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Abstract

Background:

Polycystic ovary syndrome (PCOS) is a chronic, heterogeneous and prevalent endocrine-metabolic condition.

Objectives:

This study aims to synthesize the available evidence on the comparison of lipid accumulation product (LAP) and visceral adiposity index (VAI) in women with and without PCOS.

Design:

Systematic review and meta-analysis.

Data sources and methods:

A systematic search was executed across five electronic databases. Continuous variables were assessed using the standardized mean difference (SMD) and its 95% confidence interval (CI).

Results:

A total of 46 studies were included (n = 12,442). PCOS patients have higher values of LAP (n = 10,658; SMD: 0.65; 95% CI: 0.47–0.83, p < 0.05; I² = 93.65%) and VAI (n = 7930; SMD: 0.53; 95% CI: 0.21–0.85; p < 0.05; I² = 97.24%) compared to those without the syndrome.

Conclusion:

Women with PCOS show significantly higher VAI and LAP values than those without this syndrome.

Keywords

adiposity lipid accumulation product meta-analysis polycystic ovary syndrome systematic review visceral adiposity index

Introduction

Polycystic ovary syndrome (PCOS) is a complex multifactorial endocrinopathy that affects patients since intrauterine life-compromising multiple organs and systems.^1–5 PCOS can manifest itself as hyperandrogenism, insulin resistance, dyslipidemia, metabolic syndrome (MetS), oxidative stress, infertility, progressive hepatic dysfunction, and nonalcoholic fatty liver disease.^6–11

Although PCOS pathophysiology is not fully understood, there is a consensus that insulin resistance plays an important role and that explains a mayor part of its manifestations.¹² In this context, insulin influence on the adipose tissue and inflammation stands out, which is essential for the pathogenesis of PCOS, due to insulin stimulating adipogenesis and lipogenesis and inhibition of lipolysis, which gives place to accumulation of fat tissue.¹² This accumulation of fat can manifest as visceral adiposity, which is also responsible for insulin resistance and hyperandrogenism, the two most important components in the pathophysiology of PCOS.¹³

Although the hyperinsulinemic-euglycemic clamp is regarded as the gold standard for evaluating insulin sensitivity, its use in clinical practice is limited by high costs and difficulties in adapting it for large-scale population.¹⁴ Therefore, alternative indices become increasingly popular for identifying insulin due to their easy accessibility and cost-effectiveness. In this context, the lipid accumulation product (LAP) and the visceral adiposity index (VAI) emerge as reliable markers for assessing insulin sensitivity in both the general population and women with PCOS.^15–17

From this perspective, LAP and VAI are sensitive markers for assessing adipocyte function and central visceral obesity, probably because the central fat distribution pattern is associated with cardiometabolic consequences of insulin resistance.¹⁸ Nevertheless, appropriate adiposity indicators and their optimal cutoff values vary among PCOS women,¹⁹ which may affect the evaluation of their sensitivity and specificity.

Although there are several studies that have evaluated the value of both markers in predicting metabolic status,²⁰ its impact on hormonal and clinical parameters,²¹ its predictive value in MetS,²² and cardiometabolic susceptibility in PCOS patients.²³ As far as we know, it has not been established whether its values are different between patients with or without PCOS. This is important for establishing reference ranges that allow the identification of insulin sensitivity alterations in PCOS patients as well as for define cutoff values that improve the identification of individuals at risk for this condition. Consequently, the study aims to synthesize the available evidence on the comparison of LAP and VAI in women with and without PCOS.

Methods

Protocol and report guidelines

We submitted an abbreviated version of the protocol to the International Prospective Register of Systematic Reviews (PROSPERO), where it is recorded under the code CRD42024584215. Our manuscript adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.²⁴ The PRISMA checklist is found in Table S1.

Data sources and search strategy

The Peer Review of Electronic Search Strategies (PRESS) guidelines were used to develop our search strategy.²⁵ Our strategy was based on a combination of MeSH, Entree, and open-source terms in PubMed, which was subsequently adapted to the other databases. In Table S2, we attached the complete search strategy for all databases.

The databases searched included PubMed, Web of Science, Embase, Scopus, and Scielo. Preprint repositories were not consulted, but the bibliographic references of the included studies were examined in order to identify other potentially eligible articles. Systematic search was conducted across all databases from their inception until November 28, 2024, with no language restrictions applied.

