Special Considerations on Hyperandrogenism and Insulin Resistance in Nonobese Polycystic Ovaries Syndrome

Abstract

PCOS is a widespread phenotypically inhomogeneous endocrinopathy with significant health consequences and incompletely elucidated pathogenesis. Though visceral adiposity and insulin resistance (IR) is a well-proved pathogenic set of factors of PCOS, not all women with obesity and IR have PCOS and not all PCOS women are obese and have IR, which is explained by certain genetic backgrounds. The reported prevalence of nonobese PCOS (NonObPCOS) is about 20–30%, but it may be higher because especially in lean women with nonclassical phenotypes PCOS diagnosis is often delayed or unrecognized. Unlike obese PCOS, NonObPCOS management is less clear and is limited to symptomatic treatment. This chapter presents in structured fashion the existing results on the prevalence of NonObPCOS, as well as on special aspects of body composition, IR, and hyperandrogenism pathogenesis, including adrenal contribution in NonObPCOS.

1. Introduction

Today with the use of Rotterdam diagnostic criteria (at least two of three are present—oligo-anovulation, clinical/biochemical hyperandrogenism (HA), polycystic ovarian morphology (POM) on ultrasound when other causes of these conditions are excluded) polycystic ovary syndrome (PCOS) is the most widespread endocrine disorder in women affecting their reproductive and cardio-metabolic health lifelong [1, 2, 3, 4, 5]. PCOS prevalence among reproductive-aged women is from 8 to13% depending on the population ethnicity and diagnostic criteria used [3]. A meta-analysis published in 2017 showed such proportions of PCOS prevalence (95% CI) according to the diagnostic criteria of the National Institute of Health (NIH), Rotterdam criteria, and Androgen Access PCOS Society (AE-PCOS)—6%, 10%, and 10%, respectively. When only unselected population studies were included, the given rates were 6%, 9%, and 10% [6]. Same year meta-analysis of PCOS prevalence in different ethnic groups showed the lowest prevalence in Chinese women (Rotterdam criteria: 5.6%), Caucasians (NIH: 5.5%), Middle Eastern (NIH 6.1%; Rotterdam 16.0%; AE-PCOS 12.6%), and Black women (NIH: 6.1%) [7]. Despite intensive investigations PCOS etiology remains unclear, relations between its known pathogenic mechanisms are contradictory and consequently the effectiveness of overall management is suboptimal leading to patient dissatisfaction [8]. The reason for this is the high heterogenicity of PCOS in terms of complex genetic background, involvement of developmental origins, and consequently various combinations of pathogenetic mechanisms and clinical features [9, 10].

Recommendations from the international evidence-based guideline for the assessment and management of polycystic ovary syndrome 2018 confirmed the idea of the 2012 international working group on the need of defining PCOS phenotypic forms based on combinations of diagnostic criteria in research and clinical practice. Thus, there are four phenotypic forms of PCOS—classic (A) involving all three diagnostic criteria, incomplete classic (B), ovulatory (HA and POM only) (C), and normoandrogenic (anovulation and POM only) (D). Back in 2012, much hope was relied on studying PCOS pathogenesis in different phenotypes, but to date, this approach resulted in no definitive answers on details of pathogenesis in different phenotypic forms. It was shown by numerous studies that classic phenotypes are more often associated with obesity, significant visceral adiposity, metabolic syndrome (MS), lifetime risks of type 2 diabetes mellitus (TDM), and cardiovascular disease (CVD) [11, 12, 13]. At the same time, ovulatory and normoandrogenic phenotypes seem to be more metabolically safe primarily because of a lower incidence of obesity [12, 13, 14]. Though this observation is not universal. Moreover, recent studies showed that while obesity in PCOS universally leads to metabolic complications, hyperandrogenic non-obese women with PCOS (nonObPCOS) also have serious metabolic disturbances, such as nonalchocholic fatty liver disease (NAFLD), dyslipidemia, hyperinsulinemia, and age-related complications like TDM and CVD, in spite of the absence of obvious risk factors [15].

