Datasets:
sample_id
stringlengths 15
15
| population
stringclasses 7
values | region
stringclasses 5
values | is_SSA
bool 2
classes | is_reference_panel
bool 2
classes | sex
stringclasses 2
values | age
int64 18
80
| menopausal_status
stringclasses 4
values | estradiol_pg_ml
float64 5.01
213
| progesterone_ng_ml
float64 0.1
9.99
| testosterone_ng_dl
float64 5.02
980
| insulin_fasting_uIU_ml
float64 1
23.5
| igf1_ng_ml
float64 40.3
455
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
HP_SAMPLE_00001
|
SSA_West
|
West
| true
| false
|
Male
| 49
|
NA
| 21.082348
| 0.736863
| 281.794214
| 8.038734
| 122.897581
|
HP_SAMPLE_00002
|
SSA_West
|
West
| true
| false
|
Male
| 33
|
NA
| 22.143448
| 1.077976
| 344.10562
| 6.542418
| 238.072097
|
HP_SAMPLE_00003
|
SSA_West
|
West
| true
| false
|
Female
| 54
|
postmenopausal
| 11.239978
| 0.546667
| 39.295496
| 10.963884
| 128.909294
|
HP_SAMPLE_00004
|
SSA_West
|
West
| true
| false
|
Male
| 56
|
NA
| 32.994208
| 0.659182
| 526.198772
| 9.062258
| 166.227405
|
HP_SAMPLE_00005
|
SSA_West
|
West
| true
| false
|
Male
| 22
|
NA
| 40.423737
| 0.472332
| 509.214359
| 11.227787
| 267.507728
|
HP_SAMPLE_00006
|
SSA_West
|
West
| true
| false
|
Female
| 29
|
premenopausal
| 46.966579
| 2.038657
| 36.98303
| 12.611835
| 442.139554
|
HP_SAMPLE_00007
|
SSA_West
|
West
| true
| false
|
Male
| 47
|
NA
| 39.566095
| 0.520876
| 476.536327
| 10.628819
| 187.489437
|
HP_SAMPLE_00008
|
SSA_West
|
West
| true
| false
|
Female
| 41
|
premenopausal
| 65.542641
| 3.058485
| 36.6788
| 10.384414
| 189.431615
|
HP_SAMPLE_00009
|
SSA_West
|
West
| true
| false
|
Female
| 45
|
perimenopausal
| 35.413809
| 4.319143
| 34.31416
| 11.28739
| 154.609877
|
HP_SAMPLE_00010
|
SSA_West
|
West
| true
| false
|
Male
| 35
|
NA
| 37.843605
| 0.554482
| 282.32703
| 6.850018
| 169.567116
|
HP_SAMPLE_00011
|
SSA_West
|
West
| true
| false
|
Female
| 56
|
postmenopausal
| 10.196346
| 0.179048
| 25.43856
| 13.192076
| 155.871991
|
HP_SAMPLE_00012
|
SSA_West
|
West
| true
| false
|
Female
| 54
|
perimenopausal
| 20.468239
| 0.469096
| 21.229489
| 4.75318
| 163.700569
|
HP_SAMPLE_00013
|
SSA_West
|
West
| true
| false
|
Female
| 46
|
premenopausal
| 134.123955
| 1.848192
| 30.913607
| 7.713118
| 148.518607
|
HP_SAMPLE_00014
|
SSA_West
|
West
| true
| false
|
Male
| 59
|
NA
| 37.843641
| 0.301008
| 352.991946
| 4.142126
| 152.90647
|
HP_SAMPLE_00015
|
SSA_West
|
West
| true
| false
|
Male
| 51
|
NA
| 51.005141
| 0.712193
| 629.0392
| 6.976454
| 188.286422
|
HP_SAMPLE_00016
|
SSA_West
|
West
| true
| false
|
Female
| 35
|
premenopausal
| 35.894695
| 4.418517
| 59.662809
| 10.495145
| 301.399639
|
HP_SAMPLE_00017
|
SSA_West
|
West
| true
| false
|
Male
| 49
|
NA
| 26.01902
| 0.459731
| 380.96112
| 9.10437
