# Advances in Congenital Adrenal Hyperplasia newborn screening: 11-ketotestosterone and 21-deoxycortisone as additional discriminatory biomarkers

Coumba Sow, Thao Nguyen-khoa, Caroline Moreau, Jean Fiet, Guillaume

Bachelot, Bettina Ribault, Ophélie Chaussenery-Lorentz, Layla Al Zarga,

Aurore Desmons, Frédéric Brioude, et al.

► To cite this version:

Coumba Sow, Thao Nguyen-khoa, Caroline Moreau, Jean Fiet, Guillaume Bachelot, et al. Advances in Congenital Adrenal Hyperplasia newborn screening: 11-ketotestosterone and 21-deoxycortisone as additional discriminatory biomarkers. European Journal of Endocrinology, 2025, Online ahead of print. 10.1093/ejendo/lvaf208. hal-05345787

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Advances in Congenital Adrenal

Hyperplasia newborn screening: 11-ketotestosterone and 21-deoxycortisone as additional discriminatory biomarkers.

Coumba Sow¹; Thao Nguyen-Khoa²,³; Caroline Moreau⁴; Jean Fiet¹; Guillaume Bachelot¹; Bettina

Ribault¹, Ophélie Chausseny¹; Layla Al Zarga¹; Aurore Desmons¹; Frédéric Brioude⁵,⁶;

Dulanjalee Kariyawasam⁷; Athanasia Stoupa²,⁷,⁸; Michel Polak²,⁷,⁸; Muriel Houang⁵; Antonin

Lamazière¹. On behalf of the FIRENDO group⁹

¹Sorbonne Université, INSERM, Centre de Recherche Saint-Antoine : Département de Métabolomique Clinique, Hôpital Saint-Antoine, AP-HP Sorbonne Université, Paris, France

²Centre Régional de Dépistage Néonatal-Ile de France, Hôpital Necker-Enfants Malades, AP-HP
Centre Université de Paris Cité, Paris, France

³Institut Necker-Enfants Malades, U1151, Université Paris Descartes, France

⁴Centre Régional de Dépistage Néonatal de Bretagne- CHU Pontchaillou - Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-Rennes, France

⁵Laboratoire des Explorations Fonctionnelles Endocriniennes, Hôpital Armand Trousseau, AP-HP
Sorbonne Université, Paris, France

⁶Sorbonne Université, INSERM, Centre de Recherche Saint-Antoine, Paris, France
1. ⁷Service d'Endocrinologie, Diabétologie, Gynécologie Pédiatriques, Hôpital Necker-Enfants

2. Malades, AP-HP Centre Université de Paris Cité, Paris, France

3. ⁸Institut IMAGINE, Hôpital Necker-Enfants Malades, Université Paris Descartes, France

4. ⁹FIRENDO group

5. **Short title:**

6. Enriched steroid profiling enhances CAH newborn screening.

7. **Keys words**

8. Congenital adrenal hyperplasia, newborn screening, dried blood spot, 17-hydroxyprogesterone,

9. 11-ketotestosterone, 21-deoxycortisone, 21-hydroxylase deficiency, 11-oxygenated androgens.

10. **Word count :** 3500

11.

12. **Abstract**

13. **Background**

14. A major limitation of newborn screening (NBS) for congenital adrenal hyperplasia (CAH) is the lack of specificity of the fluoroimmunoassay (FIA) currently used for 17-hydroxyprogesterone (17OHP) determination. This issue is more pronounced in newborns, due to elevated levels of interfering compounds.

18. FIA at our NBS centre in Ile-de-France has a false positive rate of around 80%, and a predictive positive value (PPV) of 16% for first- and second-tier measurements from dried blood spots (DBS). Recently, tandem mass spectrometry (LC-MS/MS) has gained international recognition as a complementary tool to FIA testing. Currently, the most frequently used biomarkers are 17OHP
1 and 2-deoxycortisol, used either alone or in combination with steroid ratios such as cortisol or
2 4-androstenedione.

3 Concurrently, the class of 11-oxygenate-androgens—such as 11-ketotestosterone—and more
4 recently 11-oxygenate pregnanes—such as 21-deoxycortisone—have attracted growing interest
5 in the diagnosis of 21-hydroxylase deficiency. These derivatives result from the combined action
6 of 11-beta hydroxysteroid dehydrogenase and 11-beta hydroxylase.

## 7 Methods

8 We propose a revisited LC-MS/MS steroid profile, enriched with these classes of biomarkers, to
9 be included in the CAH NBS algorithm. This combination could be used as a multi-steroid
10 approach implemented using a machine learning model.

11 Results

12 Our preliminary results suggest that these oxygenated androgen/pregnane steroids are
13 significantly discriminative to streamline the NBS process for CAH. We have demonstrated this
14 in two different NBS centres, in the greater Paris region and in Brittany, France.

15 Conclusion

16 This new algorithm could have an important impact on reducing the number of recall and family
17 stress related to NBS.