Apart from androgen excess and hypothalamic-pituitary-gonadal axis disfunction, there are two other gross pathogenetic factors of PCOS not included in diagnostic criteria—insulin resistance (IR) with compensatory hyperinsulinemia and ectopic fat distribution with adiposopathy [16, 17]. All these factors are very much interconnected and the question of which of them is primary is still unclear. This can be explained by the fact that the primary factors of encircled pathogenic mechanisms are different in different subgroups of PCOS patients and probably change with time.

The role of overall and especially visceral adiposity is well established in overweight and obese PCOS women [18, 19, 20, 21]. Weight reduction is an effective therapeutic approach both for fertility and menstrual function improvement and metabolic risks reduction in overweight/obese women with PCOS [22, 23, 24, 25]. But this is not the case with NonObPCOS women. In the scientific aspect, the absence of the main driver of glucose and fat metabolism disruption (obesity) makes NonObPCOS a different pathogenetic subtype of the syndrome.

Reviews summarizing data on NonObPCOS were published in 2017 [26, 27]. In this chapter, we analyze older and recent data on epidemiology, body composition, pathogenesis of insulin resistance, and hyperandrogenism in NonObPCOS.

2. Definition, epidemiology of nonobese PCOS and clinical issues of this population

NonObPCOS gained more researchers’ attention after the introduction of Rotterdam criteria. This term emerged spontaneously and there is still no clear-cut definition of professional societies, guidelines, or consensus statements. Most authors use the criteria of BMI under 25 kg/m² and address it as “normal weight PCOS” or “lean PCOS” or “nonobese PCOS” (NonObPCOS) [28]. In some sources, NonObPCOS is addressed as women with PCOS having BMI under 30 kg/m² [29]. In this case, it includes both the category of overweight women (BMI 25–29) and normal weight (BMI 18–24). There is data on rare cases of underweight PCOS (BMI < 18), which has to be carefully differentiated with hypothalamic amenorrhea [30, 31]. In this chapter, NonObPCOS will be addressed as any PCOS phenotypic form with BMI < 25 kg/m². Lack of a clear-cut definition of NonObPCOS using BMI criteria leads to inconsistent results on its prevalence in different populations. We did not observe studies focused specifically on the prevalence of NonObPCOS. Most available data on the prevalence of nonObPCOS comes from studies on the prevalence of PCOS in different populations or studies targeting metabolic derangements of PCOS and having BMI stratification in their design (Table 1). As follows from Table 1, the portion of NonObPCOS even with the use of NIH criteria in older studies varies from 20 to 76% [39, 44]. With the use of Rotterdam criteria, the percentage of NonObPCOS varies from 41 to 75% [29, 32, 33, 34, 35, 36, 38, 40, 41, 42, 43]. Heterogeneity in studies’ methodology, participants age, and other factors certainly influence the accuracy of these figures, but still depicts the fact that NonObPCOS is not a minority in this syndrome. Of note is that a greater proportion of NonObPCOS cases is observed in nonselective studies compared to clinical ones when (cohorts of women seeking medical help for hirsutism, menstrual irregularity, etc.).

