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Table 1 Bivariate analysis of malaria predictors and clinical malaria

From: Evaluating the predictive performance of malaria antibodies and FCGR3B gene polymorphisms on Plasmodium falciparum infection outcome: a prospective cohort study

Predictor Levels Protected Susceptible Combined p-value
N = 342 N = 53 N = 395
Age in years   5(3,8) 5 (3,7) 5 (3,8) 0.12e
Mosquito net use No 60% (204) 58% (31) 59% (235) 0.87f
Blood group A 18% (63) 19% (10) 18% (73) 0.93f
AB 6% (22) 8% (4) 7% (26)  
B 29% (98) 25% (13) 28% (111)  
O 46% (159) 49% (26) 47% (185)  
Sickle cell status Positive 15% (51) 19% (10) 15% (61) 0.46f
Parasite count(categorized) positive 7% (25) 4% (2) 7% (27) 0.34f
Haemoglobin at enrollment(gram per dL)   12 (11,13) 11 (11,12) 12 (11,13) 0.11e
Additive c.108C>G CC 29% (100) 26% (14) 29% (114) 0.4f
CG 42% (144) 36% (19) 41% (163)  
GG 29% (98) 38% (20) 30% (118)  
Additive c.114T>C CC 27% (92) 26% (14) 27% (106) 0.82f
CT 43% (147) 47% (25) 44% (172)  
TT 30% (103) 26% (14) 30% (117)  
Additive c.233C>A AA 9% (31) 2% (1) 8% (32) 0:2f
AC 27% (93) 28% (15) 27% (108)  
CC 64% (218) 70% (37) 65% (255)  
Additive c.244A>G GG 31% (106) 32% (17) 31% (123) 0.36f
AG 39% (134) 47% (25) 40% (159)  
AA 30% (102) 21% (11) 29%(113)  
Additive c.316A>G GG 13% (43) 17% (9) 13% (52) 0.65f
AG 32% (109) 32% (17) 32% (126)  
AA 56% (190) 51% (27) 55% (217)  
Additive c.194A>G GG 39% (135) 30% (16) 38% (151) 0.43f
AG 39% (135) 45% (24) 40% (159)  
AA 21% (72) 25% (13) 22% (85)  
Dominant c.108C>G CC vs CG-GG 71% (244) 62% (33) 70%(277) 0:18f
Dominant c.114T>C TT vs CT-CC 73% (250) 74% (39) 73% (289) 0:94f
Dominant c.194A>G AA vs AG-GG 61% (207) 70% (37) 62% (244) 0:2f
Dominant c.233C>A CC vs AC-AA 91% (311) 98% (52) 92% (363) 0:075f
Dominant c.244A>G AA vs AG-GG 69% (236) 68% (36) 69% (272) 0:87f
Dominant c.316A>G AA vs AG-GG 87% (299) 83% (44) 87% (343) 0:38f
Recessive c.108C>G CC-CG vs GG 29%(100) 26% (14) 29% (114) 0:67f
Recessive c.114T>C TT-CT vs CC 30% (103) 26% (14) 30% (117) 0:58f
Recessive c.194A>G AA-AG vs GG 21% (72) 25% (13) 22% (85) 0:57f
Recessive c.233C>A CC-AC vs AA 64% (218) 70% (37) 65% (255) 0:39f
Recessive c.244A>G AA-AG vs GG 30% (102) 21% (11) 29% (113) 0:17f
Recessive c.316A>G AA-AG vs GG 56% (190) 51% (27) 55% (217) 0:53f
log.IgG-MSP1   2.2 (1.6,3.7) 2.4 (1.6,3.2) 2.3 (1.6,3.6) 0:77e
