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© 2016 American Medical Association. All rights reserved.
Supplementary Online Content
Lotta LA, Sharp SJ, Burgess S, et al. Association between low-density lipoprotein cholesterol–lowering genetic variants and risk of type 2 diabetes: a meta-analysis. JAMA. doi:10.1001/jama.2016.14568
The EPIC-InterAct Consortium
eTable 1. Participating studies
eTable 2. Genetic variants included in the main analysis
eTable 3. Sensitivity analyses at the NPC1L1 and PCSK9 loci
eTable 4. Correlation between genetic variants
eTable 5. Burden of rare alleles in exome sequencing studies
eFigure 1. Meta-analysis results
eFigure 2. Conditional analysis at the NPC1L1 locus
eFigure 3. Conditional analysis at the PCSK9 locus
eFigure 4. Associations of LDL-lowering alleles with continuous cardiometabolic traits
eFigure 5. Stratified associations of NPC1L1 variants
eFigure 6. Associations with continuous cardiometabolic traits
eReferences
This supplementary material has been provided by the authors to give readers additional information about their work.
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The EPIC‐InterAct Consortium: Claudia Langenberg, MD, PhD; Robert A. Scott, PhD; Stephen J. Sharp; Eva Ardanaz, MD PhD; Larraitz Arriola MD MSc; Beverley Balkau, PhD; Heiner Boeing, PhD; Panos Deloukas, PhD; Nita G Forouhi, FFPHM; Paul W Franks, PhD; Sara Grioni, BSc; Rudolf Kaaks, PhD; Timothy J Key, DPhil; Carmen Navarro, MD PhD MSc; Peter M Nilsson, PhD; Kim Overvad, PhD; Domenico Palli, MD; Salvatore Panico, MD; Jose‐Ramón Quirós, MD; Elio Riboli, MD MPH, ScM; Olov Rolandsson, MD PhD; Carlotta Sacerdote, MD, PhD; Elena C Salamanca‐Fernandez, MSc; Nadia Slimani, PhD; Annemieke MW Spijkerman; Anne Tjonneland, DrMedSci; Rosario Tumino, MD MSc, DLSHTM; Daphne L van der A, PhD; Yvonne T van der Schouw, PhD; Mark I. McCarthy, MD; Inês Barroso, PhD; Nicholas J. Wareham, MB BS, PhD.
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eTable 1. Participating studies
Outcome Participating study Cases,
N
Non-cases (for case-control studies) or
participants (for continuous traits
studies), N
PubMed ID for cohort description
Website (URL)
Type 2 diabetes –
main analysis
InterAct-GWAS1* 4,187 4,254 21717116 http://www.inter-act.eu/ InterAct-CoreExome1* 5,121 7,269 21717116 http://www.inter-act.eu/
UK Biobank2 6,627 143,765 22463865 http://www.ukbiobank.ac.uk/ DIAGRAM3 34,840 114,981 22885922 http://diagram-consortium.org/
Type 2 diabetes –
exome sequencing
analysis
T2D-GENES Consortium, GoT2D Consortium,
DIAGRAM Consortium4 8,373 8,466 27398621 http://www.type2diabetesgenetics.org/home/portalHome
Coronary artery
disease
CARDIoGRAMplusC4D Consortium5 60,801 123,504 26343387 http://www.cardiogramplusc4d.org/
LDL cholesterol
Global Lipids Genetics Consortium6 - 188,577 24097068 http://csg.sph.umich.edu//abecasis/public/lipids2013/
Fasting plasma glucose
MAGIC Consortium7,8 - 133,010 22885924, 22581228 http://www.magicinvestigators.org/
Fasting insulin MAGIC Consortium7,8 - 108,557 22885924,
22581228 http://www.magicinvestigators.org/
Two hour glucose MAGIC Consortium7,8 - 42,854 22885924,
20081857 http://www.magicinvestigators.org/
Body mass index GIANT Consortium9 - 333,495 25673413 https://www.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium
Waist-to-hip ratio GIANT Consortium10 - 224,047 25673412 https://www.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium
Abbreviations: N, number of participants; LDL, low-density lipoprotein cholesterol. *In EPIC-Interact, genotyping was performed in two batches using the Illumina 660w quad and Illumina CoreExome genotyping arrays. Therefore, results of the main analysis are presented separately for individuals genotyped with the Illumina 660w quad array (InterAct-GWAS sub-study; 4,187 type 2 diabetes cases and 4,254 non-cases from the subcohort) and for individuals genotyped with the Illumina CoreExome array (InterAct-CoreExome sub-study; 5,121 type 2 diabetes cases and 7,269 non-cases from the subcohort).
