Familial partial lipodystrophy (FPLD) is a rare heterogeneous disease (1). By using a DEXA-derived fat mass ratio (FMR) (percent trunk fat divided by percent leg fat) with a cutoff (>1.2 in females and >1.7 in males), Agrawal et al. (2) identified “lipodystrophy-like phenotypes” in approximately 1 in 8 participants in the UK Biobank cohort and 1 in 20 participants in the Fenland Study cohort. Additionally, they discovered a genetic signature that links FMR to an elevated risk of metabolic syndrome and cardiometabolic diseases. The prevalence of lipodystrophy-like phenotypes appears to be much higher than the clinical or genetic prevalence of FPLD. Defining the lipodystrophy-like phenotype may aid in differentiating individuals with altered adipose tissue distribution and may allow potential targeted therapies. At a minimum, it appears important to personalize disease management for these individuals by adopting an approach similar to that applied to lipodystrophy, where a personalized approach and healthy weight goals may be necessary (3). While FMR can assist in identifying patients with lipodystrophy-like phenotypes and potentially can contribute to evaluation of a pool of patients suspected to have rare lipodystrophy syndromes, it is important to note that the diagnosis of lipodystrophy syndromes necessitates a comprehensive clinical assessment where DEXA outputs can be supportive but not definitive on their own.

The initial estimates of lipodystrophy prevalence were based on literature reviews, which suggested a prevalence of less than one in a million (4,5). The first structured prevalence study, by Chiquette et al. (6), estimated the prevalence of lipodystrophy by examining electronic medical record databases and literature. This study proposed a prevalence range of 1.3–4.7 cases per million for all types of lipodystrophy across various electronic medical record databases. Notably, in the Quintiles search, the prevalence estimate for diagnosed partial lipodystrophy was 2.8 cases per million. A later study published in Diabetes suggested a clinical prevalence of lipodystrophy as high as ∼1 in 20,000 individuals and a genetic prevalence of FPLD as high as ∼1 in 7,000 (7). However, this study’s reliance on ICD diagnoses (without additional filtering or expert adjudication) and searches in cohorts with a higher prevalence of metabolic abnormalities likely led to the inclusion of cases of localized lipodystrophy because of insulin injections, resulting in an overestimation of clinical prevalence. Additionally, the genetic prevalence of lipodystrophy may be overestimated because of a founder effect in the Geisinger Health System. Out of 92,455 sequenced individuals, Gonzaga-Jauregui et al. (7) identified LMNA variants in 17 individuals, among whom 16 shared the same variant. This variant showed significant enrichment within the studied cohort compared with other population databases (43 times more prevalent) and LMNA variant distribution in registries, strongly suggesting a founder effect within the communities covered by the cohort. Furthermore, without phenotype information and phenotypic segregation studies, the lipodystrophy-causing effects of detected PPARG variants and other identified variants (e.g., PIK3R1) remain uncertain.

An alternative approach to estimating the prevalence of lipodystrophy involves searching genetic databases, with hot-spot FPLD variants serving as reliable starting points. The v4 release of the Genome Aggregation Database (gnomAD) (gnomad.broadinstitute.org), formerly known as the Exome Aggregation Consortium (ExAC), contains 730,947 exomes and 76,215 genomes. To determine the genetic prevalence of FPLD, we focused on two hot-spot LMNA variants, p.R482W and p.R482Q, which are known to be disease causing in most patients with FPLD. We extrapolated a cumulative allele frequency of 7.4 per million for LMNA R482W and R482Q in gnomAD to model the estimated genetic relevance of FPLD (Fig. 1A). Based on our recent study (8) that analyzed all published LMNA-related lipodystrophy (FPLD2) cases, we calculated the prevalence of FPLD2 as 12.4 per million. Further modeling based on two sources of international registry data (LD Lync Study, NCT03087253, and ECLip Registry, NCT03553420) yielded a calculated FPLD prevalence of 19.0–30.0 per million. However, this prevalence estimate assumes that all patients with FPLD are clinically diagnosed, which is not always the case. Many cases remain undiagnosed, particularly for individuals with less prominent phenotypes, such as men. It also is likely that clinicians are underdiagnosing FPLD1 (or the Köbberling phenotype) due to lack of well-defined diagnostic criteria, and diagnosis rates can vary significantly based on multiple factors, including the level of awareness and the availability of centers of excellence in different regions. With more awareness of the clinical presentation of lipodystrophy, it is expected that the accurate diagnosis of lipodystrophy will improve in the future.