18 Significance statement

19 Our study demonstrates that integrating 11-ketotestosterone and 21-deoxycortisone into LC-
20 MS/MS-based newborn screening for congenital adrenal hyperplasia (CAH) markedly improves
21 diagnostic specificity. Unlike traditional biomarkers, these adrenal-specific steroids showed no
22 overlap between affected and unaffected newborns, providing superior discriminative power.
23 By incorporating these markers into a machine-learning model, we achieved robust
24 stratification across two independent French screening centers, reducing false positives and
25 minimizing unnecessary recalls. This novel multi-steroid approach addresses the major
26 limitation of current screening—poor specificity of immunoassays—while preserving sensitivity.
1 Our findings highlight a clinically relevant strategy to optimize CAH newborn screening, with the

2 potential to lessen family stress, improve early management, and serve as a model for

3 algorithmic biomarker-driven screening in endocrinology.

4 **Abbreviations**

5 11β-OHA4 : 11β-hydroxyandrostenedione

6 11DF: 11-deoxycortisol

7 11-Keto: 11-ketotestosterone

8 17OHP: 17-hydroxyprogesterone

9 21DF: 21-deoxycortisol

10 21OHD: 21-hydroxylase deficiency

11 A4: delta-4-androstenedione

12 CAH: congenital adrenal hyperplasia

13 CRDN: centre Régional de Dépistage

14 D: day

15 DBS: drop blood spot

16 F: cortisol

17 FIA: fluoro-immunoassays

18 GA: gestational age

19 LC-MS/MS: liquid chromatography- mass spectrometry
1 NBS: newborn screening

2 PPV: predictive positive value

3 W: week

4

5 Introduction

6 Congenital Adrenal Hyperplasia (CAH) is a rare autosomal recessive disorder caused by
7 mutations in genes encoding enzymes critical for cortisol and aldosterone synthesis, typically
8 resulting in adrenal insufficiency and androgen excess. The most prevalent form, accounting for
9 approximately 95% of cases, is 21-hydroxylase deficiency (21OHD) (1).

10 Since 1995, neonatal screening (NBS) for CAH in France has relied on measuring 17-
11 hydroxyprogesterone (17OHP), a precursor of 21-hydroxylase, in dried blood spots (DBS) using
12 fluoro-immunoassays (FIA) (2). Samples exceeding the analytic cut-off undergo a second-tier FIA
13 with duplicate punches. Despite this step, false-positive rates remain high—about 80% in France
14 (2,3) (Figure 1). The Endocrine Society recommends gestational age (GA)-dependent cut-offs (4).
15 For newborns with GA <38 weeks, a second DBS is requested, while those ≥38 weeks are
16 recalled for clinical evaluation.

17 This study analyzes data from two regional French NBS centers: CRDN-IdF (Necker-Enfants
18 Malades Hospital, Paris), the largest in the country covering approximately 25% of all live births
19 nationwide, and CRDN-Bretagne (CHU Rennes). The positive predictive value (PPV) for CAH at
20 CRDN-IdF is ~16%, compared to ~5% at CRDN-Bretagne. Confirmatory diagnostics at recall
1 involve serum steroid profiling by liquid chromatography-tandem mass spectrometry (LC-MS/MS), with 17OHP as the principal marker (2) (Figure 1).

3 Globally, some centers have adopted LC-MS/MS-based steroid profiling of DBS as a second-tier test to improve specificity (5,6). Broader steroidome profiling is emerging as a strategy to enhance diagnostic precision and enable stratified diagnosis of CAH (7–9). One promising biomarker, 21-deoxycortisol (21DF), specific to adrenal tissue, has shown high diagnostic accuracy for 21OHD (10–12). Composite ratios, such as [21DF+17OHP]/cortisol (F), [A4+17OHP]/F, 17OHP/F, and 17OHP/11-deoxycortisol (11DF), have been proposed to increase screening performance, with reported PPVs of 46% and 56% (5).

10 Beyond 21DF, the potential of 11-oxygenated androgens in NBS remains underexplored. In 21OHD, androgen precursors are converted by adrenal-specific 11β-hydroxylase into metabolites such as 11-ketotestosterone (11-Keto), 11β-hydroxytestosterone (11βOHT), and 11β-hydroxyandrostenedione (11βOHA4) (13). We also identified 21-deoxycortisone (21DE), a distinct adrenal-derived pregnane steroid detectable in DBS. Unlike 21-deoxycortisol (21DF), which is a cortisol precursor, 21DE likely arises from 21DF through sequential oxidation by 11β-hydroxysteroid dehydrogenase type 2 (11β-HSD2) and possibly 11β-hydroxylase activity (14). As a marker with exclusive adrenal origin, 21DE may complement existing biomarkers, refining the steroid signature of CAH, and potentially enhancing screening from the initial DBS while reducing unnecessary recalls and associated family stress.

20 Here, we introduce an expanded LC-MS/MS-based steroid profile encompassing 16 analytes, including adrenal-specific steroids. We further integrate machine-learning models to explore
1 both univariate biomarkers (e.g., 11-Keto, 21DE) and multivariate metabolic scores, aiming to

2 improve the specificity and accuracy of CAH screening.