| 193.483691
|
HP_SAMPLE_00018
|
SSA_West
|
West
| true
| false
|
Female
| 33
|
premenopausal
| 81.041403
| 2.051722
| 27.639264
| 10.035512
| 201.239219
|
HP_SAMPLE_00019
|
SSA_West
|
West
| true
| false
|
Male
| 56
|
NA
| 17.722955
| 0.321353
| 284.127752
| 6.49329
| 130.951264
|
HP_SAMPLE_00020
|
SSA_West
|
West
| true
| false
|
Male
| 44
|
NA
| 32.808512
| 0.300198
| 784.856144
| 5.216691
| 240.460623
|
HP_SAMPLE_00021
|
SSA_West
|
West
| true
| false
|
Female
| 43
|
perimenopausal
| 45.9358
| 0.842474
| 37.050912
| 7.641367
| 139.651766
|
HP_SAMPLE_00022
|
SSA_West
|
West
| true
| false
|
Female
| 37
|
premenopausal
| 151.592396
| 2.510252
| 37.851074
| 13.224068
| 188.086301
|
HP_SAMPLE_00023
|
SSA_West
|
West
| true
| false
|
Female
| 60
|
postmenopausal
| 36.288122
| 0.727736
| 8.370538
| 9.710802
| 84.24535
|
HP_SAMPLE_00024
|
SSA_West
|
West
| true
| false
|
Male
| 43
|
NA
| 43.217499
| 0.363881
| 344.516567
| 6.106232
| 222.59568
|
HP_SAMPLE_00025
|
SSA_West
|
West
| true
| false
|
Female
| 40
|
premenopausal
| 121.810993
| 5.149074
| 26.023118
| 4.786672
| 207.79321
|
HP_SAMPLE_00026
|
SSA_West
|
West
| true
| false
|
Female
| 41
|
premenopausal
| 60.545627
| 6.81828
| 19.38704
| 4.583547
| 194.252541
|
HP_SAMPLE_00027
|
SSA_West
|
West
| true
| false
|
Male
| 51
|
NA
| 33.982288
| 0.945857
| 404.337567
| 7.890131
| 165.08527
|
HP_SAMPLE_00028
|
SSA_West
|
West
| true
| false
|
Male
| 49
|
NA
| 37.992769
| 0.569011
| 535.353695
| 9.493849
| 159.175852
|
HP_SAMPLE_00029
|
SSA_West
|
West
| true
| false
|
Female
| 50
|
premenopausal
| 113.307104
| 2.280205
| 33.043503
| 13.163286
| 119.510661
|
HP_SAMPLE_00030
|
SSA_West
|
West
| true
| false
|
Male
| 50
|
NA
| 71.067251
| 0.286216
| 365.353419
| 10.619972
| 169.870308
|
HP_SAMPLE_00031
|
SSA_West
|
West
| true
| false
|
Female
| 71
|
perimenopausal
| 51.795381
| 2.318251
| 22.02104
| 15.197226
| 127.870983
|
HP_SAMPLE_00032
|
SSA_West
|
West
| true
| false
|
Female
| 40
|
perimenopausal
| 28.637659
| 0.948291
| 35.655893
| 10.457855
| 190.323802
|
HP_SAMPLE_00033
|
SSA_West
|
West
| true
| false
|
Male
| 39
|
NA
| 22.257446
| 0.832594
| 821.178692
| 9.087676
| 309.583272
|
HP_SAMPLE_00034
|
SSA_West
|
West
| true
| false
|
Male
| 35
|
NA
| 47.99384
| 1.165606
| 616.170173
| 9.409809
| 256.332068
|
HP_SAMPLE_00035
|
SSA_West
|
West
| true
| false
|
Male
| 52
|
NA
| 28.496225
| 0.622135
| 609.685974
| 11.458531
| 148.606905
|
HP_SAMPLE_00036
|
SSA_West
|
West
| true
| false
|
Male
| 59
|
NA
| 38.006234
| 0.727303
| 536.933593
| 11.250193
| 165.274715
|
HP_SAMPLE_00037
|
SSA_West
|
West
| true
| false
|
Male
| 44
|
NA
| 22.63183
| 0.300454
| 559.951679
| 11.494378
| 215.096811
|
HP_SAMPLE_00038
|
SSA_West
|
West
| true
| false
|
Female
| 35
|
premenopausal