Eligibility criteria and data extraction

The inclusion criteria encompassed (i) studies with cohort, cross-sectional, or case-control designs (ii) that reported the values of LAP and VAI in patients with and without PCOS (control group). Meanwhile, the exclusion criteria comprised letters to the editor, conference abstracts, studies without a control group, and those that did not clearly specify the diagnostic criteria used for the diagnosis of PCOS. PCOS definition was established on the diagnosticcriteria reported in the studies selected for the meta-analysis, following established international recommendations.

After retrieving records from all databases, duplicates were removed using Rayyan QCRI (Rayyan Systems Inc, Cambridge, MA, USA.^©).²⁶ This tool was also used for screening titles, abstracts, and full texts. The screening by title and abstracts was performed by four authors and the full text from the remaining records. Any conflicts or discrepancies regarding article inclusion at any phase of selection process were resolved through consensus. Subsequently, four authors extracted independently the data from each study in a Google Sheets^© template. Extracted data were study title, first author, country where the study was conducted, publication date, sample size, study design, age, and values of LAP and VAI in PCOS patients and controls.

Assessment of methodological quality (risk of bias) and publication bias

We evaluated the risk of bias using the Newcastle-Ottawa Scale (NOS) for case–control and cohort studies,²⁷ along with the adapted NOS for cross-sectional studies. A rating of ⩽6 stars meant a high risk of bias while a rating ⩾7 stars indicates that there is a low risk of bias.

We assessed publication bias using Begg’s test (publication bias was considered present with a p-value <0.1)²⁸ funnel plots, and, if necessary, the trim-and-fill method to correct for publication bias.²⁹

Data analysis

We performed a meta-analysis using STATA 17.0 software (Statacorp LLC, College Station, TX, USA). The effective measure for continuous variables was expressed as the standardized mean difference (SMD) and its 95% confidence interval (CI). We used the Hozo method to convert medians and interquartile ranges into means with standard deviations (SD).³⁰ The following formula was employed to estimate SD when studies only reported SE: SE × √(sample size).³¹ For the meta-analyses, we used the random effects model (DerSimonian and Laird method). Between-study heterogeneity was evaluated using Cochran’s Q test and the I² statistic (I² values ⩾60% were considered as high heterogeneity). Subgroup analyses were conducted based on continents, study design, PCOS diagnostic criteria, and the presence of MetS (PCOS with MetS vs Control group). The p-value for comparisons between subgroups was adjusted for multiple comparisons to a significance threshold of p = 0.025. Sensitivity analysis was performed by excluding studies with a high risk of bias and those with outlier SMD values; the latter analysis was carried out only if required by the evaluated outcome.

Results

Selection and characteristics of the included studies

According to the described search strategy, 638 studies were retrieved. After removing duplicates and screening the titles and abstracts, 83 articles remained. Finally, after full-text assessment, 46 studies were included in the meta-analysis.^{19,22,32–75} A flow diagram of the screening process is shown in Figure 1.

Figure 1.

PRISMA flow diagram.

The distribution of the included studies, by research design, was as follows: 2 cohort studies, 16 cross-sectional studies, and 28 case–control studies. These studies included a total of 12,442 women, of whom 6432 were PCOS patients. The countries that contributed the most to this review were Turkey (eight studies) and India (six studies). PCOS women have a higher average body mass index (BMI) compared to those without PCOS, although three studies did not report BMI. The diagnostic criteria used to identify PCOS in the included studies were Rotterdam consensus (41 studies),⁷⁶ United States National Institutes of Health (4 studies),⁷⁷ and only 1 study used multiple diagnostic criteria.⁵¹ Table 1 provides the main attributes of the included studies.

Table 1.

Characteristics of the included studies.

