (5.7–8.0)  6.5 (5.1–7.9)  .41   Thrombocyte count, ×103 thrombocytes/µL  183 (158–234)  197 (141–237)  .26  Electrolyte analysis         Na+ level, mmol/L  140 (139–143)  142 (140–143)  .36   K+ level, mmol/L  4.2 (4.0–4.5)  4.1 (4.0–4.3)  .11   Cl− level, mmol/L  104 (101–106)  103 (100–105)  .23  Blood chemistry analysis         Glycemia level, g/L  0.79 (0.71–0.86)  0.82 (0.77–0.87)  .09   Urea level, g/L  0.31 (0.27–0.36)  0.23 (0.18–0.30)  .01   Creatinine level, mg/L  10 (9–12)  9 (8–9)  .03   Protein level, g/L  88 (76–93)  78 (74–82)  .05   Albumin level, g/L  44 (35–48)  38 (36–40)  .13   CPK level, IU/L  117 (78–202)  64 (48–107)  .02   Amylase level, IU/L  87 (59–123)  105 (77–130)  .33   Total bilirubin level, mg/L  5.0 (3.0–8.0)  10.6 (7.1–15.0)  .002   LDH level, IU/L  751 (655–844)  637 (561–770)  .03   AST level, IU/L  25 (22–33)  27 (24–36)  .32   ALT level, IU/l  19 (15–26)  26 (19–34)  .16  Parameter  Cases, Median (IQR) (n = 24)  Controls, Median (IQR) (n = 24)  Pa  Blood count analysis         Erythrocyte count, ×103 cells/µL  4.2 (3.9–4.6)  4.6 (4.3–4.9)  .14   Hemoglobin level, g/dL  12.7 (11.8–13.9)  14.4 (13.6–15.1)  .01   Hematocrit, %  37 (35–41)  42 (40–44)  .009   MCV, fL  88 (82–90)  92 (88–94)  .03   MCH          Level, pg  29.7 (27.4–30.8)  31.1 (29.9–31.9)  .03    Percentage of RBCs  34.2 (33.6–34.4)  34.0 (33.0–35.2)  .36   Leukocyte count, ×103 cells/µL  7.1 (6.2–8.4)  6.9 (6.1–8.5)  .72   Polynuclear cells          Count, ×103 cells/µL  2.4 (1.9–3.3)  2.5 (2.0–3.7)  .71    Percentage of WBCs  34.1 (26.3–38.3)  38.1 (29.8–47.2)  .11   Eosinophils          Count, ×103 cells/µL  1.6 (1.1–2.0)  1.5 (0.7–1.9)  .35    Percentage of WBCs  21.0 (15.0–27.2)  19.9 (9.2–23.4)  .12   Basophils          Count, cells/µL  20 (0–46)  46 (27–62)  .03    Percentage of WBCs  0.3 (0.0–0.7)  0.7 (0.3–1.1)  .01   Lymphocytes          Count, ×103 cells/µL  2.4 (1.9–3.6)  2.2 (1.9–2.9)  .12    Percentage of WBCs  35.0 (27.1–42.6)  33.8 (27.1–39.2)  .88   Monocytes          Count, ×103 cells/µL  0.5 (0.4–0.6)  0.5 (0.3–0.6)  .42    Percentage of WBCs  7.3 (5.7–8.0)  6.5 (5.1–7.9)  .41   Thrombocyte count, ×103 thrombocytes/µL  183 (158–234)  197 (141–237)  .26  Electrolyte analysis         Na+ level, mmol/L  140 (139–143)  142 (140–143)  .36   K+ level, mmol/L  4.2 (4.0–4.5)  4.1 (4.0–4.3)  .11   Cl− level, mmol/L  104 (101–106)  103 (100–105)  .23  Blood chemistry analysis         Glycemia level, g/L  0.79 (0.71–0.86)  0.82 (0.77–0.87)  .09   Urea level, g/L  0.31 (0.27–0.36)  0.23 (0.18–0.30)  .01   Creatinine level, mg/L  10 (9–12)  9 (8–9)  .03   Protein level, g/L  88 (76–93)  78 (74–82)  .05   Albumin level, g/L  44 (35–48)  38 (36–40)  .13   CPK level, IU/L  117 (78–202)  64 (48–107)  .02   Amylase level, IU/L  87 (59–123)  105 (77–130)  .33   Total bilirubin level, mg/L  5.0 (3.0–8.0)  10.6 (7.1–15.0)  .002   LDH level, IU/L  751 (655–844)  637 (561–770)  .03   AST level, IU/L  25 (22–33)  27 (24–36)  .32   ALT level, IU/l  19 (15–26)  26 (19–34)  .16  Abbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; CPK, creatine phosphokinase; IQR, interquartile range; LDH, lactate dehydrogenase; MCH, mean corpuscular hemoglobin; MCV, mean corpuscular volume; RBC, red blood cell; WBC, white blood cell. aBy the paired Wilcoxon signed rank test. Values ≤.05 denote statistically significant differences. View Large Table 3. Hematological Parameters Among Cases and Controls Parameter  Cases, Median (IQR) (n = 24)  Controls, Median (IQR) (n = 24)  Pa  Blood count analysis         Erythrocyte count, ×103 cells/µL  4.2 (3.9–4.6)  4.6 (4.3–4.9)  .14   Hemoglobin level, g/dL  12.7 (11.8–13.9)  14.4 (13.6–15.1)  .01   Hematocrit, %  37 (35–41)  42 (40–44)  .009   MCV, fL  88 (82–90)  92 (88–94)  .03   MCH          Level, pg  29.7 (27.4–30.8)  31.1 (29.9–31.9)  .03    Percentage of RBCs  34.2 (33.6–34.4)  34.0 (33.0–35.2)  .36   Leukocyte count, ×103 cells/µL  7.1 (6.2–8.4)  6.9 (6.1–8.5)  .72   Polynuclear cells          Count, ×103 cells/µL  2.4 (1.9–3.3)  2.5 (2.0–3.7)  .71    Percentage of WBCs  34.1 (26.3–38.3)  38.1 (29.8–47.2)  .11   Eosinophils          Count, ×103 cells/µL  1.6 (1.1–2.0)  1.5 (0.7–1.9)  .35    Percentage of WBCs  21.0 (15.0–27.2)  19.9 (9.2–23.4)  .12   Basophils          Count, cells/µL  20 (0–46)  46 (27–62)  .03    Percentage of WBCs  0.3 (0.0–0.7)  0.7 (0.3–1.1)  .01   Lymphocytes          Count, ×103 cells/µL  2.4 (1.9–3.6)  2.2 (1.9–2.9)  .12    Percentage of WBCs  35.0 (27.1–42.6)  33.8 (27.1–39.2)  .88   Monocytes          Count, ×103 cells/µL  0.5 (0.4–0.6)  0.5 (0.3–0.6)  .42    Percentage of WBCs  7.3 (5.7–8.0)  6.5 (5.1–7.9)  .41   Thrombocyte count, ×103 thrombocytes/µL  183 (158–234)  197 (141–237)  .26  Electrolyte analysis         Na+ level, mmol/L  140 (139–143)  142 (140–143)  .36   K+ level, mmol/L  4.2 (4.0–4.5)  4.1 (4.0–4.3)  .11   Cl− level, mmol/L  104 (101–106)  103 (100–105)  .23  Blood chemistry analysis         Glycemia level, g/L  0.79 (0.71–0.86)  0.82 (0.77–0.87)  .09   Urea level, g/L  0.31 (0.27–0.36)  0.23 (0.18–0.30)  .01   Creatinine level, mg/L  10 (9–12)  9 (8–9)  .03   Protein level, g/L  88 (76–93)  78 (74–82)  .05   