3 **Materials and Methods**

4 **1. French Newborn Screening Algorithm for CAH (21OHD)**

5 In France, newborns <32 weeks of gestational age (GA) are not screened for 21OHD. For others,
6 DBS is collected at 48-72 h and 17OHP measured by FIA (AutoDELFIA® or GSP®). The screening
7 algorithm uses GA-adjusted thresholds and second-tier FIA. At CRDN-IdF, a routine second DBS
8 at day 10 is collected to limit false-positive recalls. A newborn is screen-positive if mean 17OHP
9 remains elevated (≥25 nmol/L for GA ≥38W), leading to clinical referral. Diagnosis is confirmed
10 by serum steroid profiling using LC-MS/MS at the reference center (Figure 1).

11 **2. Dried blood samples**

12 The study was approved by the French national newborn screening program and a non-
13 objection certificate has been obtained from newborn's relatives after full explanation of the
14 purpose and nature of all procedures used (number DRCI-COLLAB-24-1248). The study involved
15 a retrospective analysis using DBS already tested in two newborn screening centers CRDN-IdF
16 and CRDN-Bretagne for CAH screening (Figure 2).

17 At CRDN-IdF, 207 DBS samples collected between April 2019 and March 2023 were analyzed:

18 • 75 control samples (37 F / 38 M) screened negative by FIA at D3.

19 • 39 paired samples at D3 and D10 (17 F / 22 M) from newborns initially FIA-positive at D3
20 but negative at D10.
1. 13 samples (6 F / 7 M) from newborns FIA-positive at D3 but later ruled out for 21OHD
2. by serum LC-MS/MS at recall.

3. 41 samples (18 F / 23 M) from confirmed 21OHD cases, diagnosed via serum steroid profiling; genotype data were available for 33 cases.

5. At CRDN-Bretagne, 128 DBS samples from false-positive cases were selected as a replication cohort (collected Dec 2020–Jan 2022):

7. 64 paired samples at D3 and D10 (28 F / 36 M) from newborns initially FIA-positive at D3 but negative at D10.

9. All DBS were stored at 4°C, shielded from light, for up to one year. Infants treated with glucocorticoids were excluded.

## 3. Steroid Profile Using Tandem Mass Spectrometry

12. Steroid profiling from DBS was developed by the Clinical Metabolomics Department in a COFRAC-accredited laboratory specializing in metabolic and endocrine disorders. A total of 16 steroids were quantified using LC-MS/MS: 17OHP, pregnenolone, progesterone, 17-hydroxypregnenolone, 16-hydroxyprogesterone, A4, 11β-hydroxyandrostenedione (11β-OHA4), testosterone, 11β-hydroxytestosterone, DHEA, cortisol (F), cortisone, 11DF, 21DF, and two additional biomarkers with high diagnostic relevance in 21OHD: 21-deoxycortisol (21DE), 11-ketotestosterone (11-Keto).

19
a. Sample preparation:

The extraction protocol was adapted from one previously published by our laboratory (3), and from the de Hora study (15). Briefly A 6 mm DBS punch was eluted in 300 µL of 0.9% NaCl. After shaking for 20 min, 30 µL of deuterated internal standard and 2 mL of hexane-ethyl acetate (90:10, v/v) were added. Following 30 min of incubation at room temperature, samples were centrifuged at 5000 rpm for 10 min at 4°C. The supernatant (150 µL) was dried under a stream of air at 37°C, then reconstituted in 100 µL methanol/water (50:50, v/v).

b. LC-MS/MS Conditions :

Steroids were separated using a Coreshell C18 column (Kinetex, 2.6 µm, 100×2.1 mm, Phenomenex). Detection was performed on Shimadzu Nexera XR and ABSciex Triple Quad 6500 instruments. Data were analysed with MultiQuant software (v3.0, ABSciex), using built-in QC rules to flag outliers based on standard accuracy, ion ratios, and quantification limits. Calibration curves were processed using least-squares regression with 1/x weighting. To ensure analytical specificity, particularly for 11-oxygenated androgens, separation of isobaric and structurally related steroids was verified by monitoring both retention times and ion ratios. Chromatographic resolution was confirmed for all analytes, as illustrated in Supplementary Figure 3.

18

c. Statistical Analysis :

Steroid concentrations between confirmed 21OHD, control, and false-positive groups were compared using Kruskal-Wallis tests (XLSTAT, Lumivero, France), with significance set at p < 0.05.