Author, year	Country	Design, PCOS diagnostic criteria	n	Age	%BMI <25	%BMI >25
Neubronner (2021) [32]	Singapore	Prospective cross-sectional, healthy women health screen, Rotterdam Criteria	134	21–45	46*	54***
Jena (2021) [33]	India	Hospital-based observational, prospective, Rotterdam Criteria	251	20–40	43.6*	66.4 (BMI ≥25–29-62.94; BMI > 3 0–3.5)
Tosi (2017) [34]	Italy	Retrospective analysis of symptomatic women referred outpatient, Rotterdam Criteria	375	18–45	45.9*	54.1 (BMI ≥25–29-18.9; BMI > 3 0–35.2)
Rashidi (2014) [35]	Iran	Cluster sampling method, NIH, Rotterdam, AE Criteria	602	18–45	41*	59 (BMI ≥25–29-36.9; BMI > 3 0–22.1)
Lauritsen (2014) [36]	Denmark	Prospective, cross-sectional, employees, Rotterdam, AE Criteria	447	20–40	69.9*	31.1 (BMI 25–29 ≥ 16.2; BMI > 3 0–14.9)
Musmar (2013) [37]	Palestine	Cross sectional, students, NIH Criteria	137	18–24	60	30
Li (2013) [38]	China	Epidemiological, 10 provinces, Rotterdam Criteria	159 24	19–45	65.9*	34.1***
Gill (2012) [39]	India	Cross sectional, students, NIH Criteria	152 0	18–25	76*	24***
Yildiz (2012) [29]	Turkey	Cross-sectional, employees, NIH, Rotterdam, AE Criteria	392	18–45	**	**
Moran (2010) [40]	Mexico	Prospective cross-sectional, volunteers, NIH, Rotterdam Criteria	150	20–45	66	34
Chen (2008) [41]	China	Observational with a parallel study, unselected, Rotterdam, AE Criteria	915	20–45	75	25
Azziz (2004) [42]	USA	Prospective, preemployment exam, AE Criteria	400	18–45	32	68 (24 – BMI > 30; 42 BMI > 25)
Asuncion (2000) [43]	Spain	Prospective, blood donors, Rotterdam Criteria	154	18–45	60	40
Michelmore (1999) [44]	England	Cross-sectional observational, volunteers, NIH	230	18–25	20*	80

Table 1.

Prevalence of NonObPCOS.

^*

Figure obtained by subtraction of the percentage of BMI > 25.

^**

Non-obese (<30 kg/m²) NIH -75.0%; Rotterdam 84.6% AE-PCOS 85.0%. Obese (≥30 kg/m²) NIH -25.0%; Rotterdam 15.4% AE-PCOS 15.0%.

^***

Criterion of ≥27 kg/m² was used for obesity and < 23 kg/m² for normal weight.

Data from a meta-analysis by Lizneva et al. [45] supports the notion of the underestimated prevalence of NonObPCOS, probably more often associated with nonclassic phenotypes. The aim of this paper was to evaluate the prevalence of PCOS phenotypes and obesity among patients detected in referral versus unselected populations. The prevalence of more complete phenotypes in PCOS and mean BMI was higher in subjects identified in referral versus unselected populations, suggesting the presence of significant referral bias. The authors analyzed 41 eligible studies. Pooled estimates of detected PCOS phenotype prevalence were consequently documented in referral versus unselected populations, as phenotype A, 50% (95% confidence interval [CI], 46–54) versus 19% (95% CI, 13–27); phenotype B, 13% (95% CI, 11–17) versus 25% (95% CI, 15–37); phenotype C, 14% (95% CI, 12–16) versus 34% (95% CI, 25–46); and phenotype D, 17% (95% CI, 13–22) versus 19% (95% CI, 14–25). Differences between referral and unselected populations were statistically significant for phenotypes A, B, and C. Referral PCOS subjects had a greater mean BMI than local controls, a difference that was not apparent in unselected PCOS [45].

In the setting of the Endocrine gynecology department, Kyiv, Ukraine preliminary patients’ database analysis from 2012 to 2021 shows the prevalence of 64% NonObPCOS among all referral PCOS patients (including primary visits of symptomatic patients and referrals from primary care gynecologists because of difficulties in making the diagnosis). We suggest that such prevalence of NonObPCOS in our fourth level institution is caused by uncertainties primary care doctors face in diagnosing PCOS in lean patients especially with mild HA or nonclassical phenotypes as well doubts of patients in the correctness of the diagnosis. With these patients, we often observe interesting phenomena of “not being prone to gaining extra weight” and “having no need to control their calorie intake”, which might be a presentation of a “specific type of metabolism worth deeper investigation in terms of metabolic consequences.” Thus, available data on NonObPCOS prevalence shows, that this condition is not rare, but likely to be underdiagnosed or diagnosed with delay.