Albumin level, g/L  44 (35–48)  38 (36–40)  .13   CPK level, IU/L  117 (78–202)  64 (48–107)  .02   Amylase level, IU/L  87 (59–123)  105 (77–130)  .33   Total bilirubin level, mg/L  5.0 (3.0–8.0)  10.6 (7.1–15.0)  .002   LDH level, IU/L  751 (655–844)  637 (561–770)  .03   AST level, IU/L  25 (22–33)  27 (24–36)  .32   ALT level, IU/l  19 (15–26)  26 (19–34)  .16  Parameter  Cases, Median (IQR) (n = 24)  Controls, Median (IQR) (n = 24)  Pa  Blood count analysis         Erythrocyte count, ×103 cells/µL  4.2 (3.9–4.6)  4.6 (4.3–4.9)  .14   Hemoglobin level, g/dL  12.7 (11.8–13.9)  14.4 (13.6–15.1)  .01   Hematocrit, %  37 (35–41)  42 (40–44)  .009   MCV, fL  88 (82–90)  92 (88–94)  .03   MCH          Level, pg  29.7 (27.4–30.8)  31.1 (29.9–31.9)  .03    Percentage of RBCs  34.2 (33.6–34.4)  34.0 (33.0–35.2)  .36   Leukocyte count, ×103 cells/µL  7.1 (6.2–8.4)  6.9 (6.1–8.5)  .72   Polynuclear cells          Count, ×103 cells/µL  2.4 (1.9–3.3)  2.5 (2.0–3.7)  .71    Percentage of WBCs  34.1 (26.3–38.3)  38.1 (29.8–47.2)  .11   Eosinophils          Count, ×103 cells/µL  1.6 (1.1–2.0)  1.5 (0.7–1.9)  .35    Percentage of WBCs  21.0 (15.0–27.2)  19.9 (9.2–23.4)  .12   Basophils          Count, cells/µL  20 (0–46)  46 (27–62)  .03    Percentage of WBCs  0.3 (0.0–0.7)  0.7 (0.3–1.1)  .01   Lymphocytes          Count, ×103 cells/µL  2.4 (1.9–3.6)  2.2 (1.9–2.9)  .12    Percentage of WBCs  35.0 (27.1–42.6)  33.8 (27.1–39.2)  .88   Monocytes          Count, ×103 cells/µL  0.5 (0.4–0.6)  0.5 (0.3–0.6)  .42    Percentage of WBCs  7.3 (5.7–8.0)  6.5 (5.1–7.9)  .41   Thrombocyte count, ×103 thrombocytes/µL  183 (158–234)  197 (141–237)  .26  Electrolyte analysis         Na+ level, mmol/L  140 (139–143)  142 (140–143)  .36   K+ level, mmol/L  4.2 (4.0–4.5)  4.1 (4.0–4.3)  .11   Cl− level, mmol/L  104 (101–106)  103 (100–105)  .23  Blood chemistry analysis         Glycemia level, g/L  0.79 (0.71–0.86)  0.82 (0.77–0.87)  .09   Urea level, g/L  0.31 (0.27–0.36)  0.23 (0.18–0.30)  .01   Creatinine level, mg/L  10 (9–12)  9 (8–9)  .03   Protein level, g/L  88 (76–93)  78 (74–82)  .05   Albumin level, g/L  44 (35–48)  38 (36–40)  .13   CPK level, IU/L  117 (78–202)  64 (48–107)  .02   Amylase level, IU/L  87 (59–123)  105 (77–130)  .33   Total bilirubin level, mg/L  5.0 (3.0–8.0)  10.6 (7.1–15.0)  .002   LDH level, IU/L  751 (655–844)  637 (561–770)  .03   AST level, IU/L  25 (22–33)  27 (24–36)  .32   ALT level, IU/l  19 (15–26)  26 (19–34)  .16  Abbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; CPK, creatine phosphokinase; IQR, interquartile range; LDH, lactate dehydrogenase; MCH, mean corpuscular hemoglobin; MCV, mean corpuscular volume; RBC, red blood cell; WBC, white blood cell. aBy the paired Wilcoxon signed rank test. Values ≤.05 denote statistically significant differences. View Large Levels of Several Biochemical Parameters Differed Between Cases and Controls Blood tests were performed to explore biochemical markers of the principal functions of the liver, kidneys, and blood. Cases had significantly higher median levels of urea (median, 0.31 vs 0.23 g/L; P = .01), creatinine (10.0 vs 9.0 mg/L; P = .05), protein (88 vs. 78 g/L; P = .05), creatine phosphokinase (117 vs 64 IU/L; P = .02), and lactate dehydrogenase (751 vs 637 IU/L; P = .03) than controls, whereas their total bilirubin levels were significantly lower (5.0 vs 10.6 mg/mL; P = .002). There were no differences in the levels of electrolytes, glycemia, hepatic, or pancreatic markers between cases and controls (Table 3). Hematological Parameters and SFV Infection: No Obvious Effect of Coinfection With Other Pathogens Within each ethnic group, all participants had similar living environments, occupations, modes of subsistence, and dietary habits. Most were probably chronic carriers of several pathogens. The possible confounding factors considered were common chronic viral infections (ie, HTLV-1 and HBV infection) and the rainy season, during which carriage of parasites, including malaria parasites, increases, with possible effects on hematological and biochemical parameters. HTLV-1 can be transmitted from NHPs to humans through bites [21]. In this study, 10 cases and 3 controls were infected with HTLV-1 (42% and 13%, respectively; adjusted odds ratio, 5.00 [95% confidence interval, 1.17–21.46]; P = .03). Adjustment for HTLV-1 infection did not modify the associations observed in univariate analyses, except that the association with lactate dehydrogenase level was slightly weaker and that with MCV was slightly stronger (Figure 1). Among cases, HTLV-1 infection was associated with a higher MCV (Supplementary Table 3) and may have diminished the association between a lower MCV and SFV infection. All but 1 participant was positive for anti-HBV core antibody, and 2 cases were positive for HBV surface antigen, ruling out HBV coinfection as a major bias in this study. Collection and analysis of blood was performed during the rainy season (August–October) for one third of participants (ie, 9 cases and 7 controls). Adjustment for sampling period did not modify the associations observed in univariate analyses (Figure 1). None of the