20

21

22
# 4. Modelling set program

A steroid profiling model was developed using LC-MS/MS analysis of 16 steroids from dried blood spots (DBS) to discriminate 21OHD-affected from unaffected newborns. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were applied to a discovery cohort (2019–2021, CRDN-IdF) including confirmed 21OHD cases (n=32) and controls (n=75). Model performance was assessed using accuracy and confusion matrices across three replication cohorts (2 cohorts from CRDN-IdF: 2019–2023 and 1 cohort from CRDN-Bretagne: 2020–2022), which included both false-positive and confirmed cases, with LC-MS/MS profiling performed on DBS collected at day 3 and day 10. The same inclusion and exclusion DBS criteria were used for each cohort. The replication cohorts were not strictly independent, as only the Clinical Metabolomic department at Saint Antoine Hospital, Sorbonne University, AP-HP Paris, performed the LC-MS/MS steroid profile analysis.

13

# 14 Results

A set of DBS from 207 newborns screened for CAH at two French newborn screening centres — CRDN-IdF and CRDN-Bretagne—with the usual FIA algorithm was retrospectively analysed by LC-MS/MS using a specially adapted extraction process. Sixteen previously selected steroids were measured. Our aim was to test the relevance of two new steroid biomarkers to improve NBS for CAH by implementing a second-tier measurement by LC-MS/MS.

20

# 1. Algorithm using first-tier FIA 17OHP and second-tier LC-MS/MS 17OHP
1 The first step was to compare the discriminative power of 17OHP and 21DF detected in DBS.

2 These were considered both alone and associated with steroid ratios such as [21DF+17OHP]/F,

3 [A4+17OHP]/F), 17OHP/F, 17OHP/11DF, as recently described for second-tier determination.

4 To achieve this, we compared 32 newborns affected by 21OHD to 39 unaffected false-positive

5 newborns, initially screened positive on the first DBS but negative on the second using FIA

6 17OHP determination at CRDN-IdF. The two gold standard biomarkers, 17OHP and 21DF, are

7 highly discriminative between 21OHD-affected and false-positive newborns. Moreover, for

8 those who had a second DBS performed, these biomarkers also improved screening accuracy.

9 Nevertheless, there is an overlap in the dosages of 17OHP and 21DF between 21OHD-affected

10 newborns and those with false positive results in the first and second DBS using FIA 17OHP

11 determination. Surprisingly, five newborns were false negatives regarding 21DF alone (Figure 3).

12 Four steroid ratios have been commonly used to confirm the selective groups based on 17OHP

13 and 21DF in the second-tier determination.

14 False-positive screened newborns (FP 1st DBS group) were significantly segregated from those

15 affected by 21OHD, but with a high degree of overlap. The different steroid ratios

16 [21DF+17OHP]/F, [A4+17OHP]/F and 17OHP/F demonstrate even greater overlap in the FP 2nd

17 DBS group (Figure 3), partly because cortisol also decreases with age in newborns

18 (Supplementary Figure 3). The 17OHP/11DF ratio exhibited the least overlap, and significantly

19 decreased on the second DBS.

20 Univariate analysis of all panel steroids showed significant differences between 21OHD and

21 control newborns, except for pregnenolone (Supplementary Table 1). In false-positive cases, all
steroids differed from the 21OHD group except testosterone, progesterone, and 11-DF

(Supplementary Table 3). In recalled newborns, androgen levels—along with progesterone, 11-

DF, and delta-5 steroids—were not significantly different from those in 21OHD (Supplementary

Table 2).

## 2. Additional biomarkers: 11-ketotestosterone and 21-deoxycortisone

In a second step, we focused on two other biomarkers, 11-Keto and 21-DE. We compared the

overlap of circulating 11-Keto and 21DE concentrations between control and 21OHD groups

(n=39 and n=32, respectively, CRDN-IdF) (Figure 4).

Although there were significant differences between the 21OHD group and controls in 17OHP,

21DF, and steroid ratios, these two markers showed no overlap. As observed for 21DF, 21DE

DBS concentrations were below the lower limit of quantification (LLOQ) (0.10 ng/mL) in the two

false positive screened groups (FP 1st DBS and FP 2nd DBS) and in controls (except for two DBS

samples in which the concentration were slightly above LLOQ 0.15 ng/mL and 0.20 ng/mL; data

not shown). 11-Keto concentrations were below LLOQ (0.10 ng/mL) in one false-positive

screened group (replication cohort 2).

However, unlike 21DF or other steroid ratios, the absence of overlapping 21DE and 11-Keto

concentrations between the 21OHD group and the two false-positive screened groups

suggested a superior discriminative power. This was further confirmed by the ROC curve

analysis (Supplementary Table 6), where both 21DE and 11-Keto showed high area under the

curve (AUC) values of 1, respectively, outperforming traditional biomarkers such as 17OHP,
1 21DF or steroid ratios. These results highlight the robustness of 21DE and 11-Keto as key discriminators in neonatal screening for 21OHD.

3 Moreover, it is important to note that the concentrations of 11-Keto and 21DE in DBS were significantly higher than those of 21DF, with average levels in 21OHD patients of 12 and 25.4 ng/mL, respectively, compared to only 3.7 ng/mL for 21DF.