Authors	Year	Country	Mean/range age ± SD	Participants (PCOS/Control)	BMI in women with PCOS (Mean ± SD)	BMI in women without PCOS(mean ± SD)	PCOS definition criteria	Indices analyzed	Mean ± SD in women with PCOS	Mean ± SD in women without PCOS
Abbood et al.³²	2023	Iraq	PCOS: 24.52 ± 5.06 Control: 27.6 ± 4.1	139(69/70)	30.57 ± 6.07	23.64 ± 1.96	Rotterdam	VAI	1.34 ± 0.5	0.81 ± 0.21
								LAP	27.52 ± 16.21	17.45 ± 11.74
Abruzzese et al.³³	2016	Argentina	PCOS: 26.23 ± 5.38 Control: 27.81 ± 5.1	181(93/88)	29.09 ± 7.56	21.5 ± 1.89	Rotterdam	VAI	5.96 ± 6	1.49 ± 0.69
								LAP	42.8 ± 41.39	9.08 ± 3.91
Adamska et al.³⁴	2019	Poland	PCOS: 25.6 ± 4.7 Control: 26.3 ± 4.3	108(57/51)	23.9 ± 3.7	22.8 ± 3.1	Rotterdam	VAI	0.78 ± 0.48	1.01 ± 1.04
								LAP	21 ± 22.7	15.7 ± 10.7
Adanas et al.³⁵	2020	Turkey	PCOS: 24.90 ± 4.95 Control: 27.67 ± 4.68	172(106/66)	26.44 ± 5.65	23.69 ± 3.87	Rotterdam	VAI	3.58 ± 2.73	3.2 ± 2.57
								LAP	25.1 ± 24.89	16.67 ± 21.03
Alissa et al.³⁶	2021	Saudi Arabia	PCOS: 29.4 ± 0.6 Control: 29.6 ± 0.4	180(90/90)	28.1 ± 0.6	26.7 ± 0.6	Rotterdam	VAI	107.8 ± 10.5	86.1 ± 7.7
								LAP	36.3 ± 0.3	31.4 ± 3.2
Harpaci et al.³⁷	2020	Turkey	PCOS: 26.04 ± 6.98 Control: 29.29 ± 8.33	221(97/124)	24.7 ± 5.13	24.32 ± 4.5	Rotterdam	LAP	29.1 ± 27.25	29.1 ± 27.25
Atanasova Boshku and Panova Ivanova³⁸	2018	Macedonia	PCOS: 24.7 ± 4.9 Control: 24.7 ± 4.9	178(89/89)	NR	NR	Rotterdam	LAP	49.8 ± 37.9	23.96 ± 17.11
Babu et al.³⁹	2021	India	PCOS: 26.6 ± 4.3 Control: 27 ± 4.4	96(48/48)	26.2 ± 3.6	25.6 ± 3.1	Rotterdam	LAP	54.5 ± 24	38.9 ± 16.6
Biyik et al.⁴⁰	2021	Turkey	PCOS: 23.1 ± 3.3 Control: 23.5 ± 1.2	88(44/44)	22.95 ± 4.56	21.72 ± 3.85	Rotterdam	VAI	4.29 ± 3.92	2.53 ± 1.91
								LAP	131.53 ± 157.54	53.2 ± 63.29
Bozic Antic et al.⁴¹	2013	Serbia	PCOS: 24.8 ± 4.6 Control: 28.4 ± 4.9	262 (218/44)	22.4 ± 3.6	21.3 ± 3.2	Rotterdam	LAP	20.4 ± 19.3	18.2 ± 23.5
Çakır Biçer et al.⁴²	2023	Turkey	PCOS: 30.98 ± 7.52 Control:36 ± 7.2	132(66/66)	32.59 ± 3.09	26.95 ± 3.42	Rotterdam	VAI	3.77 ± 2.73	1.11 ± 0.48
								LAP	87.21 ± 43.9	24.66 ± 12.21
Demir Çaltekin el al.⁴³	2021	Turkey	PCOS: 26.41 ± 5.03 Control: 28.23 ± 5.09	84 (44/40)	24.07 ± 2.97	24.7 ± 3.7	Rotterdam	VAI	3.7 ± 2.7	2.8 ± 1.5
Caldas Costa et al.⁴⁴	2010	Brazil	PCOS: 24.4 ± 4.7 Control: 25.6 ± 4.4	197(35/162)	22.1 ± 2.2	21.7 ± 2.1	Rotterdam	LAP	22.26 ± 18.89	15.87 ± 10.39
Dawood et al.⁴⁵	2017	Egypt	PCOS: 25.1 ± 4.1 Control: 26.8 ± 4.3	90(40/50)	23 ± 1.5	22.1 ± 1.5	Rotterdam	LAP	21.9 ± 19.6	11.6 ± 4.5