participants had clinical signs of malaria at sampling. Most importantly, cases had lower bilirubin levels than controls, indicating that parasitic hemolysis was unlikely to be responsible for the lower hemoglobin levels observed. In conclusion, hematological differences between cases and controls were probably not affected by coinfection with pathogens commonly found in the studied population. Figure 1. View largeDownload slide Lack of impact of confounders on differences in hematological parameters between cases and controls. Separate logistic analyses were performed for the 11 blood parameters associated with simian foamy virus infection. Blood parameter levels were transformed into binary variables (based on whether the value was below versus above the median value). Results are presented as adjusted odds ratios (aORs) and 95% confidence intervals (CIs). The results from univariate analysis performed on the whole group are shown in black; those from multivariate analysis, including human T lymphotropic virus type 1 (HTLV-1) infection, are shown in dark grey; and those from multivariate analysis, including the rainy season, are shown in light grey. CPK, creatine phosphokinase; LDH, lactate dehydrogenase; MCH, mean corpuscular hemoglobin; MCV, mean corpuscular volume. Figure 1. View largeDownload slide Lack of impact of confounders on differences in hematological parameters between cases and controls. Separate logistic analyses were performed for the 11 blood parameters associated with simian foamy virus infection. Blood parameter levels were transformed into binary variables (based on whether the value was below versus above the median value). Results are presented as adjusted odds ratios (aORs) and 95% confidence intervals (CIs). The results from univariate analysis performed on the whole group are shown in black; those from multivariate analysis, including human T lymphotropic virus type 1 (HTLV-1) infection, are shown in dark grey; and those from multivariate analysis, including the rainy season, are shown in light grey. CPK, creatine phosphokinase; LDH, lactate dehydrogenase; MCH, mean corpuscular hemoglobin; MCV, mean corpuscular volume. Relevance of Observed Differences We performed additional analyses to investigate whether differences in blood markers between cases and controls could be medically relevant. First, we compared the frequencies of people in each group who had values outside of the normal range (reference ranges for men in Cameroon are presented in Supplementary Table 1). We observed significant differences between cases and controls for hemoglobin (<13.4 g/dL: 67% vs 17%, respectively; P = .001), bilirubin (>15 mg/L: 0% and 29%, respectively; P = .009), and protein (>85 g/L: 54% and 21%, respectively; P = .04) levels and basophil counts (<10 cells/µL: 42% and 4%, respectively; P = .004). We also observed mild anemia, according to the World Health Organization threshold (defined as a hemoglobin level of <13 g/dL), in 58% of cases and 17% of controls (P = .007). Three cases and 1 control had moderate anemia (defined as <11 g/dL), and no participant had severe anemia (<8 g/dL). The power of the statistical analyses was calculated for variables that differed significantly between cases and controls, with an α of 0.05 for a 2-sided test (Supplementary Table 4). The power of the tests performed for hematocrit and for hemoglobin, urea, and bilirubin levels were >0.85, and the power of the test performed for lactate dehydrogenase level was >0.70. We repeated the case-control analysis for participants aged <65 years, to exclude an age-related effect on the observed differences, and obtained results similar to those for the entire group (data not shown). Importantly, cases had still significantly lower hemoglobin levels than controls (median, 12.6 vs 14.4 g/dL; P < .01). For the 24 cases, the estimated duration of infection negatively correlated with total bilirubin levels (Spearman rho = −0.459; P = .02). However, bilirubin levels tended to negatively correlate with age, as well (Spearman rho = −0.375; P = .07). Blood SFV DNA levels were quantified for 23 cases [10] and did not correlate with hemoglobin, bilirubin, protein, or basophil levels (Supplementary Figure 1). Cases Had Higher Gamma Globulin and Lower Transferrin Levels Than Controls Blood tests and blood sampling for the biobank were performed on the same day for 12 cases, providing an opportunity to quantify additional biomarkers (Table 4). We selected 12 controls, matched for age and ethnicity. Gamma globulin levels were significantly higher in cases than controls (median, 25.0 vs 21.1 g/L; P = .004), as were beta-2 globulin levels (9.5 vs 8.8 g/L; P = .045). Transferrin levels were significantly lower in cases than in controls (median, 3.3 vs 3.6 g/L; P = .049). The powers of analyses were >0.80 and >0.90 for gamma globulin and transferrin levels, respectively (Supplementary Table 4). The levels of other plasma globulins, iron status–related molecules, and inflammatory markers did not differ significantly between cases and controls. Table 4. Plasma Analyte Levels Among Cases and Controls Analysis, Analytea  