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eTable 2. Genetic variants included in the main analysis
Gene Polymorphism Genomic position
Effect allele
Effect allele frequency, mean
(range)
Genotyped or imputed (imputation quality score*),
Interact-GWAS
Genotyped or imputed (imputation quality score*),
Interact-CoreExome
Genotyped or imputed (imputation quality score*),
UK Biobank
NPC1L1 rs2073547 chr7:44582331 A 0.81 (0.81, 0.82) Imputed (0.995) Imputed (0.998) Genotyped
NPC1L1 rs217386 chr7:44600695 A 0.42 (0.41, 0.44) Imputed (0.991) Imputed (0.998) Genotyped
HMGCR rs12916 chr5:74656539 T 0.58 (0.57, 0.60) Imputed (0.994) Genotyped Genotyped
HMGCR rs5744707 chr5:74890618 A 0.90 (0.90, 0.91) Genotyped Imputed (0.993) Imputed (0.996)
HMGCR rs16872526 chr5:74675717 T 0.91 (0.90, 0.92) Imputed (0.999) Imputed (0.998) Imputed (0.997)
PCSK9 rs11591147 chr1:55505647 T 0.02 (0.01, 0.02) Imputed (0.877) Genotyped Genotyped
ABCG5/G8 rs4299376 chr2:44072576 T 0.69 (0.68, 0.70) Genotyped Genotyped Imputed (0.995)
LDLR rs6511720 chr19:11202306 T 0.11 (0.10, 0.12) Genotyped Genotyped Genotyped
In DIAGRAM, genetic variants were directly genotyped in the Metabochip subset of the DIAGRAM meta-analysis and either directly genotyped or imputed in the genome-wide association subset.3 Polymorphism names reported in the table are rsID entries from dbSNP release 147. Genomic coordinates represent chromosome and physical position of genetic variants according to the Human Reference Genome Build 37. Effect allele frequency averages and ranges are from EPIC-InterAct,1 UK Biobank2 and DIAGRAM.3 *imputation quality score reports the correlation between genotyped and imputed genotypes in the reference imputation set, with a value of 1 indicating perfect imputation.
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eTable 3. Sensitivity analyses at the NPC1L1 and PCSK9 loci
Locus Model Reference
(PubMed ID) Polymorphisms
OR of type 2 diabetes
(95% CI)
p-value
NPC1L1 Two polymorphisms, adjusted for the GCK rs1799884 and rs2041547 lead
genetic variants This study rs2073547
rs217386 2.16
(1.51 – 3.11) 3 x
10-05
NPC1L1 Five polymorphisms Ference et al. (25770315)11
rs2073547 rs217386 rs7791240 rs2300414 rs10234070
2.20 (1.59 – 3.05)
2 x 10-06
PCSK9 Two polymorphisms This study rs11591147 rs471705 1.21 (1.04 – 1.41) 0.01
PCSK9 Nine polymorphisms This study
rs11591147 rs1998013 rs11206510 rs7523242 rs4927207 rs6662286 rs572512
rs1475701 rs7552841
1.16 (1.03 – 1.31) 0.02
Association with type 2 diabetes of LDL-cholesterol lowering genetic variants at the NPC1L1 and PCSK9 loci in sensitivity analyses. Odds ratios are per a genetically-predicted reduction in LDL cholesterol of 1 mmol/L. OR, odds ratio; CI, confidence interval.
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eTable 4. Correlation between genetic variants Correlation between genetic variants included in Mendelian randomization models at the NPC1L1, HMGCR and PCSK9 loci. The correlation between variants was obtained from the SNAP software12 or from the 1000 Genomes Project browser (URL:http://browser.1000genomes.org/).