De Madeiros et al.⁴⁶	2018	Brazil	PCOS: 27.3 ± 5.4 Control: 30.2 ± 4.9	625(385/240)	29 ± 6.48	24.51 ± 4.65	Rotterdam	VAI	2.43 ± 2.15	0.76 ± 0.46
								LAP	47.02 ± 51.4	15.73 ± 15.33
Dumesic et al.⁴⁷	2019	United States	PCOS: 26 ± 5.05 Control: 27.2 ± 5.09	28(10/18)	22.4 ± 0.6	21.6 ± 0.4	NIH	VAI	1.5 ± 2.21	0.7 ± 0.42
								LAP	20.4 ± 24.34	10.7 ± 5.09
Echiburú et al.⁴⁸	2016	Chile	PCOS: 26.75 ± 5.18 Control: 27 ± 7.4	136(117/19)	27.23 ± 5.04	23.67 ± 3.92	NIH	VAI	2.87 ± 1.2	1.92 ± 0.5
								LAP	41.3 ± 29.48	17.87 ± 14.29
Ehsani et al.⁴⁹	2016	Iran	PCOS: 37.1 ± 9.5 Control: 37.5 ± 8.9	220 (53/167)	28.4 ± 6.2	27.3 ± 5.23	NIH	VAI	2.6 ± 3.6	2.1 ± 1.6
Emokpae et al.⁵⁰	2024	Nigeria	PCOS: 26.92 ± 0.9 Control: 31.03 ± 0.77	140(90/50)	26.13 ± 0.93	26.4 ± 0.89	Rotterdam	VAI	0.57 ± 0.02	0.88 ± 0.02
								LAP	74.7 ± 7.22	57.78 ± 4.51
Farhadi-Azar et al.⁵¹	2022	Iran	PCOS: 31.1 ± 7.9 Control: 32.4 ± 7.6	1361(380/981)	26.23 ± 5	26.2 ± 4.6	Multiple diagnostic criteria	VAI	110.12 ± 92.8	94 ± 68.29
								LAP	29.3 ± 30.51	26.4 ± 23.11
Glintborg et al.⁵²	2016	Denmark	PCOS: 29.5 ± 5.92 Control: 30.25 ± 8.14	277(167/110)	28.2 ± 6.28	26.66 ± 5.63	Rotterdam	VAI	8.3 ± 1.18	1 ± 0.59
								LAP	34.55 ± 32.14	24.5 ± 18.5
Başar Gökcen et al.⁵³	2020	Turkey	PCOS: 25.25 ± 4.79 Control: 24.83 ± 3.17	37(17/20)	20.95 ± 2.06	21.88 ± 1.87	Rotterdam	VAI	1.34 ± 0.78	1.05 ± 0.36
								LAP	17.99 ± 14.06	14.79 ± 8.66
Gonulalan et al.⁵⁴	2021	Turkey	PCOS: 25.8 ± 7.1 Control: 27.3 ± 5.9	88(53/35)	28.22 ± 6.64	29.97 ± 6.72	Rotterdam	VAI	2.28 ± 2.18	2.55 ± 1.78
								LAP	39.84 ± 26.91	50.52 ± 42.74
He et al.⁵⁵	2020	China	PCOS: 27 ± 4.4 Control: 25.25 ± 2.22	149 (50/99)	22.3 ± 4.12	19.96 ± 2.03	Rotterdam	VAI	2.21 ± 1.45	1.37 ± 0.91
Hong et al.⁵⁶	2023	China	PCOS: 28.42 ± 6 Control: 27.7 ± 6.96	289 (217/72)	23.83 ± 3.88	21.36 ± 2.87	Rotterdam	LAP	24.68 ± 19.8	10.1 ± 6.88
Hosseinpanah et al.⁵⁷	2014	Iran	PCOS: 32.2 ± 7.7 Control: 35.4 ± 7.5	548(134/414)	26.8 ± 5.8	26.8 ± 4.9	Rotterdam	LAP	38.04 ± 35.92	37.42 ± 31.77
Kałużna et al.⁵⁸	2022	Poland	18–40	520(340/180)	23.2 ± 7.1	22.1 ± 5.5	Rotterdam	VAI	0.41 ± 0.48	0.45 ± 0.5
								LAP	6.75 ± 14.14	6.75 ± 12.37
Kılıç et al.⁵⁹	2016	Turkey	PCOS: 22.4 ± 5.2 Control: 21 ± 3.1	68 (35/33)	NR	NR	Rotterdam	LAP	38.2 ± 42.6	12 ± 13.1
Kogure et al.⁶⁰	2023	Brazil	PCOS: 28.41 ± 5.28 Control: 29.37 ± 4.97	279 (179/100)	29 ± 5.75	27.05 ± 5.91	Rotterdam	VAI	7.54 ± 5.34	5.63 ± 2.59
								LAP	44.1 ± 44.24	27.5 ± 30.7
