# Contribution of heterozygous PCSK1 variants to obesity and implications for precision medicine: a case-control study

Lise Folon, Morgane Baron, Bénédicte Toussaint, Emmanuel Vaillant, Mathilde Boissel, Victoria Scherrer, Hélène Loiselle, Audrey Leloire, Alaa Badreddine, Beverley J. Balkau, et al.

► To cite this version:

Lise Folon, Morgane Baron, Bénédicte Toussaint, Emmanuel Vaillant, Mathilde Boissel, et al. Contribution of heterozygous PCSK1 variants to obesity and implications for precision medicine: a case-control study. The Lancet. Diabetes & Endocrinology, 2023, 11 (3), pp.182-190. 10.1016/S2213-8587(22)00392-8. hal-04195164

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# Contribution of heterozygous PCSK1 variants to obesity and implications for precision medicine: a case-control study

Lise Folon, MSc<sup>1,2*</sup>, Morgane Baron, PhD<sup>1,2*</sup>, Bénédicte Toussaint, MSc<sup>1,2</sup>, Emmanuel Vaillant, MSc<sup>1,2</sup>, Mathilde Boissel, MSc<sup>1,2</sup>, Victoria Scherrer<sup>1,2</sup>, Hélène Loiselle<sup>1,2</sup>, Audrey Leloire<sup>1,2</sup>, Alaa Badreddine, MSc<sup>1,2</sup>, Beverley Balkau, PhD<sup>3</sup>, Guillaume Charpentier, MD<sup>4</sup>, Sylvia Franc, MD<sup>4,5</sup>, Prof Michel Marre, PhD<sup>6,7</sup>, Soulaimane Aboulouard, MSc<sup>2,8</sup>, Prof Michel Salzet, PhD<sup>2,8</sup>, Mickaël Canouil, PhD<sup>1,2</sup>, Mehdi Derhourhi, PhD<sup>1,2</sup>, Prof Philippe Froguel, PhD<sup>1,2,9**</sup>, Amélie Bonnefond, PhD<sup>1,2,9**</sup>

<sup>1</sup>Inserm UMR1283, CNRS UMR8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, Lille University Hospital, Lille, France.

<sup>2</sup>Université de Lille, Lille, France.

<sup>3</sup>Paris-Saclay University, Paris-Sud University, UVSQ, Center for Research in Epidemiology and Population Health, Inserm U1018 Clinical Epidemiology, Villejuif, France.

<sup>4</sup>CERITD (Centre d'Étude et de Recherche pour l'Intensification du Traitement du Diabète), Evry, France.

<sup>5</sup>Department of Diabetes, Sud-Francilien Hospital, Paris-Sud University, Corbeil-Essonnes, France.

<sup>6</sup>Institut Necker-Enfants Malades, INSERM, Université de Paris, Paris, France.

<sup>7</sup>Clinique Ambroise Paré, Neuilly-sur-Seine, France.

<sup>8</sup>Inserm U1192 - Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), Lille, France

<sup>9</sup>Department of Metabolism, Imperial College London, London, United Kingdom.

Correspondence to: Amélie Bonnefond (amelie.bonnefond@cnrs.fr), UMR1283 - Pôle Recherche; 1 place de Verdun ; 59045 LILLE CEDEX ; FRANCE

<footnote>*Joint first authors; **Joint last authors</footnote>
**Abstract**

**Background.** Recessive deficiency of proprotein convertase 1 (PC1/3; encoded by *PCSK1*) leads to early-onset obesity associated with severe endocrinopathy. Patients recessively deficient for *PCSK1* are eligible for MC4R agonist setmelanotide. Few heterozygous *PCSK1* mutations were associated with obesity risk, but their clinical impact and interest for precision medicine remain elusive. Here, we performed a large-scale functional genomic study focusing on rare, heterozygous variants of *PCSK1* to decipher the putative impact of these variants on obesity risk.

**Methods.** *PCSK1* was analysed in a case-control study for obesity, including 9,320 participants from three community- and one hospital-based cohort studies (France), who were recruited between Jan 1, 1995, and Dec 31, 2000. Functional assessment of rare missense variants was performed. Pathogenicity clusters of variants were determined through machine learning. Effect of each cluster on obesity was assessed through adjusted mixed-effects score test.

**Findings.** We identified 65 rare, heterozygous *PCSK1* variants including four null variants and 61 missense variants that were analysed *in vitro*. Missense variants were clustered into five groups (A-E) based on their enzymatic activity. Group_A variants had complete loss of function. Taking as reference this group, REVEL led to false positive (25%) and false negative (7%) results. Along with null variants, Group_A variants increased obesity risk (odds ratio of 9.3 [95% CI 1.5–177]) and body mass index (effect of 6.9 [SE 1.9]). In contrast, groups of variants with none or partial loss of function did not contribute to adiposity.

**Interpretation.** Our data suggest that only the carriers of heterozygous, null or complete loss-of-function *PCSK1* variants develop dominant obesity and therefore may be eligible for setmelanotide. *In silico* prediction did not enable an accurate sensibility and specificity for the detection of these variants. Therefore, *in vitro* tests are required to determine the variant pathogenicity for genetic diagnosis and precision medicine purposes.
**Funding.** Agence Nationale de la Recherche, European Research Council, European Union (FEDER), Hauts-de-France Regional Council, and European Metropolis of Lille.

**Research in context**

**Evidence before this study**

We searched, PubMed, from Jan 1, 1990, to Aug 15, 2022, for “PCSK1”, “heterozygous”, “mutation”, “obesity” and “human”. This search revealed 10 studies. Of these, three reported biallelic PCSK1 mutations (i.e. compound heterozygous) in patients with obesity; one was focused on a mouse model (knockout for *Pax6*); three sequenced PCSK1 (among other genes linked with monogenic obesity) in patients with obesity (n=1014, n=463, n=485) without highlighting associations between pathogenic, heterozygous PCSK1 mutations (with no evidence of functional effect) and obesity risk or bariatric surgery outcomes; one found a nonsense, heterozygous PCSK1 mutation co-segregating with obesity in a three-generation family; one found a significant association between the burden of rare, null, heterozygous mutations in PCSK1, MC4R and LEPR and increased body mass index; and one sequenced 845 people with obesity and found seven rare, heterozygous PCSK1 variants that had a deleterious effect on enzyme activity according to *in vitro* analyses and that contributed to obesity in the general population.