6 Two isolated 21OHD-affected newborns presented low 21DE concentrations (0.31 ng/mL and 0.45 ng/mL), but rather high concentrations of 11-Keto (2.24 ng/mL and 2.01 ng/mL). Similarly, one 21OHD-affected newborn exhibited a lower concentration of 11-Keto (0.58 ng/mL) but a higher concentration of 21DE (3.91 ng/mL).

### 3. Modeling approach

Although these two biomarkers individually provide satisfactory stratification performance, a multivariate approach incorporating a broader range of steroids from the biosynthetic pathway may enhance diagnostic accuracy, particularly for borderline cases near the decision threshold.

#### a. Model construction

We first conducted a descriptive analysis of the modelling cohort, comprising 32 newborns diagnosed with 21OHD and 75 unaffected controls from the CRDN-IdF cohort. Steroid profiling was performed using the first DBS sample, analysed by the FIA algorithm. An unsupervised multivariate analysis using PCA, including all 16 measured steroids, was then performed, yielding an R²X of 0.702 (Supplemental Figure 1). This initial analysis revealed a clear separation between 21OHD-affected and unaffected individuals, suggesting the strong discriminative potential of the steroid profile.
1 We then conducted a supervised multivariate analysis using OPLS-DA on the modelling cohort
2 (Figure 5A). The observations were projected in the plane of maximum variance (R²X = 0.732),
3 indicating robust explanatory and predictive power. Internal cross-validation confirmed the
4 strong predictive performance of the model (Q² = 0.941). Newborns with 21OHD were clearly
5 segregated on the right side of the score plot (purple dots), while unaffected individuals
6 clustered separately in the left quadrants (black dots), demonstrating excellent group
7 separation without any overlap.

8 To identify the most influential variables contributing to class separation, we calculated the
9 coefficient weights from the OPLS-DA regression model (Figure 5B). Variables with the highest
10 absolute coefficients were considered the most discriminatory. The top five contributors to
11 21OHD discrimination were 21-DE, 11-Keto, 17-OHP, 11β-OHA4, and 21-DF.

12 **b. Model replications**

13 As shown in Figure 2, we first replicated our model on newborns from CRDN-IdF to test its
14 robustness on the most problematic screening situations—false positives from the FIA-based
15 algorithm. The model was validated after replication with replication cohort 1, which included
16 20 newborns (9 affected by 21OHD and 11 false positives) for whom steroid profiles were
17 performed on the 1st DBS at D3 (Figure 6A). Replication cohort 2, including false-positive
18 screened newborns (n=39 at D3 and n=39 at D10), was also projected into the prediction model.
19 The data from the validation set were not used to refit the model; they were only used to test
20 its accuracy. Newborns from the replication cohort were labelled according to their final
diagnosis: 21OHD affected (pink triangle) and false positive screened (1st DBS = dark blue,
21 second DBS = light blue). All newborns affected by 21OHD from the replication cohorts were
22
1 segregated in the same area of the score plot as 21OHD affected newborns from the modelling
2 cohort (purple dots). All false-positive screened neonates were correctly segregated on the left
3 side, as a 21OHD unaffected group (black dots). The misclassification table (Figure 6B)
4 summarises the number of observations in each group, the FIA 17OHP results in the 1st and 2nd
5 DBS, and the correct classification rate after the measurement of the steroid profiles with LC-
6 MS/MS and modelling approach. All newborns with concentrations of 17OHP close to the cut-
7 off values with the FIA-FIA algorithm were correctly classified based on the LC-MS/MS machine
8 learning screening test on the first DBS. Interestingly, even though they were on the same side
9 of the quadrant as the controls, the newborns screened as false positives at the 1st DBS were
10 segregated below. With the 2nd DBS, at D10, they were segregated in the same area as 21OHD
11 unaffected newborns. The observation labels were then sized according to their respective DBS
12 concentrations of 17OH-pregnenolone (Supplemental Figure 2). The higher the 17OH-
13 pregnenolone concentration, the larger the dot size. A very marked vertical gradient of the label
14 size was observed running from top (low 17OH-pregnenolone concentrations) to bottom (high
15 17OH-pregnenolone concentrations) in the left quadrants (false positives and controls). As
16 anticipated, FIA false-positive newborns showed high 17OH-pregnenolone concentrations.

17 To assess potential recruitment bias, we tested the model on an independent cohort
18 (replication cohort 3) from the CRDN-Bretagne screening program, including 64 FIA false-
19 positive newborns. Steroid profiling was performed on both the initial DBS (day 3) and the
20 second sample (day 10). The model was not retrained on this data. All false positives were
21 correctly classified as unaffected, regardless of sampling day. Visually, all except two cases
22 clustered with the control group; one showed elevated 16-hydroxyprogesterone, and the other
1 had borderline 21DE levels. Despite these deviations, both were correctly classified, confirming

2 model robustness. These anomalies reflect dimensionality reduction effects, not classification

3 errors. The classification error table (Figure 7B) details group numbers and FIA 17OHP levels.

4 Overall, the model achieved 100% correct classification in this independent set using LC-MS/MS

5 steroid profiling.