Today it is obvious that BMI is not an accurate marker of metabolic health since not only adipose tissue excess but more its distribution plays role in metabolic complications, giving the basis for A. De Lorenzo classification—normal weight obese; metabolically obese normal weight; metabolically healthy obese; and metabolically unhealthy obese or “at risk” obese [46, 47]. Thus, while the presence of elevated BMI has a significant positive predictive value for metabolic risks normal BMI does not guarantee their absence since they can be caused by the predominance of ectopic fat distribution and adiposopathy. This fact is considered by most studies of PCOS metabolic aspects discussed below. Studies considering body composition and fat distribution are also inhomogeneous in methodology as will be shown below. In addition, the more accurate methods of body composition evaluation are used the smaller the groups are.

3. Body composition in NonObPCOS, specifics of adipose, and muscle tissue function

In the case of NonObPCOS, we think it is reasonable to analyze body composition data in the first place, as it may have the key to a paradox—of keeping normal BMI despite the presence of predisposing factors, such as HA and IR, and at the same time developing metabolic consequences. Recent studies on bidirectional Mendelian randomization analyses state that increased BMI is causal for PCOS while PCOS is not predictive of obesity [48, 49]. This finding puts even more questions on obese and nonobese PCOS pathogenesis. One of the interpretations can be that high BMI in PCOS is a factor exacerbating epigenetically determined features of the syndrome, such as HA, OD, and IR. This notion is supported by studies demonstrating the presence of IR in most PCOS women irrespective of BMI, though it is positively correlated with BMI. The similar association can be observed for HA—more mild forms of HA are observed in NonObPCOS compared to PCOS with obesity [11, 16, 34]. Taken together these facts shifted research focus from fat mass to the role of the functional state of muscles and different adipose tissue compartments in PCOS pathogenesis. Today adipose tissue (AT) is a recognized player of endocrine, paracrine, and even neurocrine cross-talks, being a target tissue of pancreatic and steroid hormones, source of numerous adipokines, and a place of sex steroids conversion [50]. Visceral AT (VAT) demonstrates more endocrine/paracrine actions [17, 51]. Skeletal muscles are also among the key target organs of pancreatic hormones and sex steroids as well as an important player in metabolism [13]. Thus, studies on body composition’s role in and tissue-specific effects of insulin action, androgen synthesis, and lipid turnover seem to be most perspective, especially in the case of NonObPCOS.

Most studies on the body composition of PCOS women were done using anthropometric characteristics that lack accuracy compared to imaging methods (MRI, CT). This led to the formation of the dogma of visceral adiposity in PCOS, which is being debunked by 2019 meta-analysis that using golden standards MRI or CT found no significant difference in accumulations of visceral fat, abdominal subcutaneous fat, total body fat, trunk fat, and android fat in PCOS compared to BMI matched controls. At the same time, meta-regression and subgroup analyses showed that young and non-obese patients were more likely to accumulate android fat [52]. The authors of the paper note the problem of small sample size in studies using gold standard methods for body composition assessment.

Studies on body composition in NonObPCOS in relation to endocrine dysfunctions are limited. In a cross-sectional study of Indian nonobese and obese PCOS women assessed by DXA-scan, higher total body fat, truncal fat, and estimated VAT compared to their age- and BMI-matched controls were reported. Corrected estimated VAT difference was not significant between obese and nonobese PCOS women suggesting that nonobese PCOS women had a similar amount of VAT as that of obese PCOS women when adjusted for their body weight. Also, this study reports that NonObPCOS (overweight and normal weight) were less insulin resistant when compared to the obese PCOS group and postulate that there may be factors other than IR that make the nonobese PCOS women have more VAT, such as postprandial dysglycemia caused by intracrine intestinal factors [53].

Earlier studies of SAT topography in PCOS women using optical devices demonstrated significantly lower total SAT development with a slightly lowered amount of body fat in the upper region and a highly significant leg SAT reduction [54, 55].