Cases, Median (IQR) (n = 12)  Controls, Median (IQR) (n = 12)  Pa  Protein electrophoresis         Alpha-1 globulin level, g/L  3.2 (3.0–3.8)  3.0 (2.8–3.5)  .50   Alpha-2 globulin level , g/L  5.9 (5.4–7.3)  6.1 (5.4–6.4)  .72   Beta-1 globulin level, g/L  4.5 (4.0–5.0)  4.2 (3.9–4.5)  .39   Beta-2 globulin level, g/L  9.5 (8.7–10.7)  8.8 (7.9–9.2)  .05   Gamma globulin level, g/L  25.0 (22.9–29.0)  21.1 (15.4–24.0)  .004  Iron regulation, organ function, inflammation   Ferritin level, ng/mL  108 (75–160)  112 (84–214)  .53   Transferrin level, g/L  3.3 (3.0–3.5)  3.6 (3.5–3.9)  .05   sTfR level, pg/mL  991 (702–1962)  1191 (975–1434)  1.00   Haptoglobin level, g/L  1.08 (0.24–3.16)  0.69 (0.22–1.69)  .65   Erythropoietin level, mIU/ mL  14.7 (8.3–17.4)  10.4 (7.4–11.7)  .15   Hepcidin level, ng/mL  1.6 (1.3–6.9)  1.8 (1.7–3.7)  .87   CRP level, mg/mL  2.1 (1.3–2.4)  0.7 (0.4–5.2)  .78   IL-6 level, pg/mL  2.3 (1.6–2.9)  1.9 (1.0–3.3)  .78   IL-18BPA level, ng/mL  52.1 (37.4–81.1)  47.1 (31.3–64.7)  .35  Analysis, Analytea  Cases, Median (IQR) (n = 12)  Controls, Median (IQR) (n = 12)  Pa  Protein electrophoresis         Alpha-1 globulin level, g/L  3.2 (3.0–3.8)  3.0 (2.8–3.5)  .50   Alpha-2 globulin level , g/L  5.9 (5.4–7.3)  6.1 (5.4–6.4)  .72   Beta-1 globulin level, g/L  4.5 (4.0–5.0)  4.2 (3.9–4.5)  .39   Beta-2 globulin level, g/L  9.5 (8.7–10.7)  8.8 (7.9–9.2)  .05   Gamma globulin level, g/L  25.0 (22.9–29.0)  21.1 (15.4–24.0)  .004  Iron regulation, organ function, inflammation   Ferritin level, ng/mL  108 (75–160)  112 (84–214)  .53   Transferrin level, g/L  3.3 (3.0–3.5)  3.6 (3.5–3.9)  .05   sTfR level, pg/mL  991 (702–1962)  1191 (975–1434)  1.00   Haptoglobin level, g/L  1.08 (0.24–3.16)  0.69 (0.22–1.69)  .65   Erythropoietin level, mIU/ mL  14.7 (8.3–17.4)  10.4 (7.4–11.7)  .15   Hepcidin level, ng/mL  1.6 (1.3–6.9)  1.8 (1.7–3.7)  .87   CRP level, mg/mL  2.1 (1.3–2.4)  0.7 (0.4–5.2)  .78   IL-6 level, pg/mL  2.3 (1.6–2.9)  1.9 (1.0–3.3)  .78   IL-18BPA level, ng/mL  52.1 (37.4–81.1)  47.1 (31.3–64.7)  .35  Measurements were performed on plasma samples stored at −80°C. Enzyme-linked immunosorbent assays are described in Supplementary Table 2 and were performed at the Unité d’épidémiologie et Physiopathologie des Virus Oncogènes, Institut Pasteur (Paris, France). Protein electrophoresis was performed at the the Laboratoire de Biologie Médicale Volontaires-Cerballiance (Paris). Cases and controls were matched for age and ethnicity, with plasma specimens collected and blood tests performed on the same day for each member of the pair. Abbreviations: CRP, C-reactive protein; IL-6, interleukin 6; IL-18BPA, IL-18 binding protein A; IQR, interquartile range; sTfR, soluble transferrin receptor. aBy the paired Wilcoxon signed rank test. Values ≤.05 denote statistically significant differences. View Large Table 4. Plasma Analyte Levels Among Cases and Controls Analysis, Analytea  Cases, Median (IQR) (n = 12)  Controls, Median (IQR) (n = 12)  Pa  Protein electrophoresis         Alpha-1 globulin level, g/L  3.2 (3.0–3.8)  3.0 (2.8–3.5)  .50   Alpha-2 globulin level , g/L  5.9 (5.4–7.3)  6.1 (5.4–6.4)  .72   Beta-1 globulin level, g/L  4.5 (4.0–5.0)  4.2 (3.9–4.5)  .39   Beta-2 globulin level, g/L  9.5 (8.7–10.7)  8.8 (7.9–9.2)  .05   Gamma globulin level, g/L  25.0 (22.9–29.0)  21.1 (15.4–24.0)  .004  Iron regulation, organ function, inflammation   Ferritin level, ng/mL  108 (75–160)  112 (84–214)  .53   Transferrin level, g/L  3.3 (3.0–3.5)  3.6 (3.5–3.9)  .05   sTfR level, pg/mL  991 (702–1962)  1191 (975–1434)  1.00   Haptoglobin level, g/L  1.08 (0.24–3.16)  0.69 (0.22–1.69)  .65   Erythropoietin level, mIU/ mL  14.7 (8.3–17.4)  10.4 (7.4–11.7)  .15   Hepcidin level, ng/mL  1.6 (1.3–6.9)  1.8 (1.7–3.7)  .87   CRP level, mg/mL  2.1 (1.3–2.4)  0.7 (0.4–5.2)  .78   IL-6 level, pg/mL  2.3 (1.6–2.9)  1.9 (1.0–3.3)  .78   IL-18BPA level, ng/mL  52.1 (37.4–81.1)  47.1 (31.3–64.7)  .35  Analysis, Analytea  Cases, Median (IQR) (n = 12)  Controls, Median (IQR) (n = 12)  Pa  Protein electrophoresis         Alpha-1 globulin level, g/L  3.2 (3.0–3.8)  3.0 (2.8–3.5)  .50   Alpha-2 globulin level , g/L  5.9 (5.4–7.3)  6.1 (5.4–6.4)  .72   Beta-1 globulin level, g/L  4.5 (4.0–5.0)  4.2 (3.9–4.5)  .39   Beta-2 globulin level, g/L  9.5 (8.7–10.7)  8.8 (7.9–9.2)  .05   Gamma globulin level, g/L  25.0 (22.9–29.0)  21.1 (15.4–24.0)  .004  Iron regulation, organ function, inflammation   Ferritin level, ng/mL  108 (75–160)  112 (84–214)  .53   Transferrin level, g/L  3.3 (3.0–3.5)  3.6 (3.5–3.9)  .05   sTfR level, pg/mL  991 (702–1962)  1191 (975–1434)  1.00   Haptoglobin level, g/L  1.08 (0.24–3.16)  0.69 (0.22–1.69)  .65   Erythropoietin level, mIU/ mL  14.7 (8.3–17.4)  10.4 (7.4–11.7)  .15   Hepcidin level, ng/mL  1.6 (1.3–6.9)  1.8 (1.7–3.7)  .87   CRP level, mg/mL  2.1 (1.3–2.4)  0.7 (0.4–5.2)  .78   IL-6 level, pg/mL  2.3 (1.6–2.9)  1.9 (1.0–3.3)  .78   IL-18BPA level, ng/mL  52.1 (37.4–81.1)  47.1 (31.3–64.7)  .35  Measurements were performed on plasma samples stored at −80°C. Enzyme-linked immunosorbent assays are described in Supplementary Table 2 and were performed at the Unité d’épidémiologie