**Added value of this study**

The present study was motivated by the fact that a Phase 3 clinical trial is evaluating the MC4R agonist setmelanotide in patients with obesity who carry rare, heterozygous *PCSK1* variants. This is based on the assumption that rare, heterozygous mutations in *PCSK1* cause a dominant form of obesity. However, their true contribution to obesity remains elusive. The added value of the present study is the demonstration that *in vitro* functional analyses are necessary to assess the pathogenic effect of *PCSK1* mutations on obesity.
**Implications of all the available evidence**

Only the patients with obesity who carry complete loss-of-function, heterozygous *PCSK1* variants are likely to be considered for future treatment by setmelanotide. This study advocates for performing systematic *in vitro* studies before considering precision medicine.

**Introduction**

Overweight, obesity and their health comorbidities constitute a major growing medical burden. According to the World Health Organization, they are recognized as the 5th leading cause of death in the world. Obesity is a complex multifactorial disease with a strong genetic basis¹. Although common obesity is defined as a polygenic disease, monogenic obesity is caused by the presence of a single rare mutation with strong effect on increased weight. Monogenic obesity affects less than 5% of the population with obesity¹. Genes linked with non-syndromic monogenic obesity are mostly involved in the leptin-melanocortin pathway, which is critical for controlling appetite in the central nervous system. These genes include *LEP* (encoding leptin), *LEPR* (encoding leptin receptor), *POMC* (encoding pro-opiomelanocortin), *PCSK1* (encoding prohormone convertase subtilisin/kexin type 1 [PC1/3]), and *MC4R* (encoding melanocortin receptor type 4) whose deficiency is the most prevalent in Europe¹. Importantly, the identification and investigation of these genes have enabled the development of a new drug, the MC4R agonist setmelanotide, for patients with monogenic obesity. This drug that is administered by subcutaneous injections was shown to efficiently lead to weight loss in patients with obesity who are deficient for *POMC*, *LEPR* or *PCSK1*, in whom traditional interventions have usually poor impact on weight²⁻⁴. The U.S. Food and Drug Administration and the European Medicines Agency have recently approved the use of setmelanotide in these deficiencies.
Very rare biallelic pathogenic mutations in *PCSK1* cause early-onset obesity associated with various endocrinopathies⁵. PC1/3 is a major endoprotease involved in the processing of various prohormones and neuropeptides that regulate appetite and energy homeostasis. In the leptin-melanocortin pathway, PC1/3 cleaves POMC into adrenocorticotropic hormone (ACTH) that is the precursor of α-melanocyte-stimulating hormone (α-MSH), *i.e.* the agonist of MC4R. PC1/3 is highly expressed in neural and endocrine tissues and it includes a prodomain, a catalytic domain, a P domain and a carboxy-terminal domain. PC1/3 is synthesized as an inactive precursor (preproPC1/3) that is converted into a proPC1/3 zymogen (94 kDa) by auto cleavage in the endoplasmic reticulum (ER). The prodomain is then cleaved in the ER and in the Golgi where PC1/3 is converted into a partially active form (87 kDa). Autocatalytic cleavages lead to the mature activation of PC1/3 (74/66 kDa) in the acidic environment of the secretory granules⁶. In addition to the role of *PCSK1* in recessively inherited forms of obesity, linkage analyses and genome-wide association studies showed that frequent single nucleotide polymorphisms in *PCSK1* were associated with an increased risk of common obesity⁷,⁸. In contrast, the putative impact of rare, heterozygous mutations of *PCSK1* on obesity has not been clearly defined in large-scale studies, so far. Two previous studies identified rare, heterozygous, null variants (p.R80* and c.1095+1G>A) in *PCSK1* with a deleterious functional effect on PC1/3, which apparently caused monogenic obesity⁹,¹⁰. In addition, based on the sequencing of 845 participants with morbid obesity, seven rare heterozygous *PCSK1* variants had a deleterious effect on PC1/3 activity according to *in vitro* analyses and contributed to obesity in the general population¹¹. However, several studies that investigated the functional effect of *PCSK1* variants (including reference¹¹) were possibly biased as the authors used a *PCSK1* plasmid which included a non-synonymous mutation (encoding p.S357G) that lacked normal subcellular regulation and produced anomalous substrate processing when compared to wild-type (WT) *PCSK1*¹².
The question of the impact of rare, heterozygous *PCSK1* mutation on obesity is crucial for genetic diagnosis and treatment purposes, as a company is advancing a Phase 3 clinical trial (ClinicalTrials.gov Identifier: NCT05093634) evaluating setmelanotide in patients with obesity who carry rare, heterozygous *PCSK1* variants, without functional *in vitro* assessment of identified variants.

Here, we aimed to perform a large-scale functional genomic study focusing on rare, heterozygous variants of *PCSK1* to decipher the putative impact of these variants on obesity risk in humans.

## Methods

**Study participants.** The present analysis is primarily a case-control study for obesity, including 9,320 participants from the RaDiO (Rare variants involved in Diabetes and Obesity) study that included three community- and one hospital-based cohort studies (France), who were recruited between Jan 1, 1995, and Dec 31, 2000 (Appendix, p5)13,14. The study protocols were approved by local ethics committees. All participants older than 18 years signed an informed consent form. Oral assent from children/adolescents was obtained and parents (or legal guardians) signed and informed consent form. Study enrollment was voluntary. Therefore, biases of this study are similar to those of any biobank with voluntary enrollment.

In participants older than 18 years, normal weight was defined as BMI<25 kg/m², overweight as 25≤BMI<30 kg/m² and obesity as BMI≥30 kg/m². In children and adolescents younger than 18 years, normal weight was defined as BMI-for-age<85th percentile and obesity as BMI-for-age≥95th percentile. Sex was determined based on self-reporting. Participants with type 2 diabetes (T2D) had fasting glucose≥7·0 mmol/L and/or used treatment of hyperglycaemia, and were negative for islet or insulin autoantibodies. Control individuals had fasting glucose<5·6 mmol/L and did not use treatment for hyperglycaemia. Importantly, in the case-
control study for T2D, only controls known to have fasting glucose<5·6 mmol/L after age 40
were included.

No sample size calculation was performed before analysis. We included all study participants
recruited and accurately sequenced in the RaDiO study.