6 Discussion

7 In France, CAH newborn screening relies on 17OHP measurement by FIA in both tiers (FIA-FIA),

8 with age-adjusted thresholds to improve sensitivity in preterm infants (2). However, this

9 approach suffers from high false-positive rates due to cross-reactivity with 17OHP precursors

10 (11,16,17).

11 The use of LC-MS/MS allows for more specific second-tier steroid profiling building on our

12 previous DBS extraction method (3), we assessed 16 steroids from first-tier samples to

13 distinguish 21OHD cases from controls, and validated the findings in a larger cohort including

14 confirmed cases, FIA false positives, and true negatives.

15 We developed an LC-MS/MS method for DBS steroid profiling to refine second-tier CAH

16 screening, following international implementation trends (6,15–18). Our goal was to improve

17 sensitivity while minimizing false-positive recalls, which can be medically and economically

18 burdensome. Currently, the median delay for a second DBS is 18 days —well beyond the optimal

19 diagnostic window of 10 days to prevent salt-wasting crises.
We first assessed the discriminatory power of steroids and ratios from the literature, such as 17OHP, 21DF, [21DF+17OHP]/F, [A4+17OHP]/F, and 17OHP/F (5). While statistically different between groups, these markers showed overlaps, echoing findings by de Hora et al. (5) and Lind-Holst et al. (5), who reported PPVs of 46% and 56%, respectively. Additionally, all non-21OHD samples had 21DF levels below LOQ (0.675 ng/mL), but some affected newborns also presented concentrations below LOQ (15,18).

To increase specificity, we incorporated two adrenal-specific biomarkers, 11-ketotestosterone (11-Keto) and 21-deoxycortisone (21DE), both elevated in 21OHD. 11-Keto, a potent androgen derived from 11-β-OHA4, is abundant in prepubertal children and has shown diagnostic utility in serum and DBS samples (13,15). Our findings align with Kamrath et al. (2018) (19) and Turcu et al. (2016) (13), confirming that 11-oxygenated androgens, particularly 11-Keto, dominate the androgen excess in CAH, even under treatment, supporting their use in both diagnosis and follow-up. We also previously identified 21DE as a sensitive and specific serum marker of 21OHD, likely produced from 21DF via placental 11β-HSD2 (14,20,21). Their reliable quantification by LC-MS/MS in newborns reinforces their potential for second-tier NBS.

These two biomarkers showed excellent discriminatory power without overlap between affected and unaffected groups, contrasting with traditional markers. Their neonatal concentrations were approximately four times (21DE) and three times (11-Keto) higher than 21DF, offering more robust detection.

Despite the interesting performance of 21DE and 11-Keto, some isolated 21OHD-affected newborns presented low 21DE concentrations (0.31 ng/mL and 0.45 ng/mL), close to the LOQ.
1 (0.1 ng/mL). Meanwhile, the same newborns presented rather high concentrations of 11-Keto

2 (2.24 ng/mL and 2.01 ng/mL). Similarly, one 21OHD newborn exhibited a lower concentration of

3 11-Keto (0.58 ng/mL) but a higher concentration of 21DE (3.91 ng/mL). For this specific

4 situation, close to the diagnostic boundary, it seems important either to reason in terms of

5 steroid ratios or to consider a multivariate modelling approach. This aligns with our previous

6 work proposing algorithmic rather than cut-off-based diagnostics. (22,23)

7 In the second phase, we implemented a machine-learning model to optimize marker

8 combinations. These models not only stratify patients but also rank marker importance. While

9 not all 16 steroids may be needed, the data-driven selection process highlighted 21DE and 11-

10 Keto as the most discriminant, reinforcing their utility as diagnostic end products with adrenal

11 specificity.

12 In learning models, it is important not to reason solely in terms of cross-validation, because of

13 the risk of mathematical bias (24). One of this study's strengths is its multicentric design,

14 including national NBS centres using slightly different algorithms (Figure 1). The models were

15 systematically tested both by cross-validation and by replication cohorts. Results were

16 confirmed in all replication situations, even the multi-centre CRDN-Bretagne cohort, confirming

17 the robustness of the model.

18 Although the present study is limited by the small number of participants and by a validation

19 cohort that was not entirely independent (using the same analytical centre), these results

20 represent a promising path for CAH NBS and could serve as the basis for other models.
1 As emphasized by de Hora et al. (5), caution is needed in interpreting retrospective DBS data
2 due to storage effects on steroids like 11DF, F, and 17OHP (15,25,26). To mitigate this, our DBS
3 samples were stored under controlled conditions (≤1 year, 4°C, shielded from light/humidity).

4 We also evaluated the impact of gestational and postnatal age on key biomarkers. In replication
5 cohort 2 (preterm neonates <36 weeks GA), 11-Keto and 21-DE levels did not differ significantly
6 between early (day 3) and late (day 20) DBS collections, suggesting minimal influence of
7 postnatal age. Moreover, when comparing term and preterm controls, 21-DE remained stable,
8 while 11-Keto was slightly higher in term infants (Supplementary Table 5). These findings
9 suggest that 11-Keto and 21-DE are less age-dependent than traditional markers like 17OHP,
10 supporting their utility for second-tier screening.