Genetic variant 1 Genetic variant 2 Correlation NPC1L1 locus
rs2073547 rs10234070 0.294 rs2073547 rs7791240 0.208 rs2073547 rs2300414 0.098 rs2073547 rs217386 0.083 rs217386 rs7791240 0.078 rs217386 rs10234070 0.06 rs217386 rs2300414 0.052 rs7791240 rs2300414 0.363 rs7791240 rs10234070 0.005 rs2300414 rs10234070 0.033
HMGCR locus rs12916 rs17238484 0.368 rs12916 rs5744707 0.239 rs12916 rs16872526 0.082
rs5744707 rs17238484 0.036 rs5744707 rs16872526 0.008
rs16872526 rs17238484 0.222 PCSK9 locus
rs11591147 rs471705 0.028 rs11591147 rs1998013 0.300 rs11591147 rs11206510 0.191 rs11591147 rs7523242 0.066 rs11591147 rs4927207 0.102 rs11591147 rs6662286 0.176 rs11591147 rs572512 0.008 rs11591147 rs1475701 0.028 rs11591147 rs7552841 0.004 rs1998013 rs11206510 0.191 rs1998013 rs7523242 0.066 rs1998013 rs4927207 0.102 rs1998013 rs6662286 0.176rs1998013 rs572512 0.008 rs1998013 rs1475701 0.028
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Genetic variant 1 Genetic variant 2 Correlation rs1998013 rs7552841 0.004
rs11206510 rs7523242 0.186 rs11206510 rs4927207 0.028 rs11206510 rs6662286 0.017 rs11206510 rs572512 0.200 rs11206510 rs1475701 0.087 rs11206510 rs7552841 0.127 rs7523242 rs4927207 0.048 rs7523242 rs6662286 0.066 rs7523242 rs572512 0.409 rs7523242 rs1475701 0.057 rs7523242 rs7552841 0.068 rs4927207 rs6662286 0.201 rs4927207 rs572512 0.042 rs4927207 rs1475701 0.085 rs4927207 rs7552841 0.232 rs6662286 rs572512 0.163 rs6662286 rs1475701 0.059 rs6662286 rs7552841 0.025 rs572512 rs1475701 0.118rs572512 rs7552841 0.049 rs1475701 rs7552841 0.102
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eTable 5. Burden of rare alleles in exome sequencing studies Burden of protein-truncating and missense variants predicted to be “probably deleterious” for protein function in 8,373 type 2 diabetes cases and 8,466 controls from exome sequencing studies.
Gene Class of genetic
variants
Carriers with type
2 diabetes
Non-carriers
with type 2
diabetes
Carriers among
controls
Non-carriers among
controls
Odds ratio of type 2 diabetes
for carriers (95% CI)
p-value
NPC1L1 Protein truncating 143 8230 129 8337
1.12 (0.88-1.43) 0.34
Probably deleterious missense
360 8013 294 8172 1.26
(1.07-1.47) 0.005
HMGCR Protein truncating 0 8373 0 8466 N/A N/A
Probably deleterious missense
3 8370 10 8456 0.31
(0.08-1.12) 0.07
PCSK9 Protein truncating 37 8336 33 8433
1.13 (0.71-1.82) 0.61
Probably deleterious missense
100 8273 85 8381 1.22
(0.91-1.64) 0.18
ABCG5 Protein truncating 5 8368 9 8457
0.59 (0.20-1.75) 0.34
Probably deleterious missense
54 8319 71 8395 0.77
(0.54-1.10) 0.15
ABCG8 Protein truncating 31 8342 35 8431
0.88 (0.55-1.44) 0.62
Probably deleterious missense
94 8279 112 8354 0.84
(0.64-1.11) 0.23
LDLR Protein truncating 2 8371 2 8464
1.02 (0.14-7.26) 0.98
Probably deleterious missense
53 8320 47 8419 1.15
(0.78-1.70) 0.49
N/A, not available (not calculated); CI, confidence interval.
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eFigure 1. Meta-analysis results Meta-analysis of the association of LDL-cholesterol lowering polymorphisms with risk of type 2 diabetes in EPIC-InterAct,1 UK Biobank2 and DIAGRAM3. For rs12916 in HMGCR, results of an additional eleven studies reported by Swerdlow and colleagues13 were included. In EPIC-InterAct, genotyping was performed in two batches using the Illumina 660w quad and Illumina CoreExome genotyping arrays. Therefore, results of the main analysis are presented separately for individuals genotyped with the Illumina 660w quad array (InterAct-GWAS) and for individuals genotyped with the Illumina CoreExome array (InterAct-CoreExome). Squares indicate the odds ratios and error bars their 95% confidence interval. The size of the squares reflects the weight of the study in the inverse-variance weighted meta-analysis. OR indicates the odds ratio; CI, confidence interval.