Figure 1

Prevalence of FPLD and the spectrum of disorders of altered adipose tissue distribution. A: Prevalence estimate for FPLD, derived from allele frequency data for hot-spot LMNA variants obtained from gnomAD. This estimate is further extrapolated to a broader population based on data from a recent analysis of all published lipodystrophy-associated LMNA variants and the LD-Lync and ECLip registries. B: Spectrum ranging from general obesity to relatively common lipodystrophy-like phenotypes and rare lipodystrophy syndromes in a female subject. The graphic is representative of typical FPLD2 fat distribution for monogenic FPLD.

Figure 1

Prevalence of FPLD and the spectrum of disorders of altered adipose tissue distribution. A: Prevalence estimate for FPLD, derived from allele frequency data for hot-spot LMNA variants obtained from gnomAD. This estimate is further extrapolated to a broader population based on data from a recent analysis of all published lipodystrophy-associated LMNA variants and the LD-Lync and ECLip registries. B: Spectrum ranging from general obesity to relatively common lipodystrophy-like phenotypes and rare lipodystrophy syndromes in a female subject. The graphic is representative of typical FPLD2 fat distribution for monogenic FPLD.

Close modal

DEXA scanning is a feasible and cost-effective method for assessing fat quantity and distribution (9,10). DEXA-derived FMR offers insights into the disproportion between abdominal and lower limb fat, serving as a valuable tool in the diagnosis of FPLD and for identifying individuals with lipodystrophy-like phenotypes (2,10). Similarly, measuring anterior thigh skinfold thickness can assist in diagnosing lipodystrophy, with values below 22 mm for women and 10 mm for men considered supportive (11). These skinfold measurements correspond to the 10th percentile of the normal population, akin to FMR cutoffs of 1.2 for women and 1.7 for men, which represent the 87th percentile of the FMR distribution in UK Biobank (2,11). While these cutoffs can aid in screening, they are not definitive for diagnosing FPLD. In our previous work, we failed to identify a single cutoff that would avoid substantial overlap with the normal population, particularly in males and in individuals who are lean (10). We then focused on the fat shadow (12), a color-coded representation that highlights fat tissue throughout the body and offers a focused impression of fat distribution across various anatomical regions and proposed additional features (e.g., loss of fat predominantly from the lower extremities, abdominal obesity, increased neck and supraclavicular fat, increased fat signal around the mons pubis, and lower extremity muscularity), as important in defining FPLD (13).

The main clinical feature of FPLD is the loss of fat from the lower extremities. Genetic panels can identify pathogenic variants in FPLD genes in monogenic forms of the disease. However, negative results are possible in some FPLD patients, suggesting the involvement of additional unidentified genes (1). FPLD1 adds further complexity. Most individuals with this form of lipodystrophy likely have a polygenic etiology. They have fat deficiency in the limbs, although fat accumulation around the abdomen may mimic the clinical presentation of common metabolic conditions such as type 2 diabetes, obesity, and metabolic syndrome (13,14).

A prior study comparing women diagnosed with FPLD1 to those from the Fenland Study found that women with FPLD1 show notably lower levels of leg fat mass compared with the rest of their body fat (15). Given the absence of a straightforward test to distinguish FPLD1 from lipodystrophy-like phenotypes, a thorough clinical assessment is crucial. This includes reviewing medical and family history; performing a comprehensive physical examination, anthropometric measurements, and metabolic testing; and evaluating body fat distribution. The characteristic features of FPLD1 involve extremities lacking subcutaneous fat, resulting in a noticeable ledge where the subcutaneous fat ends, but this can also be seen in individuals who lose massive amounts of fat and may not be pathognomonic. Skinfold measurements such as the subscapular/calf skinfolds (Köbberling index) can be helpful (16). Fat distribution can be further assessed using DEXA-derived fat shadows and MRI (12,17). However, clear diagnostic criteria are lacking due to the limited number of subjects with the FPLD1 phenotype in previous studies. Further efforts are needed to develop a set of criteria for the diagnosis of FPLD1 through a high-level expert contribution. Ongoing careful morphometric analyses of patients as well as microscopic and molecular characterization of tissue obtained from patients will likely be quite informative in pointing to not only similarities but also important differences between FPLD1 and monogenic FPLD syndromes.