**Procedures.** *PCSK1* (NM_000439.5) was sequenced by next generation sequencing, as previously described<sup>13,14</sup>. All rare coding *PCSK1* variants (with a minor allele frequency <1%) were covered with >40 reads and had a QUAL score>150. Furthermore, no variants had >5% missing genotype across the participants and no participants had >5% of missing genotypes across *PCSK1*. 61 rare nonsense or missense variants of *PCSK1* were created by mutagenesis and inserted into plasmids from the WT *PCSK1* plasmid (Origene) using the Quik Change II XL Site Directed Mutagenesis Kit (Agilent). Sequence of each plasmid was checked by Sanger sequencing. Two plasmids including the *PCSK1* variant encoding p.K293R or p.T366M were purchased from Twist Bioscience.

All functional analyses were done in human embryonic kidney 239 (HEK293) cells. The culture of these cells is described in the **Appendix (p2)**. For the enzymatic assays, HEK293 cells were transfected using FuGENE HD (Promega) with 150 ng/mL of plasmid encoding the β-galactosidase gene and 1,000 ng/mL of *PCSK1* plasmid (WT or with variant). 48 hours after transfection, the β-galactosidase activity in cells was measured as previously described<sup>13</sup>, and the enzymatic activity of secreted PC1/3 in supernatants was measured by adding fluorescent substrate of PC1/3. The detailed protocol for enzymatic assays is described in the **Appendix (p2)**. The experiments were performed in technical triplicate and each experiment was repeated four times. We used three different controls in each experiment: 1/ one positive control that was a plasmid including WT *PCSK1*; 2/ one negative control that was a plasmid including p.R80* PC1/3 mutant; 3/ one negative control where WT or mutated *PCSK1* was not transfected ('non-transfected' condition). Statistical analyses for enzymatic assays were
performed using R version 4.1.3. Measures were normalized by dividing PC1/3 activity
measures by background measures. Then each set of experiments was normalized using non-
transfected and WT PC1/3 activity measures. The non-transfected factor was computed as the
geometric mean of the non-transfected PC1/3 activity measures. The WT factor was computed
as the geometric mean of the WT PC1/3 activity measures at 300 minutes minus the non-
transfected condition. Finally, the normalized PC1/3 activity measures were computed as
PC1/3 activity measures minus the non-transfected factor divided by the WT factor. The
minimal value of the normalized PC1/3 activity measures was set to zero, if needed, by
adding the absolute minimum to all normalized measures. The normalized PC1/3 activity
trajectories were clustered using the R package kml with 10 resampling iterations and a
number of cluster from two to eight. The optimal number of clusters was found to be five
(Groups_A, B, C, D and E) based on five criteria, namely: AIC, BIC, Calinkski-
Harabatz, Ray-Turi, and Davues-Bouldin. The protein expression of PCSK1 variants in cell
lysates of HEK293 cells was assessed by Western blotting. The detailed protocol for Western
blotting assays is described in the **Appendix (p3)**. Three independent experiments for each
mutation were performed. Protein expression of two PCSK1 variants in HEK293 cell
supernatants was assessed by liquid chromatography-mass spectrometry (LC-MS). The
detailed protocol for LC-MS assays is described in the **Appendix (p4)**. Protein identification
was performed by comparing all MS and MS/MS data with the *Homo Sapiens* proteome
database (Uniprot, June 2021 version), using MaxQuant software (version 1.6.10.43). For the
protein and peptide identification parameters, we used a false discovery rate of 1%, and a
minimum of two peptides per protein including one unique. The statistical analysis was done
using Perseus software (version 1.6.10.43). They were three replicates per group. The results
were normalized by Z-score.
**Statistical analysis for genetic association studies.** The rare variants were divided into four clusters (I=Group_A+Null variants; II=Group_B; III=Group_C; and IV=Group_D + Group_E) based on enzymatic assays. Each cluster was analyzed using the mixed-effects score test (MiST) method¹⁷. MiST provides a score statistic S(π) for the mean effect (π) of the cluster, and a score statistic S(τ) for the heterogeneous effect (τ) of the cluster. Let the equation of the model be : Y = αX + πGZ, where Y is the trait of interest, X is the matrix of covariates (i.e. age, sex, BMI, ancestry for assessing T2D and cholesterol levels, and age, sex, ancestry for assessing adiposity), G is the matrix of PCSK1 variants and Z is a vector of ones repeated n times, with n the number of rare PCSK1 variants, leading to: πGZ = π Σn i=1 Gᵢ. The ancestry of participants was assessed using the first five genotypic principal components (from PC1 to PC5), as previously described¹⁴. As none of the association studies had significant heterogeneity, we only showed the P-values associated with the score S(π) representing the mean effect of the cluster. These statistical analyses were performed using the R software (version 4.0.2).

**Role of funding source**

Funding sources were not involved in study design, data collection, data analysis, data interpretation, writing of the manuscript or decision to submit the manuscript for publication.
## Results

All 13 coding exons of *PCSK1* (NM_000439.5) were accurately sequenced in 9,320 participants including 7,260 adults and 2,060 children from the RaDiO study (Appendix, p5). Among adults, 21% had obesity, 39% had overweight and 40% had normal weight; among children, 50% had obesity and 50% had normal weight (Appendix, p5). We detected 65 rare, heterozygous, coding variants in *PCSK1* including four null variants (i.e. nonsense in which a point mutation results in a premature stop codon, frameshift or canonical ±1 or 2 splice sites; p.R80*, p.S340Pfs*94, p.Q363* and c.1095+1G>A) and 61 missense variants (i.e. point mutations in which a single nucleotide change results in a codon that encodes a different amino acid; Table 1).

Each nonsense or missense *PCSK1* variant, or WT *PCSK1* was expressed in HEK293 cells and the enzymatic activity was measured using a fluorescent substrate of PC1/3. When compared to WT condition, most variants significantly decreased the enzymatic activity of PC1/3 to varying degrees (Appendix, pp7-11). We performed a cluster analysis via machine learning that determined five clusters of variants according to their pathogenicity on PC1/3 activity (i.e. Group_A to Group_E) (Figure 1). When compared to WT condition, 17 variants led to complete PC1/3 loss of function (Group_A), 11 variants led to partial enzymatic activity (Group_B), 16 variants showed a slightly different activity (Group_C) and 19 variants did not affect the PC1/3 enzymatic activity (Group_D and Group_E) (Figure 1). Group_A *PCSK1* variants were located in the prodomain, catalytic domain and P domain of PC1/3 (Figure 2). Western blots in cell lysates showed that most Group_A *PCSK1* variants had no or poor expression of the PC1/3 active form whereas the precursor forms of PC1/3 were well expressed (Figure 3, and Appendix pp12-13). In addition, two Group_A variants encoding p.Q363* and p.G390S did not show both precursor and active forms of PC1/3 but a single band at about 50 kDa (Figure 3, and Appendix pp12-13). LC-MS analyses confirmed that
PC1/3 protein was not present in supernatants of HEK293 cells transfected with p.Q363* or p.G390S mutant, suggesting an incorrect cleavage of PC1/3 precursor forms (Appendix p14).