In a prospective cohort study of six normal-weight PCOS women and 14 age- and BMI-matched normoandrogenic ovulatory controls, an association of HA with preferential intra-abdominal fat deposition and an increased population of small subcutaneous (SC) abdominal adipocytes was shown. Authors hypothesize that such distribution could constrain SC adipose storage and promote metabolic dysfunction [56]. In vitro studies showed that cultured subcutaneous abdominal adipocytes from women with PCOS have diminished insulin-induced glucose transport, reduced insulin receptors content, and decreased insulin-stimulated serine phosphorylation of glycogen synthase kinase (GSK)-3β [57, 58]. Further investigations of SAT-specific features in NonObPCOS by the Dumesic group discovered more details of these compartments’ role in PCOS-related dysmetabolism. A prospective cohort study including ten normal-weight women with PCOS and 18 control subjects matched for age and BMI demonstrated that NonObPCOS women have increased adipose-IR and altered adipose stem cell gene expression related to HA and IR [59]. The fact that the number of small adipocytes is stable from early childhood suggests the possibility that SC abdominal adipose expandability through the generation of new small adipocytes is programmed in early life and subsequently becomes insufficient to meet the metabolic demands of most normal-weight women with PCOS. We did not find studies on birthweight, prematurity, and puberty details focusing specifically on NonObPCOS but they might be of great interest. Results of prospective cohort study show accelerated SAT abdominal adipose stem cell differentiation into adipocytes in vitro favors sensitivity to insulin in vivo, suggesting a role for HA in the evolution of metabolic thrift to enhance fat storage through increased cellular glucose uptake [60].

The role of local androgen conversion in the regulation of abdominal SAT morphology and function is not yet clear in NonObPCOS. Overexpression of aldo-keto reductase 1C3 (AKR1C3)-mediated testosterone (T) generation from androstenedione (A4) promotes local triglyceride (TG) storage in SAT, potentially protecting against lipotoxicity and IR. One study showed that elevated serum T to A4 ratio was a biomarker of subcutaneous abdominal AKR1C3 activity that improved metabolic function in NonObPCOS [61].

Summarizing the existing limited data on AT distribution and function in NonObPCOS, it can be concluded that these women have a predominance of dysfunctional VAT and specific features of SAT limiting its lipid storage capacity. This puts NonObPCOS in the category of normal weight obese or metabolically obese. Metabolic significance of VAT is explained by the following facts—its location in the mesentery and omentum causes drainage directly through the portal circulation to the liver; the dominance of large or hypertrophic adipocytes and infiltration with immune cells; intensive vascularization and innervation; high density of androgen and glucocorticoid receptors; higher sensitivity to lipolysis and adrenergic stimulation and lower sensitivity to insulin; greater capacity to generate free fatty acids and to uptake glucose, circulating free fatty acids (FFA), and TG [62]. The impaired ability of SAT to store abundant lipids as well as SAT excess leads to accumulation of lipids in atypical sites (liver, skeletal muscles, and even pancreas), known as lipotoxicity phenomena. Lipotoxicity has detrimental effects on a molecular level—endoplasmatic reticulum and mitochondria damage with reactive lipid peroxides (endoplasmatic reticulum stress). The latter can eventually lead to cell apoptosis. At the same time, high levels of circulating FFA leads to a vicious circle of deepening glucose dysmetabolism by limiting blood glucose uptake in AT and muscles [63, 64]. Another effect of AT dysfunctional state in PCOS is altered synthesis of adipokines. Upregulated levels of mRNA levels of the proinflammatory cytokine tumor necrosis factor (TNF) in PCOS reflecting a state of chronic low-grade inflammation in SAT that could lead to low adiponectin were reported [65]. Later independent of BMI and IR decrease in high molecular weight adiponectin in PCOS was demonstrated [66].

The etiology of the described specifics of body composition and AT dysfunction most likely takes roots in genetics and epigenetics. In 2016, Kokosar et al. reported a number of genes and pathways that are affected in adipose tissue from women with PCOS as well as some specific DNA methylation pathways that may affect mRNA expression [67].