1.8 1 1.25
Study OR (95% CI)
OR of type 2 diabetes per allele
NPC1L1 – rs2073547
InterAct-GWAS 1.11 (1.02, 1.21) 4,178
Cases Controls
4,254
InterAct-Core-Exome 1.05 (0.98, 1.13) 5,121 7,269
UK Biobank 1.03 (0.99, 1.08) 6,627 143,765
DIAGRAM 1.05 (1.02, 1.09) 34,840 114,981
Overall (I-squared = 0.0%, p = 0.48)
1.05 (1.03, 1.08) 50,775 270,269
1.8 1 1.25
NPC1L1 – rs217386
Study OR (95% CI) Cases Controls
OR of type 2 diabetes per allele
InterAct-GWAS 1.03 (0.97, 1.10) 4,178 4,254
InterAct-Core-Exome 1.05 (0.99, 1.11) 5,121 7,269
UK Biobank 1.01 (0.98, 1.05) 6,627 143,765
DIAGRAM 1.03 (1.01, 1.05) 34,840 114,981
Overall(I-squared = 0.0%, p = 0.68)
1.03 (1.01, 1.05) 50,775 270,269
1.25 .5 .8 1 1.25 2 4
HMGCR – rs12916
OR of type 2 diabetes per allele
Study OR (95% CI) Cases Controls
InterAct-GWAS 1.05 (0.98, 1.11) 4,178 4,254
InterAct-Core-Exome 1.01 (0.95, 1.06) 5,121 7,269
UK Biobank 1.04 (1.00, 1.07) 6,627 143,765
DIAGRAM 1.01 (0.99, 1.04) 34,840 114,981
Overall(I-squared = 46.4%, p = 0.03)
1.03 (1.01, 1.05) 55,271 320,946
WGHS 1.12 (1.04, 1.21) 1,444 21,268
ET2DS 1.25 (1.10, 1.42) 1,046 821
BWHHS 1.10 (0.95, 1.27) 438 2,839
WHII 1.01 (0.86, 1.19) 336 4,711
WHI 1.03 (0.87, 1.22) 282 5,427
JUPITER 0.83 (0.70, 0.98) 279 8,430
MESA 1.02 (0.83, 1.25) 220 2,078
NPHS-II 1.18 (0.96, 1.45) 217 2,449
CaPS 1.06 (0.81, 1.39) 118 1,288
CARDIA 1.15 (0.85, 1.56) 99 1,344
CFS 1.13 (0.48, 2.66) 17 22
1.8 1 1.25
HMGCR – rs5744707
OR of type 2 diabetes per allele
Study OR (95% CI) Cases Controls
1.06 (0.96, 1.18)InterAct-GWAS 4,178 4,254
0.94 (0.86, 1.03)InterAct-Core-Exome 5,121 7,269
0.98 (0.93, 1.04)UK Biobank 6,627 143,765
0.98 (0.95, 1.02)DIAGRAM 34,840 114,981
Overall (I-squared = 2.8%, p = 0.38)
0.98 (0.96, 1.01) 50,775 270,269
1.8 1 1.25
HMGCR – rs16872526
OR of type 2 diabetes per allele
Study OR (95% CI) Cases Controls
1.04 (0.93, 1.16)InterAct-GWAS 4,178 4,254
1.01 (0.92, 1.12)InterAct-Core-Exome 5,121 7,269
1.08 (1.02, 1.14)UK Biobank 6,627 143,765
0.99 (0.95, 1.03)DIAGRAM 34,840 114,981
Overall(I-squared = 50.1%, p = 0.11)
1.02 (0.99, 1.05) 50,775 270,269
1.5 .8 1 1.25 2
OR of type 2 diabetes per allele
PCSK9 – rs11591147
Study OR (95% CI) Cases Controls
InterAct-GWAS 0.88 (0.67, 1.15) 4,178 4,254
InterAct-Core-Exome 1.19 (0.95, 1.48) 5,121 7,269
UK Biobank 1.10 (0.97, 1.25) 6,627 143,765
DIAGRAM 1.09 (0.98, 1.22) 34,840 114,981
Overall(I-squared = 0.0%, p = 0.39)
1.09 (1.01, 1.17) 50,775 270,269
1.8 1 1.25
ABCG5/G8 – rs4299376
OR of type 2 diabetes per allele
Study OR (95% CI) Cases Controls
0.96 (0.90, 1.03)InterAct-GWAS 4,178 4,254
1.01 (0.96, 1.07)InterAct-Core-Exome 5,121 7,269
1.03 (0.99, 1.07)UK Biobank 6,627 143,765
1.01 (0.98, 1.04)DIAGRAM 34,840 114,981
Overall(I-squared = 2.2%, p = 0.38)
1.01 (0.99, 1.03) 50,775 270,269
1.8 1 1.25
LDLR – rs6511720
OR of type 2 diabetes per allele
Study OR (95% CI) Cases Controls
1.04 (0.94, 1.14)InterAct-GWAS 4,178 4,254
1.05 (0.97, 1.14)InterAct-Core-Exome 5,121 7,269
1.03 (0.98, 1.08)UK Biobank 6,627 143,765
1.02 (0.98, 1.06)DIAGRAM 34,840 114,981
Overall(I-squared = 0.0%, p = 0.93)