We analyzed the effect of rare PCSK1 variants on metabolic homeostasis. Using the MiST method adjusted for sex, age and ancestry, the burden of all rare variants was significantly associated with a higher risk of overweight or obesity (*P*=0·018 with an odds ratio [OR] of 1·7, 95% confidence interval [CI]: 1·1-2·8; **Table 2**) and with a higher BMI (28±5·6 kg/m² in carriers *versus* 27±6·5 kg/m² in non-carriers; *P*=0·039 with an effect [*π*] of 1·6±0·79; **Table 2**). However, the burden of all rare variants was not significantly associated with T2D risk or cholesterol levels (**Table 2**).

We then performed the same analyses according to the pathogenicity of variants based on *in vitro* assays (**Figure 1**). The burden of complete loss-of-function or null PCSK1 variants (i.e. Cluster_I merging Group_A variants and the two rare null variants c.1018del and c.1095+1G>A) had a significant effect on a higher BMI (32±9·3 kg/m² in carriers *versus* 27±6·5 kg/m² in non-carriers; *P*=0·00030 with *π* of 6·9±1·9; **Table 2**). In the same line, this burden of complete loss-of-function or null PCSK1 variants was significantly higher among individuals with obesity only (six out of seven [86%] individuals with obesity among carriers *versus* 35% of individuals with obesity among non-carriers; *P*=0·014 with OR of 9·3, 95% CI: 1·5-177; **Table 2**) or among individuals with overweight or obesity (16 out of 17 [94%] among carriers *versus* 60% among non-carriers; *P*=0·013 with OR of 8·5, 95% CI: 1·7-154; **Table 2**). We found the same trend of association between obesity and the burden of complete loss-of-function or null PCSK1 variants in children although the statistical power was more limited unfortunately (five out of six [83%] children with obesity among carriers *versus* 50% of children with obesity among non-carriers; *P*=0·18 with OR of 4·7, 95% CI: 0·57-103; **Appendix p6**). In contrast, the different burdens of PCSK1 variants with partial or neutral effect on PC1/3 activity did not have any significant effect on the risk of obesity or
overweight, and on BMI or other metabolic traits (Table 2). Our present results were in line
with a recent paper that showed that heterozygous protein-truncating variants in PCSK1 were
strongly associated with increased risk of obesity, based on 640,000 exome data¹⁸. In this
study, the genetic effect seen on adiposity was higher in the carriers of null, heterozygous
PCSK1 variants when compared to the carriers of null, heterozygous MC4R variants¹⁸.

We performed prediction analysis in order to discriminate complete loss-of-function variants
(Group_A) from other variants using REVEL that is an ensemble method for predicting the
pathogenicity of rare missense variants (Table 1)¹⁹. A total of four complete loss-of-function
mutations (encoding p.F70C, p.R81S, p.R95H, p.I487T) were not predicted as pathogenic by
REVEL. In contrast, 15 variants (encoding p.T157M, p.Y181H, p.Y187H, p.T203I, p.A217V,
p.R282W, p.Y290C, p. R312C, p.S325N, p.I329V, p.I329M, p.S340Y, p.S354C, p.T377M,
p.A398G) that were part of Group_B, Group_C, Group_D or even Group_E were detected as
deleterious by REVEL (Table 1). The distribution of the carriers of a missense PCSK1 variant
according to their sex, their obesity status, the enzymatic activity of missense variants (Group
A versus Group B), and the REVEL prediction of missense variants (neutral versus
deleterious) shows an enrichment of carriers with obesity in Group A, regardless of REVEL
prediction (Appendix, p15). These results highlighted the requirement of in vitro analyzes to
pinpoint complete loss-of-function missense variants in PCSK1.
Discussion

Our results unambiguously showed that only the heterozygous, null or functionally proven
complete loss-of-function PCSK1 variants have a dominant effect on obesity risk, whereas
missense variants with neutral or partial deleterious effect on PC1/3 activity do not contribute
to obesity risk or to increased BMI in a large population. In our study, this dominant,
complete deficiency of PC1/3 affects 0.4% of participants with obesity.

We also found that the PC1/3 protein expression of complete loss-of-function *PCSK1* variants was impaired in most cases. Indeed, among the 17 complete loss-of-function variants, three mutations (encoding p.R80*, p.V304I, p.P400S) did not show any form of PC1/3, three mutations (encoding p.F70C, p.T175M, p.L449P) had only the zymogen form (94 kDa), six mutations (encoding p.L132S, p.D193Y, p.D324Y, p.I487T, p.H557T, p.G582A) had both precursor forms (94 kDa and 87 kDa) but poorly the active form of PC1/3 (74/66 kDa), and two mutations (encoding p.Q363* and p.G390S) showed an incorrectly cleaved protein (Figure 3). Only three Group_A mutations had all forms of PC1/3 expressed in the same way as the WT condition (p.R81S, p.R95H, p.T375A), suggesting that PC1/3 encoded by Group_A mutations was normally secreted but that its enzymatic activity was impaired. These results are in line with a classification of *PCSK1* mutations which was previously proposed²⁰: 1/ PC1/3 proteins that are retained within the ER, 2/ PC1/3 proteins that are able of trafficking to the secretory granules but that are altered in the secretory pathway, and 3/ PC1/3 proteins that are correctly folded and secreted but that have impaired enzymatic activity. Furthermore, the authors proposed that there was a gradation of the severity of obesity from the third category (carriers with a more severe obesity) to the first category (carriers with a less severe obesity)²⁰. We did not replicate these findings; indeed, we did not observe any increase in the severity of phenotypes according to the classification of the mutant protein in these three mechanistic categories. Some of the carriers of the mutations that did not show any form of
PC1/3 presented with overweight or even normal weight (Figure 3). More generally, we observed that the carriers of a heterozygous, null or complete loss-of-function variant did not have extreme obesity despite the dominant effect of these mutations. Although a previous study suggested that some carriers of a heterozygous mutation with predicted loss of function in LEP, LEPR, POMC and PCSK1 could present with dramatic phenotypes similar to the carriers of a biallelic mutation²¹, the present results are in line with other studies where the obesity of the carriers of a heterozygous PCSK1 variant was less severe and less penetrant than in the carriers of a biallelic PCSK1 variant⁹,²²,²³.