Though skeletal muscles also belong to insulin-sensitive organs and normally can utilize up to 70–80% of blood glucose, there are far less studies on specific features of their function in PCOS. There are a lot of debatable aspects to this topic. While osteosarcopenia was reported in obese PCOS no such studies are available for NonObPCOS [68]. On one hand, studies from sports medicine report a positive effect of higher physiological androgen levels on muscle performance as well as superior performance of mildly hyperandrogenic women in sports [69]. On the other hand, peripheral IR documented by euglycemic hyperinsulinemic clamp test in the majority of obese and NonObPCOS suggests the presence of some insulin signaling defect similar to that of TDM [70, 71]. Recent studies by N. Stepto and Hansen suggest that this defect is located in the distal part of the insulin signaling pathway but there may also be additional mechanisms [71, 72]. Hansen suggests that reduced expression and activation of AMP-activated protein kinase (key regulator of glucose uptake in muscle) is due to low levels of adiponectin [72]. Infiltration of muscle tissue with lipids both in lean and obese PCOS either due to lipodystrophy or due to fat excess may be one of the accidental causes of IR [73]. Transforming growth factor-beta (TGFβ) signaling contributes to the remodeling of reproductive and hepatic tissues in women with PCOS. It is possible that these adverse effects including profibrotic changes of extracellular matrix influence insulin signaling in skeletal muscles [74, 75]. The most recent study by Stepto et al. tested the hypothesis that TGFβ superfamily ligands signaling pathways and tissue fibrosis are involved in PCOS-specific insulin resistance. These signaling defects are probably involved in PCOS ovulatory dysfunction too [76]. The results of this study showed reduced signaling in PCOS of the mechanistic target of rapamycin (mTOR). Notably, exercise augmented but did not completely rescue this signaling defect. Molecular tests showed that genes in the TGFβ signaling network were upregulated in skeletal muscle in the overweight women with PCOS but were unresponsive to exercise except for genes encoding lipid oxidation, collagen 1 and 3 [77]. Authors admit a limited number of patients and inability to rule out the influence of other factors, such as HA, cardiometabolic fitness, and body composition. In in vitro study of TGFβ effects on myotubules suggest its indirect role in peripheral IR in PCOS [78]. Another study with in vivo and in vitro arms state that altered mitochondrial-associated gene expression in skeletal muscle in PCOS is not preserved in cultured myotubes, indicating that the in vivo extracellular milieu, rather than genetic factors, may drive this alteration [79]. Thus, molecular dysfunctions underlying peripheral resistance in women with PCOS in combination with the hormonal milieu need further investigation as they are likely to be the main cause of intrinsic IR and a perspective therapy target.

4. Specifics of androgen excess in NonObPCOS

HA is one of PCOS diagnostic criteria both by NIH, Rotterdam, and AE-PCOS criteria. Clinical HA implies mainly the presence of hirsutism. Biochemical HA is a stable elevation of circulating androgens over gender, age, and population-specific reference range. Free testosterone was traditionally considered the best maker of active androgen excess, but as its assessment with available indirect methods is not enough, an accurate estimated value of free androgen index (FAI) is recommended for clinical routine [1, 80]. In 2018, active androgens’ precursors (dehydroepiandrosterone sulfate (DHEA-s) and A4) were recognized as useful markers of mild HA present in about 30% of PCOS and recommended for lab assessment in some cases [1]. Recent studies have demonstrated that 11-oxygenated androgens can be regarded as a marker of HA in PCOS [81]. Multiple bidirectional effects of HA with IR, OD, and adiposopathy in PCOS as well as their biological effects were consistently described in many reviews [16, 50, 82].

In this chapter, we address the proportional contribution and specific effects of androgens from different sources in NonObPCOS. HA in PCOS has complex nature involving ovarian, adrenal sources, peripheral tissue androgen synthesis, and conversion; elevated free T due to low sex-steroid binding globulin (SHBG). All these components are highly interconnected with IR, ovarian hormones, adipose tissue distribution, and function [3, 82]. The role of peripheral androgen convention is the least investigated aspect of HA in PCOS. But this specific aspect is important in terms of the disproportionate severity of clinical and biochemical HA often observed both in NonObPCOS and obese PCOS. In NonObPCOS, HA symptoms and biochemical HA are sometimes disproportionate to IR and OD that demands differentiation with secondary polycystic changes of ovarian morphology or investigating other than ovarian dominating androgen excess sources. Moreover, HA may have different metabolic sequelae depending on its origination. Contemporary methods of steroid metabolome assessment may open a new page in understanding the wholesome picture of androgen excess in PCOS.