Kumar et al.⁶¹	2017	India	PCOS: 24.75 ± 3.64 Control: 26.08 ± 3.78	120(60/60)	25.76 ± 4.93	23.77 ± 3.28	Rotterdam	LAP	36.48 ± 29.22	16.16 ± 11.93
Macut et al. (A)⁶²	2016	Serbia	PCOS: 25.01 ± 4.89 Control: 28.58 ± 4.91	267(222/45)	22.99 ± 4.57	21.62 ± 3.88	Rotterdam	LAP	28.94 ± 38.37	18.59 ± 23.47
Macut et al. (B)⁶³	2016	Serbia	PCOS: 25.6 ± 0.74 Control 31.4 ± 1.48	725 (600/125)	.30.7 ± 7.5	29.4 ± 7.4	Rotterdam	LAP	40.7 ± 3.7	32 ± 8.44
Naghshband et al.²²	2021	India	PCOS: 25.78 ± 5 Control: 24.79 ± 4.66	250(150/100)	26.73 ± 5.28	23.07 ± 3.71	Rotterdam	VAI	9.17 ± 8.08	6.61 ± 2.25
								LAP	63.49 ± 48.96	43.74 ± 20.85
Ozegowska et al.⁶⁴	2015	Poland	PCOS: 27 ± 4.4 Control: 28.5 ± 3.7	278 (168/110)	24.63 ± 5.9	21.03 ± 3.4	Rotterdam	VAI	2.52 ± 0.45	2.32 ± 1.62
Polyzos et al.⁶⁵	2014	Greece	PCOS: 26.1 ± 7 Control: 33 ± 4.41	392(314/78)	31.7 ± 7.09	28.8 ± 6.18	Rotterdam	LAP	43.3 ± 33.66	34.7 ± 27.37
Rashidi et al.⁶⁶	2018	Iran	PCOS: 32 ± 7.8 Control: 34 ± 7.3	577(153/424)	NR	NR	Rotterdam	LAP	3376.19 ± 2742.13	2995.78 ± 3257.01
Saxena et al.⁶⁷	2023	India	PCOS: 26.75 ± 3.7 Control: 43.75 ± 18.5	510 (143/367)	24.77 ± 5	23.98 ± 3.74	Rotterdam	VAI	2.58 ± 0.94	2.05 ± 0.95
								LAP	27.57 ± 5.26	20.6 ± 16.05
Sethi et al.⁶⁸	2023	India	PCOS: 23.99 ± 3.58 Control: 25.22 ± 3.98	100 (50/50)	26.36 ± 4.92	25.09 ± 3.91	Rotterdam	LAP	39.58 ± 24.82	26.73 ± 16.76
Sharma et al.⁶⁹	2022	India	PCOS: 24.11 ± 4.6 Control: 25.24 ± 0.25	90 (45/45)	25.92 ± 2.92	22.9 ± 2.87	Rotterdam	VAI	3.97 ± 11.32	3.38 ± 1.01
Soares-Jr et al.⁷⁰	2023	Brazil	PCOS: 25.5 ± 5.5 Control: 28.4 ± 5.4	381(310/71)	30.6 ± 7.2	28.8 ± 6.5	Rotterdam	VAI	2.37 ± 1.77	1.46 ± 1.22
Sóter et al.⁷¹	2015	Brazil	PCOS: 30.6 ± 4.9 Control: 29.4 ± 7.7	196(97/99)	28.7 ± 5.78	23.1 ± 4	Rotterdam	LAP	23.4 ± 37.92	10.12 ± 13.55
Techatraisak et al.⁷²	2015	Thailand	PCOS: 25.42 ± 5.6 Control: 25.5 ± 4.7	441 (399/42)	26.1 ± 7.3	21.5 ± 3.7	Rotterdam	VAI	4.05 ± 3.59	3.59 ± 1.41
Ramezani Tehrani et al.¹⁹	2014	Iran	PCOS: 30.3 ± 2.66 Control: 27.62 ± 0.96	605(80/525)	26.5 ± 1.81	24.7 ± 0.74	Rotterdam	VAI	2.6 ± 1.18	2.3 ± 0.59
								LAP	35.2 ± 10.51	28.87 ± 4.37
Vassilatou et al.⁷³	2018	Greece	PCOS: 27.5 ± 7.1 Control: 32.1 ± 8.1	290 (145/145)	31.8 ± 6.9	30.5 ± 6.9	Rotterdam	VAI	1.9 ± 1.5	1.5 ± 1.2
								LAP	45.7 ± 37.6	35.3 ± 29.5
Zhang et al.⁷⁴	2015	China	PCOS: 29 ± 5Control: 30 ± 5	141 (78/63)	27.6 ± 4	25.3 ± 3	Rotterdam	VAI	3.98 ± 2.24	1.61 ± 1.03
Zhao et al.⁷⁵	2015	China	PCOS: 28.74 ± 2.82 Control: 26.797 ± 4.19	186(95/91)	24.83 ± 4.29	22.37 ± 4.01	NIH	LAP	45.75 ± 34.69	26.79 ± 30.81