1.03 (1.00, 1.06) 50,775 270,269
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eFigure 2. Conditional analysis at the NPC1L1 locus
Association with LDL cholesterol at the NPC1L1 locus in the Global Lipids Genetics Consortium6 results before conditioning (left), after conditioning on the lead rs2073547 polymorphism (middle) and after conditioning on both the rs2073547 and rs217386 polymorphisms (right) in approximate conditional analyses using the GCTA software.14 After conditioning on two polymorphisms the signal was attenuated. Genomic coordinates are relative to Human Reference Genome Build 37.
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eFigure 3. Conditional analysis at the PCSK9 locus
Panel A shows associations with LDL cholesterol at the PCSK9 locus before (left) and after (right) conditioning on rs11591147, rs1998013, rs11206510, rs7523242, rs4927207, rs6662286, rs572512, rs1475701, and rs7552841 in approximate conditional analyses using the GCTA software.14 Data are from the Global Lipids Genetics Consortium.6 After conditioning on the nine polymorphisms the signal was attenuated. Panel B shows associations with LDL cholesterol in a smaller sample with available individual level data. There was evidence of two distinct genome-wide significant signals (p<5 x 10-08) represented by rs11591147 and rs471705. The association signal in the region (left graph) was progressively attenuated after conditioning on rs11591147 (middle graph) and, then, after conditioning on both rs11591147 and rs471705 (right graph). Genomic coordinates are relative to Human Reference Genome Build 37.
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eFigure 4. Associations of LDL-lowering alleles with continuous cardiometabolic traits Associations are in standardized units per LDL-cholesterol lowering allele. LDL cholesterol (N=173,021), HDL cholesterol (N=187,087), ln-transformed triglycerides (N=177,791) levels data are from the Global Lipids Genetics Consortium.6 Systolic (N=8,756) and diastolic (N=8,755) blood pressure data are from the EPIC-InterAct1 subcohort. Body mass index (N=333,495) and waist-to-hip ratio (N=224,047) data are from the GIANT Consortium9,10; fasting glucose (N=133,010), two hour glucose (N=42,854) and ln-transformed fasting insulin data (N=108,557) are from the MAGIC Consortium7,8. Abbreviations: LDL, low density lipoprotein cholesterol; HDL, high-density lipoprotein cholesterol; Ln-TG, triglycerides (natural logarithm transformed); SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index; WHR, waist-to-hip ratio; FPG, fasting plasma glucose; 2hr, 2 hour glucose; Ln-FI, fasting insulin (natural logarithm transformed); SD, standard deviation; CI, confidence interval. All genetic variants were strongly associated with LDL cholesterol levels. NPC1L1 polymorphisms were weakly associated with lower triglyceride levels, consistent with the effect of ezetimibe on triglyceride levels.15 HMGCR polymorphisms were associated with higher BMI levels, consistent with the effect of statins on body weight.13
NPC1L1 – rs2073547
HMGCR – rs16872526