| 52.991331
| 2.697665
| 17.38582
| 4.825714
| 214.481362
|
HP_SAMPLE_00039
|
SSA_West
|
West
| true
| false
|
Male
| 35
|
NA
| 55.372345
| 0.529941
| 817.13896
| 8.507688
| 211.716946
|
HP_SAMPLE_00040
|
SSA_West
|
West
| true
| false
|
Female
| 53
|
postmenopausal
| 7.071945
| 0.395827
| 13.100618
| 4.363988
| 97.218092
|
HP_SAMPLE_00041
|
SSA_West
|
West
| true
| false
|
Male
| 54
|
NA
| 36.279126
| 0.278988
| 390.015262
| 10.229612
| 80.304073
|
HP_SAMPLE_00042
|
SSA_West
|
West
| true
| false
|
Male
| 52
|
NA
| 36.215541
| 0.754423
| 562.147139
| 10.549701
| 147.651798
|
HP_SAMPLE_00043
|
SSA_West
|
West
| true
| false
|
Male
| 37
|
NA
| 25.643917
| 0.475292
| 781.484852
| 9.819199
| 204.121497
|
HP_SAMPLE_00044
|
SSA_West
|
West
| true
| false
|
Female
| 48
|
postmenopausal
| 19.509542
| 0.514917
| 32.709515
| 8.259849
| 159.253112
|
HP_SAMPLE_00045
|
SSA_West
|
West
| true
| false
|
Female
| 46
|
perimenopausal
| 74.900007
| 1.978602
| 24.278964
| 3.284363
| 131.269529
|
HP_SAMPLE_00046
|
SSA_West
|
West
| true
| false
|
Male
| 48
|
NA
| 34.459309
| 0.530444
| 596.200579
| 11.424055
| 102.664252
|
HP_SAMPLE_00047
|
SSA_West
|
West
| true
| false
|
Female
| 55
|
perimenopausal
| 59.43759
| 2.228544
| 27.189226
| 10.507324
| 63.036564
|
HP_SAMPLE_00048
|
SSA_West
|
West
| true
| false
|
Female
| 48
|
premenopausal
| 40.3914
| 5.732094
| 26.6435
| 15.669312
| 116.078879
|
HP_SAMPLE_00049
|
SSA_West
|
West
| true
| false
|
Male
| 53
|
NA
| 58.456584
| 0.316788
| 553.492318
| 8.113853
| 155.205517
|
HP_SAMPLE_00050
|
SSA_West
|
West
| true
| false
|
Female
| 46
|
postmenopausal
| 19.764346
| 0.315994
| 39.613277
| 8.202379
| 188.902497
|
HP_SAMPLE_00051
|
SSA_West
|
West
| true
| false
|
Male
| 48
|
NA
| 29.939971
| 0.879063
| 590.308034
| 11.659637
| 182.665884
|
HP_SAMPLE_00052
|
SSA_West
|
West
| true
| false
|
Male
| 53
|
NA
| 60.144118
| 0.586532
| 370.78799
| 11.565423
| 191.516926
|
HP_SAMPLE_00053
|
SSA_West
|
West
| true
| false
|
Female
| 28
|
premenopausal
| 105.183649
| 2.194132
| 44.222083
| 13.179975
| 278.653
|
HP_SAMPLE_00054
|
SSA_West
|
West
| true
| false
|
Male
| 41
|
NA
| 12.833562
| 0.311371
| 412.215506
| 6.73703
| 74.435785
|
HP_SAMPLE_00055
|
SSA_West
|
West
| true
| false
|
Male
| 39
|
NA
| 63.358663
| 0.237114
| 437.447237
| 3.174875
| 257.653594
|
HP_SAMPLE_00056
|
SSA_West
|
West
| true
| false
|
Female
| 37
|
premenopausal
| 76.365406
| 2.298173
| 30.52998
| 10.763413
| 183.598817
|
HP_SAMPLE_00057
|
SSA_West
|
West
| true
| false
|
Male
| 42
|
NA
| 5.166484
| 0.112539
| 325.348708
| 9.825954
| 226.500952
|
HP_SAMPLE_00058
|
SSA_West
|
West
| true
| false
|
Female
| 63
|
perimenopausal
| 86.645141
| 2.12027
| 15.07943
| 17.770374
| 185.342165
|
HP_SAMPLE_00059
|
SSA_West