BMI, body mass index; LAP, lipid accumulation product; NIH, National Institutes of Health; NR, not reported; PCOS, polycystic ovary syndrome; SD, standard deviation; VAI, visceral adiposity index.

In the assessment of methodological quality (risk of bias), only 9 studies were classified as high risk of bias, while the remaining 37 studies were of low risk of bias (Table S3).

Differences in LAP values between PCOS and non-PCOS women

A total of 38 studies (n = 10,658 women) assessed LAP values in PCOS patients and controls. Patients with PCOS had higher LAP values than controls without the syndrome (SMD: 0.65; 95% CI: 0.47–0.83; p < 0.05; I² = 93.65%; Figure 2). In the subgroup analysis (Figure S1) according to continents, study design, diagnostic criteria, and presence of MetS (PCOS with MetS vs Control group), significant differences were maintained with high heterogeneity. Upon performing the sensitivity analysis and excluding studies with a high risk of bias, the significant difference remained (SMD: 0.61; 95% CI: 0.42–0.81; p < 0.05; Figure S2) with high heterogeneity (I² = 94.36%).

Figure 2.

Forest plots of studies comparing LAP values in women with and without polycystic ovary syndrome.

Differences in VAI values between PCOS and non-PCOS women

A total of 29 studies (n = 7930 women) assessed VAI values in PCOS patients and controls. Patients with PCOS had higher VAI values than controls without the syndrome (SMD: 0.53; 95% CI: 0.21–0.85; p < 0.05; I² = 97.24%; Figure 3). In the subgroup analysis (Figure S3) according to continents, study design and diagnostic criteria, no significant difference was found only in the subgroup of cross-sectional studies. Similarly, only the subgroup of studies that did not use the Rotterdam criteria to diagnose PCOS had low heterogeneity (I² = 54.8%). The subgroup analysis based on the presence of MetS could not be performed due to a lack of information provided by the included studies. Upon performing the sensitivity analysis (Figure S4), significant differences remained with high heterogeneity after excluding studies with a high risk of bias (SMD: 0.5; 95% CI: 0.13–0.87; p < 0.05; I² = 97.78%) and outliers in SMD values (SMD: 0.57; 95% CI: 0.39–0.76; p < 0.05; I² = 91.55%).

Figure 3.

Forest plots of studies comparing VAI values in women with and without polycystic ovary syndrome.

Publication bias

Publication bias was detected when evaluating the difference in LAP values between PCOS and non-PCOS individuals (Begg’s test, p < 0.1). The publication bias was corrected using the trim and fill method (SMD: 0.862; 95% CI: 0.662–1.062; Figure S5). No publication bias was found when evaluating the difference in VAI values between PCOS and non-PCOS individuals (Begg’s test, p = 0.227; Figure S6).