In summary, FPLD and lipodystrophy-like phenotypes likely share some similarities in underlying pathophysiology and are both associated with insulin resistance, metabolic dysfunction–associated fatty liver disease/metabolic dysfunction–associated steatohepatitis, and cardiovascular disorders (2,18). Both lipodystrophy and lipodystrophy-like phenotypes are on a continuous spectrum of loss in adipocyte function, quantity, and quality (Fig. 1B). These similarities between the two conditions contribute to diagnostic challenges between rare and extreme cases versus more common conditions. Identifying individuals with a lipodystrophy-like phenotype within populations and among patients with diabetes could be significant, as it would enable tailored risk management and improved medical care. Nonetheless, it is crucial to carefully differentiate rare lipodystrophy syndromes as unique diseases, as correct identification also enables specific lipodystrophy-focused interventions.

Some or all data sets generated and/or analyzed during the course of this work are not publicly available but are available from the corresponding author on reasonable request.

See accompanying article, p. 1099.

Acknowledgments. The authors extend their appreciation to LD-Lync Study and ECLip Registry for allowing the authors to use current registry numbers for prevalence modeling.

Duality of Interest. B.A. received a consultancy fee from Amryt Pharmaceuticals, now Chiesi Farmaceutici, and has run projects for and/or served as a consultant, board member, steering committee member, and/or speaker to Amryt Pharmaceuticals, now Chiesi Farmaceutici, Alnylam, Regeneron, ThirdRock Ventures, AstraZeneca, Novo Nordisk, Boehringer Ingelheim, Sanofi, Bilim Ilac, ARIS, and Servier. D.A.-V. received a consultancy fee from Amryt Pharmaceuticals, now Chiesi Farmaceutici, and Regeneron. M.W. served as a consultant, board member, and/or speaker to Amryt Pharmaceuticals, now Chiesi Farmaceutici, Regeneron, ThirdRock Ventures, Novo Nordisk, and Boehringer Ingelheim. E.A.O. received grant support from Aegerion Pharmaceuticals (Amryt Pharmaceuticals, now Chiesi Farmaceutici), Ionis Pharmaceuticals, Akcea Therapeutics, Gemphire Therapeutics, GI Dynamics (current), and AstraZeneca (past 2 years). E.A.O. was a consultant or advisor to AstraZeneca, Thera Therapeutics, and BMS in the past. E.A.O. currently is a consultant or advisor to Aegerion Pharmaceuticals (Amryt Pharmaceuticals, now Chiesi Farmaceutici) and Regeneron Pharmaceuticals. E.A.O. receives drug support from Aegerion Pharmaceuticals (Amryt Pharmaceuticals, now Chiesi Farmaceutici), Akcea Therapeutics, and Rhythm Pharmaceuticals. E.A.O. receives other support from Aegerion Pharmaceuticals (Amryt Pharmaceuticals, now Chiesi Farmaceutici) and Regeneron Pharmaceuticals. No other potential conflicts of interest relevant to this article were reported.