Innovative medicine should be given to right patients to maximize their efficiency and to minimize side effects. That is the principle of precision medicine. So far, setmelanotide has been given to patients with obesity who carry biallelic pathogenic mutations in LEP, LEPR, POMC and PCSK1 that are not very numerous. As a company is currently trialing setmelanotide for patients with heterozygous PCSK1 variants, it is crucial to know whether these mutations are truly functional, especially because they represent a much larger population than the carriers of homozygous variants²¹. Unfortunately, the disability of *in silico* tests to accurately detect complete loss-of-function PCSK1 variants suggested that *in vitro* assays are necessary to determine the variant pathogenicity before proposing a treatment targeting the leptin-melanocortin pathway. Of note, the current trial is also assessing the effect of setmelanotide on obesity in patients with heterozygous variants in POMC or LEPR, while their true contribution to dominant obesity is unclear. Therefore, future research directions could be focused on deciphering the functional effect of heterozygous variants in POMC or LEPR and their contribution to obesity.

Our study was limited by the fact that it was not possible to have access to levels of insulin and ACTH (which are both cleaved by PC1/3) in the carriers of null or complete loss-of-function PCSK1 variants. However, we did not find any association of these variants with
T2D or lipids, which suggests a minimal impact on these metabolic pathways. Furthermore, the *in vitro* functional studies were done in HEK293 cells, where PC1/3 is not expressed in physiological conditions. These cells strongly differ from neuronal cells which embody the main actors of the obesity phenotype. However, this limitation was overcome by the use of a large genetic association case-control study following our standard *in vitro* assays. Importantly, our study does not imply that the patients with severe, early-onset obesity carrying a neutral, heterozygous *PCSK1* mutation should not be further investigated as another mutation might have a deleterious effect on the leptin-melanocortin pathway, and therefore these patients could be eligible for setmelanotide treatment.
## Figure Legends

### Figure 1. Cluster analysis according to enzymatic activity of 63 rare missense or nonsense variants of PCSK1

The figure shows the PC1/3 activity trajectories clustering of PCSK1 variants by kml (k = 5). The Y-axis represents PC1/3 catalytic activity normalised by β-galactosidase activity. Enzymatic activity was measured for 300 minutes. Five groups according to enzymatic activity of variants were determined by machine learning. Pink variants are included in Group_A; orange variants are included in Group_B; yellow variants are included in Group_C; green variants are included in Group_D and blue variants are included in Group_E. Non-transfected and WT conditions are highlighted in black. Data are the mean of four independent experiments performed in technical triplicate. *NT*, non-transfected; *WT*, wild-type.

### Figure 2. Localization of rare coding missense or nonsense variants of PCSK1

The different domains of PC1/3 protein are represented. Pink variants are amino acids changes linked to complete loss of enzymatic activity (Group_A) according to our *in vitro* assays. Black variants are amino acids changes linked to variants with partial or neutral effect on PC1/3 activity (Group_B, Group_C, Group_D and Group_E) according to our *in vitro* assays. *S*, peptide signal; *C-term*, C-terminal domain

### Figure 3. Protein expression of complete loss-of-function PCSK1 variants (Group_A) on cell lysates by Western blotting

This table recapitulates the protein expression of Group_A PCSK1 variants by Western blotting. Sizes of precursor forms of PC1/3: 94 kDa and 87kDa; size of active form of PC1/3: 74/66 kDa. Three independent experiments were performed for each mutation (images of Western blotting available in **Appendix, pp12-13**). *N*<sub>nwt</sub>, number of normal-weight
participants carrying the mutation in the RaDiO study; *N*<sub>ob</sub>, number of participants with
obesity carrying the mutation in the RaDiO study; *Nowt*, number of overweight participants
carrying the mutation in the RaDiO study.

**Acknowledgments**

We are grateful to all individuals included in the different cohort studies. We thank Frédéric
Allegaert and Timothée Beke for their technical assistance. We thank "France Génomique"
consortium (ANR-10-INBS-009). This work was supported by grants from the French
National Research Agency (ANR-10-LABX-46 [European Genomics Institute for Diabetes]
and ANR-10-EQPX-07-01 [LIGAN-PM]), from the European Research Council (ERC
GEPIDIAB – 294785, to PF; ERC OpiO – 101043671, to AB), and from the National Center
for Precision Diabetic Medicine – PreciDIAB, which is jointly supported by the French
National Agency for Research (ANR-18-IBHU-0001), by the European Union (FEDER), by
the Hauts-de-France Regional Council and by the European Metropolis of Lille (MEL).

**Declaration of Interests**

Authors declare that they have no competing interests.

**Contributors statement**

LF, MBar, PF and ABon conceived and designed the research question; LF, MBar, VS, HL
and AL contributed to functional analyses of each variant; BT and EV contributed to next-
generation sequencing; MBoi, MC and ABon contributed to statistical analyses; ABad and
MD contributed to computer analyses; MS and SA contributed to proteomic analyses; BB,
GC, SF, MM and PF contributed to cohort data; LF and ABon wrote the first draft of the
manuscript; LF, MBar and ABon have directly accessed and verified the underlying data. All
authors provided input on the interpretation of results. All authors revised the manuscript for
important intellectual content and read and approved the final manuscript.