The dominance of adrenal androgen excess was reported in older studies [83, 84]. In 2015, Moran et al. paper A4 and DHEA-s levels were significantly higher in nonobese than in obese PCOS patients. A significant correlation between luteinizing hormone (LH) and A4 in nonobese PCOS patients was observed. The frequency of hyperandrogenism by increased A4, and DHEA along with DHEAs was significantly higher in NonObPCOS compared with high-BMI PCOS patients [85]. In a 2015 review paper by M. Goodarzi, E. Carmina and R. Azziz analyzing the issue of adrenal androgen precursors’ elevation etiology and role in PCOS conclude that inherited defects of steroidogenesis may be one of the causes and have to be further investigated; also there is the intrinsic exaggerated activity of hypothalamic-pituitary-adrenal axis, while extra-adrenal factors, such as IR, play a limited role in the adrenal androgen precursors excess of PCOS [86].

A new study addressed specifically the issue of androgen excess sources in obese and NonObPCOS using liquid chromatography-tandem mass spectrometry and genetic tests. Its results showed increased DHEA-s, 17-hydroxyprogesterone(17-OHP), 17-hydroxypregnenolone, and estrone (E1) levels in NonObPCOS compared with both the lean controls and the obese PCOS patients, while lower FAI was found in the lean PCOS patients compared with the obese PCOS patients. The correlation analysis showed that FAI was positively correlated with BMI and HOMA-IR, which is in line with previous studies [34, 53, 87]. Enzyme activity evaluation showed that NonObPCOS had increased activity of cytochromes P450c17, P450aro, 3β-hydroxysteroid dehydrogenase type 2 (3βHSD2) and decreased activity of P450c21. Higher frequencies of CYP21A2- (encoding P450c21) c.552 C > G (p. D184E) in NonObPCOS were found compared with obese patients. The limitation of this study was that the gene sequencing was performed only for those with HA [88].

Active androgens and androgen precursors seem to have different effects on metabolism [89]. This is clearly demonstrated in a new cross-sectional study of 823 women with PCOS 76.2% with biochemical HA and 23.8% with normal androgen levels. Anthropometric indexes were used to assess metabolic risk characteristics. In normoandrogenemic PCOS, FAI predicted significant abnormality in the visceral adipose index (VAI) and dehydroepiandrosterone (DHEA) predicted against alteration in β-cell function. In HA PCOS, FAI predicted derangements in waist TG index and lipid accumulation products. DHEA weakly predicted against VAI, DHEA-s tended to predict against the abdominal obesity index [90].

Thus, existing studies show that NonObPCOS women have different profiles of androgen excess sources. Evaluation of the prevailing source of androgen excess might be a valuable component of metabolic risk estimation since some types of androgens seem to have a protective role. Also, more studies on steroidogenic enzymes function and alternative steroidogenic adrenal and peripheral tissue pathways are needed to have the whole picture of NonObPCOS pathogenesis.

5. Specifics of glucose and lipid metabolism in NonObPCOS

Inconsistencies in data on IR incidence in NonObPCOS can be explained by prevalent usage of indirect and not enough accurate methods of IR assessment. Indexes, such as HOMA-IR and others, have a good positive predictive value, but a poor negative predictive value [34, 91]. First studies proving the presence of hyperinsulinemia and IR in NonObPCOS with the use of gold standard method hyperinsulinemic euglycemic clamp were published in the nineties [92, 93]. The presence of unique disorder of insulin action was hypothesized. More recent studies with larger groups and more sophisticated methods supported these findings [94]. A study by Stepto et al. showed that the prevalence of IR in PCOS is 75% in NonObPCOS, 62% in overweight controls, and 95% in overweight PCOS [95]. In 2016, meta-analysis of premenopausal women diagnosed with PCOS compared with a control group for insulin sensitivity, measured by euglycaemic–hyperinsulinaemic clamp, NonObPCOS, and overweight PCOS compared with their respective controls had lower insulin sensitivity with large and very large magnitudes [96]. In a recent study by Tosi, evaluation of insulin action on glucose and lipid oxidation, nonoxidative glucose metabolism, and serum FFA in different PCOS phenotypes was performed. Results of this study showed that irrespective of phenotype, PCOS women had impaired insulin-mediated substrate use influenced by T levels [97].