NPC1L1 – rs217386
PCSK9 – rs11591147
HMGCR – rs12916
ABCG5/G8 – rs4299376
HMGCR – rs5744707
LDLR – rs6511720
2hr -0.01 (-0.03, 0.02)
Ln-TG -0.01 (-0.02, -0.00)
WHR -0.00 (-0.01, 0.01)
FPG 0.00 (-0.01, 0.01)
Ln-FI -0.01 (-0.02, 0.00)
DBP -0.02 (-0.05, 0.01)
SBP -0.02 (-0.04, 0.01)
HDL 0.00 (-0.01, 0.01)
BMI 0.00 (-0.00, 0.01)
LDL -0.04 (-0.04, -0.03)
0-.1 0 .1
FPG 0.00 (-0.01, 0.01)
Ln-FI 0.01 (0.00, 0.02)
DBP -0.00 (-0.03, 0.03)
WHR 0.01 (0.00, 0.01)
Ln-TG -0.00 (-0.01, 0.00)
LDL -0.07 (-0.08, -0.07)
HDL -0.00 (-0.01, 0.00)
2hr 0.01 (-0.00, 0.02)
SBP -0.02 (-0.04, 0.01)
BMI 0.02 (0.01, 0.02)
0-.1 0 .1
BMI -0.00 (-0.01, 0.01)
WHR -0.01 (-0.02, 0.01)
Ln-TG -0.01 (-0.02, 0.01)
DBP -0.03 (-0.08, 0.02)
LDL -0.05 (-0.07, -0.04)
SBP -0.03 (-0.07, 0.01)
FPG 0.01 (-0.01, 0.02)
Ln-FI -0.00 (-0.02, 0.02)
HDL 0.01 (-0.00, 0.02)
2hr 0.03 (-0.01, 0.06)
0-.1 0 .1
WHR 0.01 (-0.00, 0.02)
FPG 0.01 (-0.00, 0.02)
2hr -0.00 (-0.02, 0.02)
Ln-FI 0.01 (-0.00, 0.03)
LDL -0.22 (-0.23, -0.21)
Ln-TG -0.01 (-0.02, 0.00)
DBP 0.01 (-0.03, 0.06)
BMI 0.01 (-0.00, 0.02)
SBP 0.01 (-0.03, 0.06)
HDL 0.02 (0.01, 0.04)
0-.25 0 .25
Ln-FI -0.00 (-0.01, 0.01)
HDL 0.00 (-0.01, 0.01)
BMI 0.01 (0.00, 0.01)
WHR 0.00 (-0.00, 0.01)
SBP 0.01 (-0.02, 0.04)
LDL -0.08 (-0.09, -0.07)
FPG -0.00 (-0.01, 0.01)
DBP -0.01 (-0.04, 0.02)
Ln-TG -0.01 (-0.02, -0.00)
2hr -0.01 (-0.02, 0.01)
0-.1 0 .1
WHR 0.04 (0.01, 0.08)
FPG 0.04 (0.01, 0.07)
BMI 0.01 (-0.03, 0.04)
DBP 0.02 (-0.11, 0.14)
2hr 0.05 (-0.00, 0.10)
LDL -0.50 (-0.53, -0.46)
SBP 0.06 (-0.05, 0.17)
Ln-FI 0.03 (-0.02, 0.07)
HDL 0.04 (0.00, 0.07)
Ln-TG -0.01 (-0.04, 0.03)
0-.6 0 .6
LDL -0.04 (-0.05, -0.03)
BMI 0.02 (0.01, 0.03)
FPG 0.01 (-0.00, 0.03)
2hr 0.00 (-0.04, 0.04)
WHR 0.01 (-0.00, 0.02)
Ln-FI 0.01 (-0.01, 0.03)
Ln-TG 0.01 (-0.01, 0.02)
SBP 0.00 (-0.05, 0.05)
HDL -0.01 (-0.02, 0.00)
DBP 0.02 (-0.03, 0.08)
0-.1 0 .1
SBP 0.00 (-0.03, 0.04)
Ln-FI -0.00 (-0.02, 0.01)
2hr 0.01 (-0.02, 0.03)
FPG 0.01 (-0.00, 0.03)
WHR -0.00 (-0.01, 0.01)
BMI 0.00 (-0.01, 0.01)
DBP 0.00 (-0.04, 0.04)
Ln-TG -0.01 (-0.02, -0.01)
LDL -0.05 (-0.06, -0.04)
HDL 0.00 (-0.00, 0.01)
0-.1 0 .1
SD per allele SD per allele SD per allele SD per allele
SD per allele SD per allele SD per allele SD per allele
Phenotype Beta (95% CI) Phenotype Beta (95% CI) Phenotype Beta (95% CI) Phenotype Beta (95% CI)
Phenotype Beta (95% CI) Phenotype Beta (95% CI) Phenotype Beta (95% CI) Phenotype Beta (95% CI)
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eFigure 5. Stratified associations of NPC1L1 variants Combined association of LDL-lowering alleles at NPC1L1 with risk of type 2 diabetes in strata of sex, age and body mass index. Data are from the EPIC-InterAct1 and UK Biobank2 studies. Analyses are scaled to represent the odds ratio of type 2 diabetes for a genetically predicted reduction in LDL cholesterol of 1 mmol/L. Squares indicate the odds ratio and the error bars its 95% confidence interval. The size of the squares indicates the weight of the subgroup analysis in the inverse-variance weighted meta-analysis. OR indicates the odds ratio; CI, confidence interval.