Data sharing

All relevant data have been included in the manuscript and supplementary tables. Given the
sensitivity and risk of re-identification, all clinical data linked with NGS data for this study is
available only upon request from the corresponding author.
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<table><caption>Table 1. Rare PCSK1 variants detected in the RaDiO study</caption><thead><tr><th>Mutation in PCSK1 (NM_000439.5)</th><th>MAC in gnomAD</th><th>MAC in RaDiO</th><th>References†</th><th>Functional group‡</th><th>REVEL (score)*</th></tr></thead><tbody><tr><td>c.34G&gt;C, p.A12P</td><td>0</td><td>1</td><td>-</td><td>Group D</td><td>Neutral (0.28)</td></tr><tr><td>c.173T&gt;C, p.L58S</td><td>0</td><td>1</td><td>-</td><td>Group B</td><td>Neutral (0.15)</td></tr><tr><td>c.209T&gt;G, p.F70C</td><td>0</td><td>1</td><td>-</td><td>Group A</td><td>Neutral (0.48)</td></tr><tr><td>c.238C&gt;T, p.R80*</td><td>2</td><td>1</td><td>9,24</td><td>Group A and Null</td><td>NA</td></tr><tr><td>c.239G&gt;A, p.R80Q</td><td>585</td><td>1</td><td>-</td><td>Group B</td><td>Neutral (0.21)</td></tr><tr><td>c.243G&gt;C, p.R81S</td><td>1</td><td>1</td><td>-</td><td>Group A</td><td>Neutral (0.24)</td></tr><tr><td>c.254A&gt;G, p.H85R</td><td>0</td><td>1</td><td>-</td><td>Group C</td><td>Neutral (0.10)</td></tr><tr><td>c.281A&gt;G, p.D94G</td><td>1</td><td>2</td><td>-</td><td>Group D</td><td>Neutral (0.063)</td></tr><tr><td>c.284G&gt;A, p.R95H</td><td>3</td><td>1</td><td>-</td><td>Group A</td><td>Neutral (0.23)</td></tr><tr><td>c.334G&gt;A, p.A112T</td><td>0</td><td>2</td><td>-</td><td>Group D</td><td>Neutral (0.015)</td></tr><tr><td>c.375G&gt;A, p.M125I</td><td>51</td><td>13</td><td>11</td><td>Group B</td><td>Neutral (0.33)</td></tr><tr><td>c.395T&gt;C, p.L132S</td><td>0</td><td>1</td><td>-</td><td>Group A</td><td>Deleterious (0.92)</td></tr><tr><td>c.470C&gt;T, p.T157M</td><td>5</td><td>1</td><td>-</td><td>Group B</td><td>Deleterious (0.83)</td></tr><tr><td>c.524C&gt;T, p.T175M</td><td>41</td><td>2</td><td>11</td><td>Group A</td><td>Deleterious (0.83)</td></tr><tr><td>c.541T&gt;C, p.Y181H</td><td>29</td><td>4</td><td>11</td><td>Group B</td><td>Deleterious (0.94)</td></tr><tr><td>c.559T&gt;C, p.Y187H</td><td>4</td><td>2</td><td>-</td><td>Group D</td><td>Deleterious (0.83)</td></tr><tr><td>c.577G&gt;T, p.D193Y</td><td>0</td><td>1</td><td>-</td><td>Group A</td><td>Deleterious (0.91)</td></tr><tr><td>c.608C&gt;T, p.T203I</td><td>0</td><td>1</td><td>-</td><td>Group C</td><td>Deleterious (0.64)</td></tr><tr><td>c.646A&gt;G, p.I216V</td><td>1</td><td>1</td><td>-</td><td>Group C</td><td>Neutral (0.36)</td></tr><tr><td>c.650C&gt;T, p.A217V</td><td>15</td><td>3</td><td>-</td><td>Group C</td><td>Deleterious (0.87)</td></tr><tr><td>c.844C&gt;T, p.R282W</td><td>7</td><td>1</td><td>-</td><td>Group D</td><td>Deleterious (0.65)</td></tr><tr><td>c.869A&gt;G, p.Y290C</td><td>16</td><td>1</td><td>-</td><td>Group C</td><td>Deleterious (0.75)</td></tr><tr><td>c.878A&gt;G, p.K293R</td><td>2</td><td>1</td><td>-</td><td>Group B</td><td>Neutral (0.23)</td></tr><tr><td>c.910G&gt;A, p.V304I</td><td>35</td><td>3</td><td>-</td><td>Group A</td><td>Deleterious (0.63)</td></tr><tr><td>c.934C&gt;T, p.R312C</td><td>1</td><td>1</td><td>-</td><td>Group B</td><td>Deleterious (0.76)</td></tr><tr><td>c.970G&gt;T, p.D324Y</td><td>0</td><td>1</td><td>-</td><td>Group A</td><td>Deleterious (0.72)</td></tr><tr><td>c.974G&gt;A, p.S325N</td><td>0</td><td>1</td><td>11</td><td>Group C</td><td>Deleterious (0.73)</td></tr><tr><td>c.985A&gt;G, p.I329V</td><td>1</td><td>1</td><td>-</td><td>Group C</td><td>Deleterious (0.55)</td></tr><tr><td>c.987C&gt;G, p.I329M</td><td>0</td><td>2</td><td>-</td><td>Group C</td><td>Deleterious (0.78)</td></tr><tr><td>c.1018del, p.S340Pfs*94</td><td>0</td><td>1</td><td>-</td><td>Null</td><td>NA</td></tr><tr><td>c.1019C&gt;A, p.S340Y</td><td>0</td><td>1</td><td>-</td><td>Group B</td><td>Deleterious (0.60)</td></tr><tr><td>c.1061C&gt;G, p.S354C</td><td>2</td><td>1</td><td>-</td><td>Group D</td><td>Deleterious (0.62)</td></tr><tr><td>c.1087C&gt;T, p.Q363*</td><td>2</td><td>1</td><td>-</td><td>Group A and Null</td><td>NA</td></tr><tr><td>c.1095+1G&gt;A, p.?</td><td>0</td><td>1</td><td>10,25</td><td>Null</td><td>NA</td></tr><tr><td>c.1097C&gt;T, p.T366M</td><td>9</td><td>1</td><td>-</td><td>Group B</td><td>Neutral (0.37)</td></tr><tr><td>c.1123A&gt;G, p.T375A</td><td>0</td><td>1</td><td>-</td><td>Group A</td><td>Deleterious (0.87)</td></tr><tr><td>c.1130C&gt;T, p.T377M</td><td>4</td><td>1</td><td>-</td><td>Group E</td><td>Deleterious (0.60)</td></tr><tr><td>c.1168G&gt;A, p.G390S</td><td>2</td><td>2</td><td>-</td><td>Group A</td><td>Deleterious (0.96)</td></tr><tr><td>c.1193C&gt;G, p.A398G</td><td>0</td><td>1</td><td>-</td><td>Group C</td><td>Deleterious (0.55)</td></tr><tr><td>c.1198C&gt;T, p.P400S</td><td>0</td><td>1</td><td>-</td><td>Group A</td><td>Deleterious (0.70)</td></tr><tr><td>c.1283A&gt;C, p.K428T</td><td>0</td><td>1</td><td>-</td><td>Group D</td><td>Neutral (0.33)</td></tr><tr><td>c.1346T&gt;C, p.L449P</td><td>0</td><td>1</td><td>-</td><td>Group A</td><td>Deleterious (0.96)</td></tr><tr><td>c.1381G&gt;A, p.V461M</td><td>7</td><td>1</td><td>-</td><td>Group D</td><td>Neutral (0.32)</td></tr><tr><td>c.1405G&gt;A, p.V469I</td><td>6</td><td>2</td><td>-</td><td>Group E</td><td>Neutral (0.069)</td></tr><tr><td>c.1406T&gt;C, p.V469A</td><td>1</td><td>1</td><td>-</td><td>Group C</td><td>Neutral (0.19)</td></tr><tr><td>c.1441G&gt;A, p.A481T</td><td>0</td><td>1</td><td>-</td><td>Group C</td><td>Neutral (0.054)</td></tr><tr><td>c.1460T&gt;C, p.I487T</td><td>6</td><td>1</td><td>-</td><td>Group A</td><td>Neutral (0.31)</td></tr></tbody></table>
<table><tr><td>c.1472C>T, p.T491I</td><td>0</td><td>1</td><td>-</td><td>Group D</td><td>Neutral (0·41)</td></tr><tr><td>c.1582G>A, p.A528T</td><td>0</td><td>2</td><td>-</td><td>Group B</td><td>Neutral (0·24)</td></tr><tr><td>c.1585G>A, p.A529T</td><td>0</td><td>1</td><td>-</td><td>Group C</td><td>Neutral (0·13)</td></tr><tr><td>c.1621C>T, p.R541W</td><td>11</td><td>1</td><td>-</td><td>Group C</td><td>Neutral (0·45)</td></tr><tr><td>c.1622G>A, p.R541Q</td><td>1</td><td>1</td><td>-</td><td>Group C</td><td>Neutral (0·35)</td></tr><tr><td>c.1669C>T, p.H557Y</td><td>0</td><td>1</td><td>-</td><td>Group A</td><td>Deleterious (0·84)</td></tr><tr><td>c.1745G>C, p.G582A</td><td>0</td><td>1</td><td>-</td><td>Group A</td><td>Deleterious (0·88)</td></tr><tr><td>c.1813C>T, p.R605C</td><td>0</td><td>1</td><td>-</td><td>Group B</td><td>Neutral (0·40)</td></tr><tr><td>c.1823C>T, p.T608M</td><td>7</td><td>1</td><td>-</td><td>Group D</td><td>Neutral (0·16)</td></tr><tr><td>c.1855G>A, p.G619R</td><td>0</td><td>3</td><td>-</td><td>Group D</td><td>Neutral (0·27)</td></tr><tr><td>c.1918A>G, p.T640A</td><td>368</td><td>32</td><td>-</td><td>Group D</td><td>Neutral (0·026)</td></tr><tr><td>c.1961G>A, p.R654Q</td><td>8</td><td>1</td><td>-</td><td>Group D</td><td>Neutral (0·069)</td></tr><tr><td>c.1978G>A, p.E660K</td><td>2</td><td>1</td><td>-</td><td>Group D</td><td>Neutral (0·046)</td></tr><tr><td>c.2076G>C, p.K692N</td><td>126</td><td>1</td><td>-</td><td>Group D</td><td>Neutral (0·16)</td></tr><tr><td>c.2129C>G, p.P710R</td><td>1</td><td>1</td><td>-</td><td>Group D</td><td>Neutral (0·17)</td></tr><tr><td>c.2180T>A, p.V727D</td><td>0</td><td>1</td><td>-</td><td>Group D</td><td>Neutral (0·12)</td></tr><tr><td>c.2219G>A, p.R740Q</td><td>7</td><td>1</td><td>-</td><td>Group C</td><td>Neutral (0·26)</td></tr><tr><td>c.2236G>A, p.V746M</td><td>10</td><td>3</td><td>-</td><td>Group C</td><td>Neutral (0·065)</td></tr></table>