11 Further studies should assess this approach in very preterm infants (<32 weeks gestational age),
12 who are currently under-screened in France, representing a missed opportunity for early CAH
13 management. Economic evaluation is also crucial: while LC-MS/MS may increase upfront costs,
14 it could reduce endocrine consultations, family stress, and false-positive recalls (e.g., emergency
15 visits and lab tests).

## 16 Conclusion

17 Second-tier LC-MS/MS improves the sensitivity and specificity of CAH NBS, but conventional
18 steroid profiles alone are insufficient to fully discriminate 21OHD cases. By introducing 21DE
19 and 11-Keto, we significantly enhance diagnostic clarity. Ultimately, a multivariate approach
1 using these biomarkers offers a promising strategy for accurate, early detection—even in
2 complex or borderline cases.

3 Acknowledgments

4 The authors gratefully acknowledge the support of the International Fund for Congenital Adrenal
5 Hyperplasia (IFCAH), which awarded this project a research grant. We also thank the French
6 Anti-Doping Agency (AFLD) and Association Surrénales for their financial supports.

7 **Disclosure statement:** The authors declare that there is no conflict of interest that could be
8 perceived as prejudicing the impartiality of the research reported.

9 **Funding statement:** The authors gratefully acknowledge the support of the International Fund
10 for Congenital Adrenal Hyperplasia (IFCAH), which awarded this project a research grant. We
11 also thank the French Anti-Doping Agency (AFLD) and Association Surrénales for their
12 financial supports.

13 All authors confirm that this study was conducted in accordance with the principles of the
14 Declaration of Helsinki of the World Medical Association.

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33 26. SciELO - Brazil - How Long are Residual Newborn Screening Specimens Useful for Retesting when Stored in Suboptimal and Uncontrolled Conditions of Temperature and Humidity? How Long are Residual Newborn Screening Specimens Useful for Retesting when Stored in Suboptimal and Uncontrolled Conditions of Temperature and Humidity? [Internet]. [cité 13 févr 2024]. Disponible sur: https://www.scielo.br/j/jiems/a/8CXB5Qdjnvk74KWdcrjv9Jg/?lang=en
1. Figure 1.

2. A. CRDN-IdF (Centre Régional de Dépistage Néonatal-Ile-de-France, Necker-Enfants Malades Hospital, APHP

3. Centre, Université Paris Cité, Paris) algorithm

4. B. CRDN-Bretagne (Biochemistry Laboratory of Pontchaillou Hospital, CHU Rennes, Brittany) algorithm

5. Figure 2. Study overview. The first step consisted of selecting 16 discriminant steroids measured by

6. highperformance liquid chromatography associated with tandem mass spectrometry (LC-MS/MS) on the first

7. dried blood spot (DBS), from 32 newborns with 21OHD diagnosis confirmed by genotyping and 75 DBS from

8. newborns with FIA 17OHP negative results (controls). Orthogonal partial least squares (OPLS-DA) analysis

9. was then performed on the final cohort (n=166) to build a machine learning model that increased the

10. discriminant power between 21OHD and unaffected newborns (modelling cohort). The second step consisted

11. of testing OPLS-DA on the 1st DBS of an independent cohort (replication cohort 1) of 21OHD newborns and

12. screened positive newborns (false positive, FIA 17OHP positive result at 1st DBS, with 21OHD diagnosis

13. excluded at recall after LC-MS/MS serum steroid determination). The third step consisted of testing the 1st

14. and 2nd DBS of an independent cohort (replication cohort 2) from 39 screened positive newborns (false

15. positive, FIA-17OHP positive result on the 1st DBS, then FIA-17OHP negative on the 2nd DBS). Finally, the

16. last step consisted of testing the 1st and 2nd DBS of an independent cohort (replication cohort 3) from

17. another screening centre (CRDN-Bretagne) with 64 screened positive newborns (false positive, FIA-17OHP

18. positive result on the 1st DBS, then FIA-17OHP negative on the 2nd DBS)

19. Figure 3. Scattergram of 17-hydroxyprogesterone and 21-deoxycortisol from DBS samples and four steroid

20. ratios measured by tandem mass spectrometry on the 1st DBS from CRDN-IdF for 21OHD newborns (n=32)

21. and false positive screened newborns on the 1st and 2nd DBS (n=39) (FIA 17OHP positive on the first DBS,

22. then negative on the second). All steroids are expressed in ng/mL (log scale). p-value legend: * p < 0.05; **

23. p < 0.01; *** p < 0.001; **** p < 0.0001

24.

25. Figure 4. Scattergram of new biomarkers, 21-deoxycortisone and 11-ketotestosterone, tested on the 1st

26. DBS from CRDN-IdF for 21OHD-affected newborns (n=32) and on the 1st and 2nd DBS for false positive FIA

27. 17OHP-screened newborns (n=39) (i.e. screened positive on the first DBS then negative on the second DBS.