|
West
| true
| false
|
Female
| 35
|
premenopausal
| 148.075816
| 4.60488
| 40.728323
| 7.792251
| 177.037355
|
HP_SAMPLE_00060
|
SSA_West
|
West
| true
| false
|
Female
| 57
|
perimenopausal
| 29.997373
| 2.117576
| 40.604028
| 7.929576
| 117.494304
|
HP_SAMPLE_00061
|
SSA_West
|
West
| true
| false
|
Female
| 25
|
premenopausal
| 20.659864
| 3.489383
| 58.439012
| 12.837982
| 332.706324
|
HP_SAMPLE_00062
|
SSA_West
|
West
| true
| false
|
Female
| 41
|
perimenopausal
| 60.385165
| 1.590189
| 45.387027
| 5.669816
| 157.957754
|
HP_SAMPLE_00063
|
SSA_West
|
West
| true
| false
|
Female
| 47
|
perimenopausal
| 92.771202
| 0.425394
| 29.586502
| 10.893992
| 184.134928
|
HP_SAMPLE_00064
|
SSA_West
|
West
| true
| false
|
Male
| 52
|
NA
| 52.241263
| 0.957703
| 507.559718
| 13.170188
| 108.209604
|
HP_SAMPLE_00065
|
SSA_West
|
West
| true
| false
|
Male
| 54
|
NA
| 32.636911
| 1.221557
| 683.151651
| 5.169182
| 149.425692
|
HP_SAMPLE_00066
|
SSA_West
|
West
| true
| false
|
Female
| 55
|
premenopausal
| 85.442985
| 3.833051
| 37.740427
| 14.517477
| 151.683728
|
HP_SAMPLE_00067
|
SSA_West
|
West
| true
| false
|
Female
| 41
|
premenopausal
| 124.879432
| 4.296392
| 29.746431
| 3.905525
| 230.923681
|
HP_SAMPLE_00068
|
SSA_West
|
West
| true
| false
|
Male
| 39
|
NA
| 44.077906
| 0.616877
| 412.622773
| 14.877412
| 218.385954
|
HP_SAMPLE_00069
|
SSA_West
|
West
| true
| false
|
Female
| 55
|
postmenopausal
| 5.868995
| 0.109304
| 31.774724
| 11.121749
| 129.164543
|
HP_SAMPLE_00070
|
SSA_West
|
West
| true
| false
|
Male
| 43
|
NA
| 41.939377
| 0.700282
| 518.190279
| 6.895977
| 228.067797
|
HP_SAMPLE_00071
|
SSA_West
|
West
| true
| false
|
Female
| 30
|
premenopausal
| 113.505252
| 4.13536
| 22.494022
| 5.006903
| 214.208815
|
HP_SAMPLE_00072
|
SSA_West
|
West
| true
| false
|
Male
| 31
|
NA
| 48.943567
| 0.812112
| 631.813457
| 7.648637
| 165.768593
|
HP_SAMPLE_00073
|
SSA_West
|
West
| true
| false
|
Male
| 34
|
NA
| 34.746998
| 0.497785
| 705.021341
| 7.560857
| 188.18429
|
HP_SAMPLE_00074
|
SSA_West
|
West
| true
| false
|
Male
| 51
|
NA
| 65.824867
| 0.965367
| 344.066347
| 13.406256
| 99.515321
|
HP_SAMPLE_00075
|
SSA_West
|
West
| true
| false
|
Male
| 47
|
NA
| 28.489119
| 0.403927
| 489.913089
| 10.859269
| 185.323666
|
HP_SAMPLE_00076
|
SSA_West
|
West
| true
| false
|
Male
| 53
|
NA
| 44.22091
| 0.379448
| 494.906715
| 9.236901
| 87.525147
|
HP_SAMPLE_00077
|
SSA_West
|
West
| true
| false
|
Male
| 40
|
NA
| 42.245153
| 0.528817
| 407.191754
| 7.725474
| 159.983963
|
HP_SAMPLE_00078
|
SSA_West
|
West
| true
| false
|
Male
| 47
|
NA
| 31.326223
| 1.027687
| 771.733077
| 10.464523
| 227.747568
|
HP_SAMPLE_00079
|
SSA_West
|
West
| true
| false
|
Male
| 53
|
NA
| 31.758848
| 0.778427
| 648.500123