et Physiopathologie des Virus Oncogènes, Institut Pasteur (Paris, France). Protein electrophoresis was performed at the the Laboratoire de Biologie Médicale Volontaires-Cerballiance (Paris). Cases and controls were matched for age and ethnicity, with plasma specimens collected and blood tests performed on the same day for each member of the pair. Abbreviations: CRP, C-reactive protein; IL-6, interleukin 6; IL-18BPA, IL-18 binding protein A; IQR, interquartile range; sTfR, soluble transferrin receptor. aBy the paired Wilcoxon signed rank test. Values ≤.05 denote statistically significant differences. View Large DISCUSSION Foamy viruses were first recognized as a distinct subfamily among retroviruses almost 30 years ago [22]. Foamy viruses have been reported to be nonpathogenic, with rare exceptions in animal studies [23–25]. The search for associated diseases after isolation of a foamy virus from a human sample in 1971 led to conflicting results, most likely due to the lack of specificity of diagnostic assays [13, 26, 27]. Over the last 20 years, the assessment of infection in humans has been performed using validated diagnostic assays, and zoonotic transmission of SFV has been firmly demonstrated [12, 13, 28]. In our study, the infection status was established by the detection of both plasma SFV-specific antibodies and SFV DNA in blood cells. Having identified a series of SFV-infected humans in South Cameroon, we set up a case-control study investigating key human physiological functions. Cases and controls consisted of men who reported NHP-inflicted injuries during hunting, lived in the same or neighboring villages of South Cameroon, and were matched for age and ethnicity. We selected individuals infected with gorilla SFV, because this host-specific viral clade was the most frequently found among Central African hunters and because of the phylogenetic relatedness between humans and apes. There was no statistical association between clinical signs and SFV infection. However, levels of hemoglobin, basophils, urea, creatinine, protein, creatine phosphokinase, bilirubin, and lactate dehydrogenase differed significantly between cases and controls. One of these observations may be medically significant: 11 of 24 individuals infected with SFV had mild anemia (hemoglobin level, <13 g/dL), and 3 had moderate anemia (<11 g/dL). Our results raise several questions, including whether the differences between cases and controls are a consequence of SFV infection and, if so, what the underlying mechanism is for these di fferences, as well as how to evaluate the clinical implications of our findings in SFV-infected humans. The critical issue regarding the identification of biological markers that differ between SFV-infected and noninfected hunters is whether they are a consequence of SFV infection or another health-related factor. We performed a case-control study because it is the best-adapted method to study rare diseases with a long incubation period. We carefully considered selection biases during the recruitment of controls. Indeed, cases and controls shared high-risk exposure to NHP body fluids that are a source of SFV, and their SFV infections status was unknown at the time of inclusion in the initial epidemiological survey. For the present study, controls were matched to cases on the basis of age, ethnicity, and geographical proximity. Nevertheless, the statistical association between hemoglobin levels and SFV infection should be interpreted with caution because of the high rate of possible concurrent infections and comorbidities in the study population and because hemoglobin levels are affected by multiple factors. The aim of this study was to search for medical features associated with SFV infection and not to define the causes of an a priori unknown condition. Thus, we can only check for the absence of obvious causes of reduced hemoglobin levels. There was no evidence of chronic bleeding (ie, soluble transferrin receptor and ferritin values were normal, transferrin levels were low, and bleeding was not observed during clinical examination) or hemolysis (ie, bilirubin levels did not increase, and haptoglobin levels did not decrease). Some parasitic infections decrease hemoglobin levels by lysing red blood cells. However, cases had low levels of both hemoglobin and bilirubin, arguing against parasite-induced hemolysis. We found no differences in eosinophil levels between cases and controls, and the inclusion of rainy season as a confounder did not modify the statistical associations between blood parameters and SFV infection status (Figure 1). These results are consistent with adequate matching of cases and controls for environmental antigens. An alternative explanation for the low hemoglobin levels observed in cases is that genetic polymorphisms associated with hemoglobin synthesis also control susceptibility to SFV infection. Such polymorphisms are highly prevalent in Africa and affect the susceptibility to infection by several Plasmodium species and viruses, including HIV-1, or their severity [29–31]. Our preliminary data suggest 2 possible mechanisms by which SFV infection could