†References where the *PCSK1* mutations were reported pathogenic according to HGMD Professional (version 2022.3);

‡According to our *in vitro* assays;

*Pathogenicity threshold for REVEL: 50%.

*gnomAD*, genome aggregation database browser (v2.1.1); *MAC*, minor allele count; *NA*, not available
<table><caption>Table 2. Protein expression of complete loss-of-function *PCSK1* variants (Group_A) on cell lysates by Western blotting</caption><thead><tr><th rowspan="2">Variants</th><th colspan="14">Complete loss-of-function PCSK1 variants</th></tr><tr><th>WT</th><th>F70C</th><th>R80*</th><th>R81S</th><th>R95H</th><th>L132S</th><th>T175M</th><th>D193Y</th><th>V304I</th><th>D324Y</th><th>Q363*</th><th>T375A</th><th>G390S</th><th>P400S</th><th>L449P</th><th>I487T</th><th>H557Y</th><th>G582A</th></tr></thead><tbody><tr><td>N<sub>ob</sub> / N<sub>owt</sub> / N<sub>nwt</sub></td><td>-</td><td>0/1/0</td><td>1/0/0</td><td>1/0/0</td><td>1/0/0</td><td>1/0/0</td><td>0/2/0</td><td>0/0/1</td><td>1/1/1</td><td>1/0/0</td><td>0/1/0</td><td>1/0/0</td><td>1/1/0</td><td>1/0/0</td><td>1/0/0</td><td>1/0/0</td><td>0/1/0</td><td>0/1/0</td></tr><tr><td>Precursor protein</td><td>Yes</td><td>Yes</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Active protein</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td></tr><tr><td>Examples</td><td colspan="20"></td></tr><tr><td>kD</td><td colspan="20"></td></tr><tr><td>100</td><td colspan="20"></td></tr><tr><td>70</td><td colspan="20"></td></tr><tr><td>55</td><td colspan="20"></td></tr></tbody></table>