28. All steroids are expressed in ng/mL (log scale). p-value legend: * p < 0.05; ** p < 0.01; *** p < 0.001;

29. **** p < 0.0001

30.

31. Figure 5: Orthogonal partial least squares discriminant model (OPLS-DA) with 32 21OHD and 75 unaffected

32. newborns from the modelling cohort (CRDN-IdF), using 16 steroid concentrations in DBS samples. A: score

33. plot generated with a supervised approach from the known 21OHD-affected group (n=32) and unaffected

34. group (n=75). R²=0.735, Q²=0.94. B: variable importance for projection (VIP) plot displaying the

35. contribution of the 16 steroids to the model

36.

37. Figure 6. Score plots from orthogonal partial least squares discriminant analysis (incremental process with

38. model training and replication) of 9 21OHD-affected newborns, 39 false positives, and 11 recalled newborns

39. from CRDN-IdF, assessed by their DBS steroid profile in tandem mass spectrometry (LC-MS/MS) on the first

40. DBS (except for the false positive group (replication cohort 2), for whom the 1st and 2nd DBS were

41. analysed).

42. A: Score plot of the modelling cohort (21OHD (n = 32, purple dots) and controls (n = 75, black dots))

43. superimposed with newborns from the replication cohorts: 21OHD (n = 9, pink triangle) and FIA false

44. positive (1st DBS (n = 50, dark blue triangle) and 2nd DBS (n=39, light blue triangle)).

45. B: Model prediction results. Assignment for each class (21OHD, controls and FIA false positives) of the

46. newborns from the modelling and replication cohorts

47.

48. Figure 7. Score plots from orthogonal partial least squares discriminant analysis (incremental process with

49. model training and replication) of 21OHD-affected and control newborns, assessed by their DBS steroid

50. profile in tandem mass spectrometry (LC-MS/MS) performed on the 1st and 2nd DBS. A: Score plot of the

51. modeling cohort (21OHD (n = 32, purple dots) and controls (n = 75, black dots)) superimposed with the

52. newborns from the replication cohort 3: FIA false positive (1st DBS (n = 64, dark red triangle) and 2nd DBS

53. (n=64, dark red triangle)). B: Model prediction results. Assignment for each class (21OHD, controls and FIA

54. false positives) of the newborns from the modelling and replication cohort

55.

56.
A

<figure><img src="image_2.png" /></figure>

B

<figure><img src="image_3.png" /><figcaption>Figure 1
309x182 mm (x DPI)</figcaption></figure>

1

2

3

4

<figure><img src="image_4.png" /><figcaption>Figure 2
342x173 mm (x DPI)</figcaption></figure>

5

6

7

8
<figure><img src="image_5.png" /><figcaption>Figure 3
137x190 mm (x DPI)</figcaption></figure>

1

2

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<figure><img src="image_6.png" /><figcaption>Figure 4
259x135 mm (x DPI)</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Figure 5
335x116 mm (x DPI)</figcaption></figure>
A

<figure><img src="image_8.png" /></figure>

B

<table><caption>Figure 6
340x135 mm (x DPI)</caption><thead><tr><th></th><th></th><th>n</th><th>Screening FIA DBS1</th><th>Screening FIA DBS2</th><th>Correct classification rate LC-MS/MS</th></tr></thead><tbody><tr><td rowspan="2">Modeling cohort</td><td>21OHD</td><td>32</td><td>Positive</td><td>Positive</td><td>100%</td></tr><tr><td>Controls</td><td>75</td><td>Negative</td><td>Negative</td><td>100%</td></tr><tr><td rowspan="2">Replication cohort 1</td><td>21OHD</td><td>9</td><td>Positive</td><td>Positive</td><td>100%</td></tr><tr><td>Recalled</td><td>11</td><td>Positive</td><td>Positive</td><td>100%</td></tr><tr><td rowspan="2">Replication cohort 2</td><td>False positives</td><td>39</td><td>Positive</td><td></td><td>100%</td></tr><tr><td></td><td>39</td><td></td><td>Negative</td><td>100%</td></tr></tbody></table>

A

<figure><img src="image_9.png" /></figure>

B

<table><caption>Figure 7
214x89 mm (x DPI)</caption><thead><tr><th></th><th></th><th>n</th><th>Screening FIA DBS1</th><th>Screening FIA DBS2</th><th>Correct classification rate LC-MS/MS</th></tr></thead><tbody><tr><td rowspan="2">Modeling cohort</td><td>21OHD</td><td>32</td><td>Positive</td><td>Positive</td><td>100%</td></tr><tr><td>Controls</td><td>75</td><td>Negative</td><td>Negative</td><td>100%</td></tr><tr><td rowspan="2">Replication cohort 3</td><td>False positives</td><td>64</td><td>Positive</td><td></td><td>100%</td></tr><tr><td></td><td>64</td><td></td><td>Negative</td><td>100%</td></tr></tbody></table>

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