cause anemia: inflammation and/or reduced red blood cell production. Anemia related to chronic disease is supported by low transferrin levels and high gamma globulin levels in cases and could be explained by the persistence of immune stimulation, because SFV is a potent inducer of type I interferon production in vitro [32]. Plasma soluble transferrin receptor levels, a marker of erythropoiesis, were similar for cases and controls, supporting a defect in red blood cell production. SFV infection of erythroid progenitors is a possible mechanism to account for lower levels of red blood cell production in cases. Furthermore, the prototype foamy virus of chimpanzee origin replicates in erythroblastoid cells [33]. Foamy virus can be recovered from most organs of infected animals [34]. Foamy virus DNA has been detected in bone marrow from infected monkeys [35] and cattle [36] but not cats [24]. The following retroviruses cause anemia by infecting cells of the erythroid lineage: feline leukemia virus [37], avian erythroblastosis virus [38], and Friend ecotropic murine leukemia virus [39]. Hemoglobin levels did not correlate with SFV DNA levels in blood cells. Such a correlation would have supported an effect of SFV infection on hemoglobin levels. However, the lack of such an association may reflect that the level of SFV DNA in blood cells is not an appropriate marker of SFV replication, owing to its ubiquitous cell and organ tropism [26], in contrast to HIV-1 and HTLV-1, for which the major target is CD4+ T lymphocytes. The clinical implications of our study for SFV-infected individuals are globally reassuring. One limitation of this study was the enrollment of apparently healthy individuals, which reduced the ability to detect potential cases of acute and/or severe SFV-associated disease. In addition, identifying symptoms associated with a chronic retroviral infection is challenging: for people infected with HIV-1 and HTLV-1, symptoms appear after year-long or decade-long incubation periods, are diverse, and affect only a fraction of infected individuals. This was a particular challenge in our study population, which came from rural areas and was of a low socioeconomic status, with limited access to healthcare, and which exhibited multiple clinical signs. Alcohol and tobacco consumption were frequent and not significantly different between cases and controls (data not shown). However, medications were rarely reported by participants in the study. In conclusion, we did not identify clinical signs specific to cases, but our results do not rule out the existence of pathological consequences of SFV infection, because we only studied apparently healthy individuals. The low prevalence of SFV infection and logistical challenges of such field studies are major obstacles to the replication of our results. The number of participants was also limited. Nevertheless, the power of analysis was >0.85 for 4 variables (hemoglobin, hematocrit, bilirubin, and urea levels). Recruiting 2 controls per case would still not have provided sufficient power to strengthen the conclusions for the other variables (Supplementary Table 4). These limitations are inherent to the situations in which SFV is transmitted to humans: ape bites are infrequent, and exposed hunters live in areas in which local healthcare structures are very modest and cannot provide medical or virological follow-up for such infections. In Western countries, blood tests have been performed for 7 SFV-infected individuals living in the United States, and no erythrocyte or hemoglobin values were reported to be out of the normal range [20]. However, the 7 individuals studied were infected with different SFV strains (chimpanzee and baboons) and were of different genetic background than those of our study population. Also, there was no control group. Thus, whether the hematological changes observed in gorilla SFV-infected individuals from Central Africa are due to infection with a SFV from a different NHP species, differences in living conditions, and/or differences in ethnic origin remains unknown. Several future studies based on our results are possible to establish the potential medical relevance of low hemoglobin levels and other hematological changes. First, the generic physical examination and blood tests initially performed in our study population could be repeated with the addition of anemia-specific investigations (eg, determination of reticulocyte counts, diagnostic testing for malaria parasites, and analysis of stool specimens for the presence of blood) and the assessment of comorbidities (eg, poor nutritional status and enteropathies). The prevalence of SFV infection could be investigated in specific groups, such as patients displaying clinical signs or anemia, in the context of a systematic hospital-based survey. Immunosuppressed individuals, specifically those infected with HIV-1, merit careful analysis, because coinfection with both retroviruses has been described in humans [40, 41] and because SFV worsen the consequences of simian immunodeficiency virus infection in macaques [25, 42]. Studies in animals infected with foamy viruses may