Discussion

Our main results show that VAI and LAP values were greater in PCOS patients than in non-PCOS patients, although with high heterogeneity. These markers are two indicators used to predict various conditions in the adult population, such as the risk of chronic kidney disease,^78,79 diabetes mellitus,⁸⁰ arterial hypertension,⁸⁰ or nonalcoholic fatty liver disease.⁸¹ Although the causes of these associations are not completely understood, they are related to the processes that are activated when visceral adiposity increases.^78,79 Thus, as in the adult population, patients with PCOS, show that both indices are associated with metabolic alterations and related conditions that are also associated with visceral adiposity.^20–23 This association is mediated by increased insulin resistance, which in the case of PCOS patients is a fundamental part of its pathogenesis.¹²

Different approaches have been proposed to attempt to explain the association between visceral adiposity and insulin resistance. Visceral adipocytes secrete both adipose tissue-specific cytokines and pro-inflammatory cytokines, which contribute to the exacerbation of insulin resistance.^82,83 Additionally, macrophage accumulation into visceral adipose tissue stimulates the release of inflammatory cytokines, which impair insulin sensitivity.⁸⁴ The presence of excessive adipose tissue can induce inflammation thereby increasing insulin resistance.^85,86 In line with this, an increase in adipose tissue associated with low adiponectin values may worsen insulin resistance and metabolic disturbances.⁸⁷ The classic phenotype of PCOS patients encompasses obesity, elevated insulin levels, and heightened insulin resistance, despite the absence of significant BMI variations compared to other phenotypes.⁸⁸ This evidence emphasizes that insulin resistance represents the most important pathophysiological manifestation in PCOS patients.^12,88

In this context, LAP and VAI are valuable indicators to discriminate insulin resistance in patients with PCOS. A study investigating the most effective marker of cardiometabolic risk in PCOS patients, revealed that LAP exhibited a specificity of 60%, a sensitivity of 70%, and a positive predictive value of 80% for identifying insulin resistance.¹⁹ Similarly, VAI achieved a specificity of 72%, a sensitivity of 60%, and a positive predictive value of 83% to predict it.¹⁹ However, it is essential to emphasize that these values were established considering that the average value in women with PCOS for LAP was 33.8 and in the normal population 34.7, while for the case of VAI it was 1.8 in women with PCOS and 1.6 in the normal population.¹⁹ In other words, these results suggest the need to investigate whether LAP and VAI values are increased in PCOS patients compared to controls.

Despite the limitations of the individual studies included in our analysis, our results provide evidence suggesting that the values of both markers are higher in PCOS patients than in non-PCOS patients. As noted, the possible explanation for these results is related to the ability of both markers to predict insulin resistance,¹⁹ which is the cornerstone in the pathophysiology of PCOS.^12,88 However, there are other considerations to take into account when interpreting it. Our results seem to be indirectly linked to the type of adipose tissue in PCOS patients, which varies with respect to women without this disease.¹⁹ In fact, PCOS patients have an increased tendency for upper-body fat accumulation when compared to weight- or BMI-matched controls^89,90; this pattern is also seen even in lean PCOS patients.^89,91 Although lean PCOS patients may not exhibit higher insulin resistance compared to healthy controls, an elevated BMI could exacerbate this condition in all women.¹⁹

This means that the high heterogeneity found in the results of our research could be related to variations in the characteristics of the populations studied, which includes variations in BMI. Likewise, although there are no studies on this relationship in patients with PCOS, there is evidence that suggests that the association between LAP and total bone mineral density of the femur can be modified by the poverty–income relationship,⁹² which is something that also deserves investigation in cases of patients with PCOS. This review included studies that applied different diagnostic criteria for PCOS; however, the various existing phenotypes were not considered, and variation in their frequency could explain heterogeneity.⁹³