| 4.884474
| 156.019285
|
HP_SAMPLE_00080
|
SSA_West
|
West
| true
| false
|
Male
| 41
|
NA
| 64.351208
| 0.12867
| 485.019796
| 7.654808
| 232.594637
|
HP_SAMPLE_00081
|
SSA_West
|
West
| true
| false
|
Male
| 50
|
NA
| 52.868689
| 0.548698
| 453.865116
| 3.842556
| 214.035113
|
HP_SAMPLE_00082
|
SSA_West
|
West
| true
| false
|
Male
| 37
|
NA
| 29.702505
| 0.457302
| 686.023972
| 9.592799
| 265.211307
|
HP_SAMPLE_00083
|
SSA_West
|
West
| true
| false
|
Male
| 41
|
NA
| 44.051152
| 0.561136
| 540.357555
| 5.631682
| 172.550994
|
HP_SAMPLE_00084
|
SSA_West
|
West
| true
| false
|
Male
| 40
|
NA
| 51.260706
| 0.230408
| 407.01411
| 6.361127
| 187.66903
|
HP_SAMPLE_00085
|
SSA_West
|
West
| true
| false
|
Male
| 31
|
NA
| 56.037933
| 0.664148
| 245.225239
| 10.945875
| 185.685047
|
HP_SAMPLE_00086
|
SSA_West
|
West
| true
| false
|
Male
| 51
|
NA
| 42.640735
| 0.45515
| 346.745213
| 8.785585
| 130.133845
|
HP_SAMPLE_00087
|
SSA_West
|
West
| true
| false
|
Female
| 39
|
premenopausal
| 92.308598
| 1.22799
| 18.128452
| 11.196246
| 270.986173
|
HP_SAMPLE_00088
|
SSA_West
|
West
| true
| false
|
Female
| 45
|
premenopausal
| 103.202312
| 1.702454
| 21.119161
| 6.952532
| 224.519746
|
HP_SAMPLE_00089
|
SSA_West
|
West
| true
| false
|
Female
| 51
|
perimenopausal
| 29.373779
| 2.384041
| 31.369032
| 12.713181
| 197.841889
|
HP_SAMPLE_00090
|
SSA_West
|
West
| true
| false
|
Female
| 50
|
postmenopausal
| 19.341245
| 0.683039
| 26.154734
| 7.880239
| 211.300091
|
HP_SAMPLE_00091
|
SSA_West
|
West
| true
| false
|
Female
| 53
|
perimenopausal
| 46.216608
| 0.970551
| 36.240562
| 7.784722
| 123.922805
|
HP_SAMPLE_00092
|
SSA_West
|
West
| true
| false
|
Male
| 44
|
NA
| 18.092688
| 0.253947
| 404.529386
| 10.863143
| 129.046385
|
HP_SAMPLE_00093
|
SSA_West
|
West
| true
| false
|
Female
| 40
|
perimenopausal
| 57.580745
| 0.866049
| 29.804797
| 12.847248
| 183.577237
|
HP_SAMPLE_00094
|
SSA_West
|
West
| true
| false
|
Female
| 44
|
perimenopausal
| 87.740528
| 1.431853
| 42.083478
| 7.357718
| 185.384533
|
HP_SAMPLE_00095
|
SSA_West
|
West
| true
| false
|
Female
| 25
|
premenopausal
| 42.68128
| 1.371602
| 36.630004
| 5.014398
| 259.561598
|
HP_SAMPLE_00096
|
SSA_West
|
West
| true
| false
|
Female
| 28
|
premenopausal
| 67.889601
| 3.46916
| 20.302135
| 11.105197
| 351.85682
|
HP_SAMPLE_00097
|
SSA_West
|
West
| true
| false
|
Male
| 29
|
NA
| 57.297996
| 1.071172
| 606.364649
| 7.943016
| 279.255012
|
HP_SAMPLE_00098
|
SSA_West
|
West
| true
| false
|
Female
| 33
|
premenopausal
| 70.471209
| 4.638599
| 31.483972
| 4.597942
| 182.044475
|
HP_SAMPLE_00099
|
SSA_West
|
West
| true
| false
|
Female
| 50
|
postmenopausal
| 13.306719
| 0.50355
| 25.23845
| 13.107059
| 92.015678
|
HP_SAMPLE_00100
|
SSA_West
|