allow the testing of some hypotheses, although pathogenesis may differ between natural and nonnatural hosts. In conclusion, the exposure of humans to SFV is ongoing worldwide, mostly in Central Africa, Asia, and South America. Our findings in Central African hunters may have implications for infected individuals. Further studies are warranted in other SFV-infected populations. Supplementary Data Supplementary materials are available at The Journal of Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author. Notes Acknowledgments. We thank the study participants; the Institut de Recherche pour le Développement, for their support for the field work; the staff of the Centre Pasteur du Cameroun; Dr Bernard Metogo (Centre des Urgences de Yaoundé); Dr Irène Onana Metogo (Hôpital Jamot de Yaoundé); Agnès Durand (Laboratoire de Biologie Médicale Volontaires-Cerballiance); and A. Fontanet, for statistical analysis. This text has been verified by a native English speaker. Disclaimer. The funding agencies had no role in the study design, generation of results, or writing of the manuscript. Financial support. This work was supported by the Institut Pasteur; the Programme Transversal de Recherche, Institut Pasteur (PTR 437); and the Agence Nationale de la Recherche (grant ANR-10-LABX-62-IBEID and REEMFOAMY project ANR-15-CE-15-0008-01). Potential conflicts of interest. O. H. reports receiving grants, personal fees, and other support from Ab science; grants from Celgene; personal fees from LFB; and grants from Inatherys, Hybrigenics, and Novartis; all grants, fees, and support were received for activities outside the scope of the submitted work. All other authors report no potential conflicts. The authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed. References 1. Taylor LH, Latham SM, Woolhouse ME. Risk factors for human disease emergence. Philos Trans R Soc Lond B Biol Sci  2001; 356: 983– 9. Google Scholar CrossRef Search ADS PubMed  2. Sharp PM, Hahn BH. Origins of HIV and the AIDS pandemic. Cold Spring Harb Perspect Med  2011; 1: a006841. Google Scholar CrossRef Search ADS PubMed  3. Peeters M, D’Arc M, Delaporte E. Origin and diversity of human retroviruses. AIDS Rev  2014; 16: 23– 34. Google Scholar PubMed  4. Gessain A, Cassar O. Epidemiological aspects and world distribution of HTLV-1 infection. Front Microbiol  2012; 3: 388. Google Scholar CrossRef Search ADS PubMed  5. Murray SM, Picker LJ, Axthelm MK, Hudkins K, Alpers CE, Linial ML. Replication in a superficial epithelial cell niche explains the lack of pathogenicity of primate foamy virus infections. J Virol  2008; 82: 5981– 5. Google Scholar CrossRef Search ADS PubMed  6. Heneine W, Switzer WM, Sandstrom Pet al.   Identification of a human population infected with simian foamy viruses. Nat Med  1998; 4: 403– 7. Google Scholar CrossRef Search ADS PubMed  7. Switzer WM, Bhullar V, Shanmugam Vet al.   Frequent simian foamy virus infection in persons occupationally exposed to nonhuman primates. J Virol  2004; 78: 2780– 9. Google Scholar CrossRef Search ADS PubMed  8. Wolfe ND, Switzer WM, Carr JKet al.   Naturally acquired simian retrovirus infections in central African hunters. Lancet  2004; 363: 932– 7. Google Scholar CrossRef Search ADS PubMed  9. Calattini S, Betsem EB, Froment Aet al.   Simian foamy virus transmission from apes to humans, rural Cameroon. Emerg Infect Dis  2007; 13: 1314– 20. Google Scholar CrossRef Search ADS PubMed  10. Betsem E, Rua R, Tortevoye P, Froment A, Gessain A. Frequent and recent human acquisition of simian foamy viruses through apes’ bites in central Africa. PLoS Pathog  2011; 7: e1002306. Google Scholar CrossRef Search ADS PubMed  11. Engel GA, Small CT, Soliven Ket al.   Zoonotic simian foamy virus in Bangladesh reflects diverse patterns of transmission and co-infection. Emerg Microbes Infect  2013; 2: e58. Google Scholar CrossRef Search ADS PubMed  12. Gessain A, Rua R, Betsem E, Turpin J, Mahieux R. HTLV-3/4 and simian foamy retroviruses in humans: discovery, epidemiology, cross-species transmission and molecular virology. Virology  2013; 435: 187– 99. Google Scholar CrossRef Search ADS PubMed  13. Pinto-Santini DM, Stenbak CR, Linial ML. Foamy virus zoonotic infections. Retrovirology  2017; 14: 55. Google Scholar CrossRef Search ADS PubMed  14. Schweizer M, Falcone V, Gänge J, Turek R, Neumann-Haefelin D. Simian foamy virus isolated from an accidentally infected human individual. J Virol  1997; 71: 4821– 4. Google Scholar PubMed  15. Brooks JI, Rud EW, Pilon RG, Smith JM, Switzer WM, Sandstrom PA. Cross-species retroviral transmission from macaques to human beings. Lancet  2002; 360: 387– 8. Google Scholar CrossRef Search ADS PubMed  16. Sandstrom PA, Phan KO, Switzer WMet al.   Simian foamy virus infection among zoo keepers. Lancet  2000; 355: 551– 2. Google Scholar CrossRef Search ADS PubMed  17. Mouinga-Ondémé A, Caron M, Nkoghé Det al.   Cross-species transmission of simian foamy virus to humans in rural Gabon, Central Africa. J Virol  2012; 86: 1255– 60. Google Scholar CrossRef Search ADS PubMed  18. Huang F, Wang H, Jing S, Zeng W. Simian foamy virus prevalence in Macaca mulatta and zookeepers. AIDS Res Hum Retroviruses  2012; 28: 591– 3. Google Scholar CrossRef Search ADS PubMed  19. Muniz CP, Cavalcante LTF, Jia Het al.   