This table recapitulates the protein expression of Group_A *PCSK1* variants by Western blotting. Sizes of precursor forms of PC1/3: 94 kDa and 87kDa; size of active form of PC1/3: 74/66 kDa. Three independent experiments were performed for each mutation (images of Western blotting available in **Appendix, pp9-10**). *N*<sub>ob</sub>, number of normal-weight participants carrying the mutation in the RaDiO study; *N*<sub>nwt</sub>, number of participants with obesity carrying the mutation in the RaDiO study; *Nowt*, number of overweight participants carrying the mutation in the RaDiO study.
<table><caption>Table 3. Effect of different clusters of rare *PCSK1* variants on obesity and other metabolic traits in adults, according to variant pathogenicity on PC1/3 activity</caption><thead><tr><th>Trait</th><th>n<sub>total</sub></th><th>Cluster</th><th>%, mean ±SD or median [IQR] of the trait in carriers</th><th>%, mean ±SD or median [IQR] of the trait in non-carriers</th><th>OR [CI]</th><th>π ±SE</th><th>P</th></tr></thead><tbody><tr><td rowspan="5">BMI (kg/m²)</td><td rowspan="5">7,260</td><td>I</td><td>32 ±9.3</td><td>27 ±6.5</td><td>-</td><td>6.9 ±1.9</td><td>0.00030</td></tr><tr><td>II</td><td>26 ±3.6</td><td>27 ±6.5</td><td>-</td><td>-0.99 ±1.7</td><td>0.56</td></tr><tr><td>III</td><td>27 ±4.5</td><td>27 ±6.5</td><td>-</td><td>0.87 ±2.3</td><td>0.70</td></tr><tr><td>IV</td><td>27 ±4.0</td><td>27 ±6.5</td><td>-</td><td>1.2 ±1.1</td><td>0.30</td></tr><tr><td>All</td><td>28 ±5.6</td><td>27 ±6.5</td><td>-</td><td>1.6 ±0.79</td><td>0.039</td></tr><tr><td rowspan="5">Obesity</td><td rowspan="5">4,402</td><td>I</td><td>86%</td><td>35%</td><td>9.3 [1.5–177]</td><td>-</td><td>0.014</td></tr><tr><td>II</td><td>36%</td><td>35%</td><td>1.0 [0.27–3.4]</td><td>-</td><td>0.97</td></tr><tr><td>III</td><td>43%</td><td>35%</td><td>1.4 [0.27–6.3]</td><td>-</td><td>0.68</td></tr><tr><td>IV</td><td>42%</td><td>35%</td><td>1.2 [0.51–2.7]</td><td>-</td><td>0.66</td></tr><tr><td>All</td><td>47%</td><td>35%</td><td>1.5 [0.85–2.7]</td><td>-</td><td>0.16</td></tr><tr><td rowspan="5">OWT or obesity</td><td rowspan="5">7,260</td><td>I</td><td>94%</td><td>60%</td><td>8.5 [1.7–154]</td><td>-</td><td>0.013</td></tr><tr><td>II</td><td>68%</td><td>60%</td><td>1.4 [0.57–3.6]</td><td>-</td><td>0.50</td></tr><tr><td>III</td><td>67%</td><td>60%</td><td>1.1 [0.34–4.3]</td><td>-</td><td>0.85</td></tr><tr><td>IV</td><td>71%</td><td>60%</td><td>1.6 [0.85–3.1]</td><td>-</td><td>0.16</td></tr><tr><td>All</td><td>73%</td><td>60%</td><td>1.7 [1.1–2.8]</td><td>-</td><td>0.018</td></tr><tr><td rowspan="5">T2D</td><td rowspan="5">5,496</td><td>I</td><td>65%</td><td>40%</td><td>1.2 [0.39–3.8]</td><td>-</td><td>0.79</td></tr><tr><td>II</td><td>47%</td><td>40%</td><td>1.7 [0.64–4.3]</td><td>-</td><td>0.29</td></tr><tr><td>III</td><td>56%</td><td>40%</td><td>1.3 [0.29–5.9]</td><td>-</td><td>0.71</td></tr><tr><td>IV</td><td>39%</td><td>40%</td><td>0.83 [0.39–1.7]</td><td>-</td><td>0.61</td></tr><tr><td>All</td><td>48%</td><td>40%</td><td>1.11 [0.68–1.8]</td><td>-</td><td>0.67</td></tr><tr><td rowspan="5">HDL (mmol/L)</td><td rowspan="5">6,864</td><td>I</td><td>1.4 ±0.56</td><td>1.5 ±0.44</td><td>-</td><td>0.074 ±0.12</td><td>0.54</td></tr><tr><td>II</td><td>1.5 ±0.34</td><td>1.5 ±0.44</td><td>-</td><td>-0.048 ±0.10</td><td>0.65</td></tr><tr><td>III</td><td>1.4 ±0.41</td><td>1.5 ±0.44</td><td>-</td><td>-0.024 ±0.14</td><td>0.87</td></tr><tr><td>IV</td><td>1.5 ±0.45</td><td>1.5 ±0.44</td><td>-</td><td>0.039 ±0.070</td><td>0.58</td></tr><tr><td>All</td><td>1.4 ±0.44</td><td>1.5 ±0.44</td><td>-</td><td>0.018 ±0.049</td><td>0.71</td></tr><tr><td rowspan="5">TG (mmol/L)</td><td rowspan="5">6,912</td><td>I</td><td>1.5 [1.1–1.7]</td><td>1.1 [0.77–1.6]</td><td>-</td><td>-0.015 ±0.13</td><td>0.91</td></tr><tr><td>II</td><td>1.1 [0.75–1.7]</td><td>1.1 [0.77–1.6]</td><td>-</td><td>0.097 ±0.11</td><td>0.39</td></tr><tr><td>III</td><td>1.0 [0.86–1.4]</td><td>1.1 [0.77–1.6]</td><td>-</td><td>-0.080 ±0.16</td><td>0.61</td></tr><tr><td>IV</td><td>1.1 [0.93–1.5]</td><td>1.1 [0.77–1.6]</td><td>-</td><td>-0.0047 ±0.076</td><td>0.95</td></tr><tr><td>All</td><td>1.2 [0.86–1.6]</td><td>1.1 [0.77–1.6]</td><td>-</td><td>0.0073 ±0.053</td><td>0.89</td></tr></tbody></table>

The associations between the different burdens of rare *PCSK1* variants and adiposity were analyzed using the MiST method adjusted for age, sex and ancestry. The associations between the different burden of rare *PCSK1* variants and T2D risk or cholesterol levels were analyzed using the MiST method adjusted for age, sex, BMI and ancestry. Cluster I includes Group_A and Null variants; Cluster II includes Group_B variants; Cluster III includes Group_C variants and Cluster IV includes Group_D and Group_E variants. TG data were log-transformed before analysis. *BMI*, body mass index; *CI*, confidence interval; *HDL*, high-density lipoprotein; *IQR*,
interquartile range; *π*, mean effect; *OR*, odds ratio; *OWT*, overweight; *SD*, standard deviation; *SE*, standard error; *T2D*, type 2 diabetes; *TG*, triglycerides.