West
| true
| false
|
Male
| 34
|
NA
| 32.717837
| 0.236311
| 725.863555
| 7.315638
| 191.089359
|
SSA Hormonal Profiles Dataset (Multi-ancestry, Synthetic)
Dataset summary
This dataset provides a synthetic hormonal profile cohort of 10,000 adults across multiple ancestry groups with a focus on sub-Saharan Africa (SSA). It includes:
- Estradiol (E2) and progesterone (P4) in women and men (by menopausal status for women).
- Testosterone (T) in women and men (by age band).
- Fasting insulin levels (by population).
- IGF-1 levels (by age band).
Values are informed by published reference intervals and age/sex trends for these hormones, but all individuals and measurements are fully synthetic.
Cohort design
Sample size and populations
Total N: 10,000 synthetic adults.
Populations:
SSA_West: 2,000SSA_East: 2,000SSA_Central: 1,500SSA_Southern: 1,500AAW(African American, admixed): 1,500EUR(European reference): 1,000EAS(East Asian reference): 500
Sex distribution:
Male: ~45%Female: ~55%
Age: 18β80 years with population-specific means/SDs, loosely aligned with prior cardiovascular and reproductive-history datasets.
Hormonal variables
Menopausal status (women)
Variable:
menopausal_statusβ for women:premenopausalperimenopausalpostmenopausal
- For men:
menopausal_status = "NA".
Status is assigned by age band (18β39, 40β49, 50β80) with probabilities reflecting typical menopause timing:
- 18β39: mostly premenopausal.
- 40β49: mix of pre-, peri-, and postmenopausal.
- 50β80: predominantly postmenopausal.
Estradiol (E2)
Variable:
estradiol_pg_mlβ estradiol in pg/mL.
Design anchors:
- Premenopausal women have higher and more variable estradiol (mean ~80 pg/mL, SD ~40), capturing the broad range across the menstrual cycle.
- Perimenopausal women have intermediate values (mean ~50 pg/mL).
- Postmenopausal women have low values (mean ~15 pg/mL) with an approximate upper bound of 20β30 pg/mL.
- Men have low-to-moderate estradiol (mean ~35 pg/mL, SD ~15).
These values are informed qualitatively by clinical reference intervals and reviews of estradiol levels in pre- vs postmenopausal women and men.
Progesterone (P4)
Variable:
progesterone_ng_mlβ progesterone in ng/mL.
Design anchors:
- Premenopausal women: mean ~3 ng/mL (large SD), representing random-cycle values with luteal peaks.
- Perimenopausal women: mean ~1.5 ng/mL.
- Postmenopausal women: mean ~0.5 ng/mL.
- Men: mean ~0.5 ng/mL.
These reflect the general pattern of high-cycle variation in premenopausal women and low steady values after menopause or in men.
Testosterone (T)
Variable:
testosterone_ng_dlβ total testosterone in ng/dL.
Age bands:
- 18β29, 30β39, 40β49, 50β59, 60β80 years.
Design anchors (mid-range values from reference summaries):
Men
- 18β29: mean ~600 ng/dL.