Zoonotic infection of Brazilian primate workers with New World simian foamy virus. PLoS One  2017; 12: e0184502. Google Scholar CrossRef Search ADS PubMed  20. Boneva RS, Switzer WM, Spira TJet al.   Clinical and virological characterization of persistent human infection with simian foamy viruses. AIDS Res Hum Retroviruses  2007; 23: 1330– 7. Google Scholar CrossRef Search ADS PubMed  21. Filippone C, Betsem E, Tortevoye Pet al.   A severe bite from a nonhuman primate is a major risk factor for HTLV-1 infection in hunters from Central Africa. Clin Infect Dis  2015; 60: 1667– 76. Google Scholar CrossRef Search ADS PubMed  22. Flügel RM, Rethwilm A, Maurer B, Darai G. Nucleotide sequence analysis of the env gene and its flanking regions of the human spumaretrovirus reveals two novel genes. EMBO J  1987; 6: 2077– 84. Google Scholar PubMed  23. Aguzzi A, Wagner EF, Netzer KO, Bothe K, Anhauser I, Rethwilm A. Human foamy virus proteins accumulate in neurons and induce multinucleated giant cells in the brain of transgenic mice. Am J Pathol  1993; 142: 1061– 71. Google Scholar PubMed  24. German AC, Harbour DA, Helps CR, Gruffydd-Jones TJ. Is feline foamy virus really apathogenic? Vet Immunol Immunopathol  2008; 123: 114– 8. Google Scholar CrossRef Search ADS PubMed  25. Choudhary A, Galvin TA, Williams DK, Beren J, Bryant MA, Khan AS. Influence of naturally occurring simian foamy viruses (SFVs) on SIV disease progression in the rhesus macaque (Macaca mulatta) model. Viruses  2013; 5: 1414– 30. Google Scholar CrossRef Search ADS PubMed  26. Meiering CD, Linial ML. Historical perspective of foamy virus epidemiology and infection. Clin Microbiol Rev  2001; 14: 165– 76. Google Scholar CrossRef Search ADS PubMed  27. Heneine W, Schweizer M, Sandstrom P, Folks T. Human infection with foamy viruses. Curr Top Microbiol Immunol  2003; 277: 181– 96. Google Scholar PubMed  28. Khan AS. Simian foamy virus infection in humans: prevalence and management. Expert Rev Anti Infect Ther  2009; 7: 569– 80. Google Scholar CrossRef Search ADS PubMed  29. Allison AC. Genetic control of resistance to human malaria. Curr Opin Immunol  2009; 21: 499– 505. Google Scholar CrossRef Search ADS PubMed  30. Taylor SM, Parobek CM, Fairhurst RM. Haemoglobinopathies and the clinical epidemiology of malaria: a systematic review and meta-analysis. Lancet Infect Dis  2012; 12: 457– 68. Google Scholar CrossRef Search ADS PubMed  31. Cooling L. Blood groups in infection and host susceptibility. Clin Microbiol Rev  2015; 28: 801– 70. Google Scholar CrossRef Search ADS PubMed  32. Rua R, Lepelley A, Gessain A, Schwartz O. Innate sensing of foamy viruses by human hematopoietic cells. J Virol  2012; 86: 909– 18. Google Scholar CrossRef Search ADS PubMed  33. Yu SF, Stone J, Linial ML. Productive persistent infection of hematopoietic cells by human foamy virus. J Virol  1996; 70: 1250– 4. Google Scholar PubMed  34. Mergia A, Heinkelein M. Foamy virus vectors. Curr Top Microbiol Immunol  2003; 277: 131– 59. Google Scholar PubMed  35. Falcone V, Leupold J, Clotten Jet al.   Sites of simian foamy virus persistence in naturally infected African green monkeys: latent provirus is ubiquitous, whereas viral replication is restricted to the oral mucosa. Virology  1999; 257: 7– 14. Google Scholar CrossRef Search ADS PubMed  36. Materniak M, Hechler T, Löchelt M, Kuzmak J. Similar patterns of infection with bovine foamy virus in experimentally inoculated calves and sheep. J Virol  2013; 87: 3516– 25. Google Scholar CrossRef Search ADS PubMed  37. Willett BJ, Hosie MJ. Feline leukaemia virus: half a century since its discovery. Vet J  2013; 195: 16– 23. Google Scholar CrossRef Search ADS PubMed  38. Graf T, Ade N, Beug H. Temperature-sensitive mutant of avian erythroblastosis virus suggests a block of differentiation as mechanism of leukaemogenesis. Nature  1978; 275: 496– 501. Google Scholar CrossRef Search ADS PubMed  39. Sitbon M, Sola B, Evans Let al.   Hemolytic anemia and erythroleukemia, two distinct pathogenic effects of Friend MuLV: mapping of the effects to different regions of the viral genome. Cell  1986; 47: 851– 9. Google Scholar CrossRef Search ADS PubMed  40. Switzer WM, Garcia AD, Yang Cet al.   Coinfection with HIV-1 and simian foamy virus in West Central Africans. J Infect Dis  2008; 197: 1389– 93. Google Scholar CrossRef Search ADS PubMed  41. Switzer WM, Tang S, Zheng Het al.   Dual simian foamy virus/human immunodeficiency virus type 1 infections in persons from Côte d’Ivoire. PLoS One  2016; 11: e0157709. Google Scholar CrossRef Search ADS PubMed  42. Murray SM, Picker LJ, Axthelm MK, Linial ML. Expanded tissue targets for foamy virus replication with simian immunodeficiency virus-induced immunosuppression. J Virol  2006; 80: 663– 70. Google Scholar CrossRef Search ADS PubMed  © The Author(s) 2018. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

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Published: Mar 28, 2018

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