1.
Akinci
B
,
Sahinoz
M
,
Oral
E
. Lipodystrophy syndromes: presentation and treatment. In
Endotext.
Feingold
KR
,
Anawalt
B
,
Blackman
MR
, et al
., Eds.
South Dartmouth, MA, MDText
,
2000
2.
Agrawal
S
,
Luan
J
,
Cummings
BB
,
Weiss
EJ
,
Wareham
NJ
,
Khera
AV
.
Relationship of fat mass ratio, a biomarker for lipodystrophy, with cardiometabolic traits
.
Diabetes
2024
;
73
:
1099
1111
3.
Koo
E
,
Foss-Freitas
MC
,
Meral
R
, et al
.
The metabolic equivalent BMI in patients with familial partial lipodystrophy (FPLD) compared with those with severe obesity
.
Obesity (Silver Spring)
2021
;
29
:
274
278
4.
Garg
A
.
Clinical review#: lipodystrophies: genetic and acquired body fat disorders
.
J Clin Endocrinol Metab
2011
;
96
:
3313
3325
5.
Patni
N
,
Garg
A
.
Congenital generalized lipodystrophies–new insights into metabolic dysfunction
.
Nat Rev Endocrinol
2015
;
11
:
522
534
6.
Chiquette
E
,
Oral
EA
,
Garg
A
,
Araújo-Vilar
D
,
Dhankhar
P
.
Estimating the prevalence of generalized and partial lipodystrophy: findings and challenges
.
Diabetes Metab Syndr Obes
2017
;
10
:
375
383
7.
Gonzaga-Jauregui
C
,
Ge
W
,
Staples
J
, et al.;
Geisinger-Regeneron DiscovEHR Collaboration
.
Clinical and molecular prevalence of lipodystrophy in an unascertained large clinical care cohort
.
Diabetes
2020
;
69
:
249
258
8.
Besci
O
,
Foss de Freitas
MC
,
Guidorizzi
NR
, et al
.
Deciphering the clinical presentations in LMNA-related lipodystrophy: report of 115 cases and a systematic review
.
J Clin Endocrinol Metab
2024
;
109
:
e1204
e1224
9.
Brown
RJ
,
Araujo-Vilar
D
,
Cheung
PT
, et al
.
The diagnosis and management of lipodystrophy syndromes: a multi-society practice guideline
.
J Clin Endocrinol Metab
2016
;
101
:
4500
4511
10.
Ajluni
N
,
Meral
R
,
Neidert
AH
, et al
.
Spectrum of disease associated with partial lipodystrophy: lessons from a trial cohort
.
Clin Endocrinol (Oxf)
2017
;
86
:
698
707
11.
Handelsman
Y
,
Oral
EA
,
Bloomgarden
ZT
, et al.;
American Association of Clinical Endocrinologists
.
The clinical approach to the detection of lipodystrophy–an AACE consensus statement
.
Endocr Pract
2013
;
19
:
107
116
12.
Meral
R
,
Ryan
BJ
,
Malandrino
N
, et al
.
“Fat shadows” from DXA for the qualitative assessment of lipodystrophy: when a picture is worth a thousand numbers
.
Diabetes Care
2018
;
41
:
2255
2258
13.
Köbberling
J
,
Dunnigan
MG
.
Familial partial lipodystrophy: two types of an X linked dominant syndrome, lethal in the hemizygous state
.
J Med Genet
1986
;
23
:
120
127
14.
Herbst
KL
,
Tannock
LR
,
Deeb
SS
,
Purnell
JQ
,
Brunzell
JD
,
Chait
A
.
Köbberling type of familial partial lipodystrophy: an underrecognized syndrome
.
Diabetes Care
2003
;
26
:
1819
1824
15.
Lotta
LA
,
Gulati
P
,
Day
FR
, et al.;
EPIC-InterAct Consortium
;
Cambridge FPLD1 Consortium
.
Integrative genomic analysis implicates limited peripheral adipose storage capacity in the pathogenesis of human insulin resistance
.
Nat Genet
2017
;
49
:
17
26
16.
Guillín-Amarelle
C
,
Sánchez-Iglesias
S
,
Castro-Pais
A
, et al
.
Type 1 familial partial lipodystrophy: understanding the Köbberling syndrome
.
Endocrine
2016
;
54
:
411
421
17.
Adiyaman
SC
,
Altay
C
,
Kamisli
BY
, et al
.
Pelvis magnetic resonance imaging to diagnose familial partial lipodystrophy
.
J Clin Endocrinol Metab
2023
;
108
:
e512
e520
18.
DiCorpo
D
,
LeClair
J
,
Cole
JB
, et al
.
Type 2 diabetes partitioned polygenic scores associate with disease outcomes in 454,193 individuals across 13 cohorts
.
Diabetes Care
2022
;
45
:
674
683
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