- 30β39: ~550 ng/dL.
- 40β49: ~500 ng/dL.
- 50β59: ~450 ng/dL.
- 60β80: ~400 ng/dL.
Women
- 18β29: mean ~40 ng/dL.
- 30β39: ~35 ng/dL.
- 40β49: ~30 ng/dL.
- 50β59: ~25 ng/dL.
- 60β80: ~20 ng/dL.
Testosterone decreases with age in both sexes, roughly consistent with published age-specific reference data.
Fasting insulin
Variable:
insulin_fasting_uIU_mlβ fasting serum insulin in uIU/mL.
Design anchors:
- Healthy adult reference intervals suggest 2β12 uIU/mL as typical.
- Population-specific means reflect metabolic differences:
- SSA and AAW have slightly higher means (~8.5β10 uIU/mL).
- EUR/EAS have somewhat lower means (~7β7.5 uIU/mL).
Values are truncated between 1 and 40 uIU/mL.
IGF-1
Variable:
igf1_ng_mlβ insulin-like growth factor 1 in ng/mL.
Age bands and means reflect age-related decline (β15% per decade) from young adulthood:
- 18β29: mean ~250 ng/mL.
- 30β39: ~210 ng/mL.
- 40β49: ~180 ng/mL.
- 50β59: ~150 ng/mL.
- 60β80: ~130 ng/mL.
Values are truncated between 40 and 500 ng/mL.
File and schema
hormonal_profiles_data.parquet / hormonal_profiles_data.csv
One row per synthetic individual with:
sample_idpopulation,region,is_SSA,is_reference_panelsex(Male,Female)age(18β80 years)menopausal_status(for women;NAfor men)estradiol_pg_mlprogesterone_ng_mltestosterone_ng_dlinsulin_fasting_uIU_mligf1_ng_ml
Generation
The dataset is generated with:
hormonal_profiles/scripts/generate_hormonal_profiles.py
using configuration in:
hormonal_profiles/configs/hormonal_profiles_config.yaml
and literature curated in:
hormonal_profiles/docs/LITERATURE_INVENTORY.csv
Key modeling steps:
- Sample generation β multi-ancestry sample with age/sex distribution.
- Menopausal status assignment β by age band in women.
- Hormone sampling β for each hormone, sample from normal distributions with age/sex/status-specific means and SDs and truncate to plausible ranges.
Validation
Validation is implemented in:
hormonal_profiles/scripts/validate_hormonal_profiles.py
Checks include:
- C01βC02 β Sample size and population counts vs config.
- C03 β Estradiol means by sex and menopausal status vs config.
- C04 β Progesterone means by sex and menopausal status vs config.
- C05 β Testosterone means by sex and age band vs config.
- C06 β Insulin means by population vs config.
- C07 β IGF-1 means by age band vs config.
- C08 β Missingness in key variables.
The validator writes:
hormonal_profiles/output/validation_report.md
For the released version, the validator reports overall status PASS (with some checks at WARN level where sampling noise produces minor deviations from target means).
Intended use
This dataset is intended for:
- Methods development in endocrine modeling and multi-modal risk prediction.
- Exploring how age, sex, and menopausal status relate to key hormonal axes.
- Serving as a companion to the reproductive history, body composition, and cardiovascular metrics datasets.
It is not suitable for:
- Clinical decision-making or hormone therapy dosing.
- Deriving exact clinical reference intervals.
- Individual-level risk prediction.
All hormone levels are synthetic.
Ethical considerations
- No real patient data are used.
- Population labels are for simulation and methodological realism only.
- Analyses should be interpreted as methodological demonstrations rather than statements about specific groups.
License
- License: CC BY-NC 4.0.
- Free to use for non-commercial research, education, and methods development with attribution.
Citation
If you use this dataset, please cite:
Electric Sheep Africa. "SSA Hormonal Profiles Dataset (Multi-ancestry, Synthetic)." Hugging Face Datasets.
and consider citing relevant endocrine reference works that informed the design (estradiol/progesterone reference intervals, age-related testosterone and IGF-1 studies, and fasting insulin reference interval papers).
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