log.IgG1-MSP1   3.2 (2.4,5.4) 3.1 (2.6,4.4) 3.2 (2.4,5.4) 0:6e
log.IgG2-MSP1   1.9 (1.6,2.8) 2.0 (1.6,2.7) 1.9 (1.6,2.8) 0:71e
log.IgG3-MSP1   3.3 (1.9,6.0) 3.1 (2.0,6.0) 3.3 (1.9,6.0) 0:83e
log.IgG4-MSP1   1.6 (1.4,2.0) 1.6 (1.3,1.9) 1.6 (1.4,2.0) 0:64e
log.IgG-MSP3   3.2 (2.5,4.6) 3.2 (2.5,4.8) 3.2 (2.5,4.6) 0:66e
log.IgG-1MSP3   3.2 (2.6,4.7) 2.9 (2.6,4.2) 3.2 (2.6,4.7) 0:36e
log.IgG-2MSP3   1.8 (1.6,2.2) 1.8 (1.5,2.1) 1.8 (1.6,2.2) 0:52e
log.IgG-3MSP3   2.7 (1.8,4.5) 2.6 (2.0,4.1) 2.7 (1.8,4.5) 0:89e
log.IgG-4MSP3   1.7 (1.4,2.1) 1.7 (1.5,2.1) 1.7(1.4,2.1) 0:82e
log.IgG-GLURPR0   3.4 (2.4,4.7) 3.8 (2.6,4.8) 3.4 (2.4,4.7) 0:36e
log.IgG1-GLURPR0   3.4 (2.5,4.9) 3.7 (2.7,5.1) 3.4 (2.5,4.9) 0:40e
log.IgG2-GLURPR0   1.9(1.6,2.3) 1.8(1.6,2.9) 1.9 (1.6,2.4) 0:61e
log.IgG3-GLURPR0   2.1 (1.6,3.4) 2.0 (1.7,2.7) 2.1 (1.6,3.4) 0:78e
log.IgG4-GLURPR0   1.5 (1.3,1.7) 1.5 (1.2,1.7) 1.5 (1.3,1.7) 0:44e
log.IgG-GLURPR2   3.9 (2.2,5.9) 4.2 (2.8,6.0) 4.0 (2.2,5.9) 0:26e
log.IgG1-GLURPR2   5.8 (3.8,8.0) 5.8 (4.3,7.5) 5.8 (3.9,8.0) 0:93e
log.IgG2-GLURPR2   3.2 (2.1,6.3) 2.9 (2.1,6.4) 3.1 (2.1,6.3) 0:56e
log.IgG3-GLURPR2   5.9 (3.5,7.8) 6.1 (4.1,7.3) 5.9 (3.5,7.6) 0:96e
log.IgG4-GLURPR2   2.1 (1.8,3.1) 2.1 (1.8,2.4) 2.1 (1.8,2.9) 0:62e
log.igG-AMA1   6.2 (3.6,9.2) 6.4 (4.5,8.4) 6.7 (3.8,9.1) 0:45e
log.IgG1-AMA1   9.5 (6.2,10.2) 8.9 (6.6,10.0) 9.4 (6.3,10.2) 0:61e
log.IgG2-AMA1   3.2 (2.1,4.5) 2.9 (2.1,4.2) 3.2 (2.1,4.4) 0:57e
log.IgG3-AMA1   4.7 (3.1,6.4) 4.8 (3.2,6.8) 4.7 (3.2,6.5) 0:54e
log.igG4-AMA1   4.0 (2.5,5.1) 3.6 (2.9,4.7) 3.9 (2.6,5.1) 0:62e
  1. a (b, c), represent the median, lower quartile, and the upper quartile for continuous variables.\( e \)-Wilcoxon Ranksum test, \( f \)-Fishers Exact Test. Numbers after percents are frequencies. Additive model: assumes the risk associated with an allele is increased r-fold for heterozygotes and 2r-fold for homozygote; Dominant model: assumes risk association with the dominant allele and compares homozygous wild type with a combination of the heterozygous and homozygous for the variant; Recessive model: assesses risk association with the recessive allele and compares homozygous variant type with a combination of the heterozygous and homozygous for the wild type. Tests used: Wilcoxon test; Pearson test, MSP: Merozoite surface protein, GLURP: Glutamate Rich Protein, AMA: Apical membrane antigen. Note: natural log transformation was used