Our results have important implications for public health. Additionally to the known differences in VAI and LAP values between women with and without PCOS,^20,22 these markers could be used as mortality predictors in this population. Although no studies have focused on PCOS patients, one study investigated the association between LAP values and mortality from all causes and cardiovascular disease, using data from the National Health and Nutrition Examination Survey.⁹⁴ Their results determined that, compared with participants in LAP quartile 1, the adjusted hazard ratios of participants in LAP quartile 4 were 1.55 for cardiovascular disease mortality and 1.54 for all-cause mortality.⁹⁴ The study also showed that for every unit increase in natural log-transformed LAP, there was a 14% increase in cardiovascular disease mortality and a 22% increase in all-cause mortality.⁹⁴ Based on the evidence presented, LAP and VAI should be part of the routine clinical evaluation in PCOS patients. Since these markers are associated with the incidence of diabetes,^95,96 MetS,⁹⁷ or cardiometabolic multimorbidity,⁹⁸ clinicians should monitor them to identify PCOS women who are at greater risk of cardiometabolic disturbances. Furthermore, future research should determine whether these composite indices are superior to simple indicators such as BMI, waist circumference, or a lipid profile for assessing the risk of metabolic diseases.

Our study has limitations that must be taken into account when interpreting the results. Firstly, we evidence high heterogeneity due to multiple methodological (e.g., study designs or diagnostic criteria for PCOS) and clinical variations (e.g., differences in BMI, age groups, severity of insulin resistance, or disease duration). Many unmeasured factors could be contributing to this variability, and it is important to recognize that the true magnitude of the differences between LAP and VAI may vary across population groups, potentially influenced by lifestyle, dietary patterns, and environmental exposures. Second, many of the studies that were included didn’t carefully match or adjust for BMI, age, or other variables (e.g., comorbidities, sociodemographic conditions, lifestyles, among others), when comparing PCOS to controls. Therefore, it would be important for future studies to evaluate this variables, either to adjust for marker values or to explore possible causes of heterogeneity in further subgroup analysis. In particular, assessing associations stratified by BMI category (lean vs obese PCOS), insulin resistance status (insulin-resistant vs noninsulin-resistant PCOS), and different age groups would enhance the clinical relevance of the findings. Third, the absence of significant differences in VAI values between PCOS women and controls in the subgroup analysis of cross-sectional studies could be explained by differences in the study setting (community-based vs clinic-based), disease duration, or other unmeasured confounders; therefore, future prospective cohort studies are needed to better evaluate this topic. Fourth, given that most included studies employed a cross-sectional design, our findings are limited to associations rather than causal relationships. Finally, due to insufficient data in the studies, we were unable to identify an optimal cut-off value for predicting the risk of PCOS.

Nevertheless, a significant strength of this study is the inclusion of numerous studies and participants, facilitating the performance of multiple subgroup analyses. To the best of our knowledge, this systematic review and meta-analysis constitutes the first comprehensive synthesis of evidence on VAI and LAP in PCOS and non-PCOS individuals.

Conclusion

Patients with PCOS show significantly higher VAI and LAP values than those without PCOS. Future investigations should aim to clarify the association between these markers and PCOS, including assessments of their sensitivity, specificity, and optimal cut-off values for predicting PCOS risk.

Supplemental Material

sj-docx-1-tae-10.1177_20420188251397203 – Supplemental material for Lipid accumulation product and visceral adiposity index in women with and without polycystic ovary syndrome: a systematic review and meta-analysis

Supplemental material, sj-docx-1-tae-10.1177_20420188251397203 for Lipid accumulation product and visceral adiposity index in women with and without polycystic ovary syndrome: a systematic review and meta-analysis by Juan R. Ulloque-Badaracco, Enrique A. Hernandez-Bustamante, Juan C. Cabrera-Guzmán, Jose E. Delgado-Raygada, Giuseppe Dotto-Vasquez, Gian F. Maldonado-Basurto, Alberto A. Figueroa-Larragán, Katherin Z. Trujillo-Jurado, Gustavo Evaristo-Ballmann, Aldo Flores-Gavino, Percy Herrera-Añazco and Vicente A. Benites-Zapata in Therapeutic Advances in Endocrinology and Metabolism

Footnotes

Acknowledgements

None.

Declarations

ORCID iDs

Juan R. Ulloque-Badaracco

Juan C. Cabrera-Guzmán

Jose E. Delgado-Raygada

Giuseppe Dotto-Vasquez

Alberto A. Figueroa-Larragán

Katherin Z. Trujillo-Jurado

Gustavo Evaristo-Ballmann

Aldo Flores-Gavino

Percy Herrera-Añazco

Vicente A. Benites-Zapata

Supplemental material

Supplemental material for this article is available online.

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