1.125 1 8 32
Body mass index
> 55 years
Age
Obese
Overweight
<= 55 years
All (I-squared = 48.4%, p = 0.144)
Men
All (I-squared = 0.0%, p = 0.939)
Lean
Sex
All (I-squared = 0.0%, p = 0.609)
Women
Analysis
1.17 (0.46, 2.97)
2.75 (1.07, 7.02)
2.89 (1.11, 7.53)
2.19 (1.19, 4.02)
2.17 (1.00, 4.71)
2.22 (1.25, 3.93)
2.37 (1.33, 4.22)
2.27 (0.97, 5.33)
2.11 (1.03, 4.35)
6.46 (1.40, 29.81)
OR (95% CI)
9,100
6,973
5,823
5,411
14,549
8,038
14,657
14,511
6,619
1,753
Cases
Odds ratio of type 2 diabetesper 1 mmol/L reduction
in LDL cholesterol
Controls
68,024
26,481
50,617
47,229
118,484
54,333
118,854
115,253
64,521
41,386
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eFigure 6. Associations with continuous cardiometabolic traits Association of LDL lowering alleles with continuous anthropometric and glycemic traits. Associations are in standardised units per 1 mmol/L reduction in LDL cholesterol. Body mass index (N=333,495) and waist-to-hip ratio (N=224,047) data are from the GIANT Consortium9,10; fasting glucose (N=133,010), two hour glucose (N=42,854) and ln-transformed fasting insulin data (N=108,557) are from the MAGIC Consortium7,8. Abbreviations: BMI, body mass index; WHR, waist-to-hip ratio; FPG, fasting plasma glucose; 2hr, 2 hour glucose; Ln-FI, fasting insulin (natural logarithm transformed); CI, confidence interval.
BMI 0.08 (-0.04, 0.20)
0-.5 0 .5
0.23 (0.15, 0.30)
0-.5 0 .5
-0.02 (-0.09, 0.05)
0-.5 0 .5
0.09 (0.00, 0.17)
0-.5 0 .5
0.03 (-0.01, 0.08)
FPG 0.14 (-0.05, 0.33) 0.02 (-0.07, 0.11) 0.08 (0.02, 0.15) -0.02 (-0.11, 0.08) 0.04 (-0.01, 0.08)
Ln-FI -0.15 (-0.37, 0.07) 0.16 (0.05, 0.27) 0.05 (-0.03, 0.14) -0.04 (-0.16, 0.09) 0.05 (-0.01, 0.11)
2hr -0.05 (-0.51, 0.41) 0.14 (-0.03, 0.31) 0.10 (0.00, 0.20) -0.09 (-0.28, 0.11) -0.01 (-0.11, 0.09)
WHR -0.01 (-0.14, 0.12) 0.09 (0.01, 0.18) 0.08 (0.01, 0.15) 0.04 (-0.06, 0.14) 0.03 (-0.02, 0.08)
0-.5 0 .5
Standard deviationper 1 mmol/L genetically-predicted reduction
in LDL cholesterol
NPC1L1 HMGCR PCSK9 ABCG5/G8 LDLR
Beta (95% CI) Beta (95% CI) Beta (95% CI) Beta (95% CI) Beta (95% CI)
-1
Phenotype
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