Thus, studies using the gold standard method of insulin sensitivity assessment demonstrate the presence of hyperinsulinemia and IR in NonObPCOS while estimated indexes are often not enough sensitive to detect mild IR in fasting state. At the same time, it is necessary to take into account some limitations of clamp tests apart from the technical complexity and high price. In the case of intravenous glucose administration, intestinal factors (glucagon-like peptide (GLP1), glucose-dependent insulinotropic peptide (GIP)) are not involved. Thus, being a gold standard for IR evaluation clamp tests detects glucose uptake in specific conditions that are quite different from physiological. At the same time, recent investigations in TDM pay much attention to the role of postprandial dysmetabolism including postprandial dysglycemia and dyslipidemia [98]. Studies on postprandial dysglycemia in NonObPCOS are very limited. One study with obese PCOS women showed that area under curve (AUC) for TG, insulin, and glucose were higher compared to obese controls while AUC for high-density lipoproteins (HDL) was lower after meal after adjustment for BMI and HOMA-IR [99]. In a study including also NonObPCOS HOMA-IR and AUC for glucose, TG, very low-density lipoproteins, and total cholesterol were higher in PCOS compared to BMI-matched controls [100]. Thus, clinical assessment of postprandial dysglycemia could be a valuable tool for glucose metabolism impairments early detection in NonObPCOS. Well known tool for this purpose is the oral glucose tolerance test with 75 g of glucose hardly can be done often. For this reason, emerging methods like self-monitoring blood glucose and continuous glucose monitoring as well as biomarkers are very much awaited to be approved for routine use in PCOS. Standardized methods of postprandial dyslipidemia are not yet available. In terms of postprandial dysglycemia data on patients eating habits are of great importance, especially on the frequency of food intake.

Described above altered body composition as well as AT and skeletal muscles physiology combined with HA results not only in IR but in high rates of dyslipidemia in NonObPCOS. In meta-analysis elevated prevalence of high-TG and low-HDL were shown NonObPCOS (for high-TG: OR 10.46; 95% CI 1.39–78.56; for low-HDL: OR 4.03; 95% CI 1.26–12.9) [15]. Thus, laboratory monitoring for dyslipidemia which by some authors is regarded as an IR marker is warranted for all NonObPCOS patients [101].

Liver is an active participant in glucose, lipid, steroid, and protein metabolism. NAFLD is a clinical disease characterized by the histologic finding of ≥5% macrovesicular steatosis of the hepatocytes in individuals with nonsignificant alcohol consumption or other known cause of chronic liver disease [102]. Traditionally NAFLD was attributed to overt diabetes and obesity. Studies on metabolic obesity in general and specifically on NonObPCOS changed this view. 2018 meta-analysis shows that compared to the control group, the risk of NAFLD in the PCOS group was higher (OR = 2.25, 95% CI = 1.95–2.60). When stratified by BMI frequency of NAFLD risk was significantly higher in both obese subjects (OR = 3.01, 95% CI = 1.88–4.82) and non-obese subjects (OR = 2.07, 95% CI = 1.12–3.85). In addition, PCOS patients with HA had a significantly higher risk of NAFLD, compared with controls (OR = 3.31, 95% CI = 2.58–4.24) [103]. Some studies do not demonstrate such a strong association with HA [104].

Overall pathogenesis of NAFLD includes not only IR but a complex of factors: altered energy balance, adipose tissue excess, hormonal changes, genetic factors [105]. As the liver secretes proteins, metabolites, and hepatokines to influence metabolism in other tissues presence of NAFLD in PCOS exacerbates all major and minor pathological circuits of the syndrome. Most vivid example is low SSBG secreted by the liver leading to higher levels of free T and HA symptoms. Thus, it is logical to detect NAFLD in NonObPCOS regarding epidemiological data and close pathophysiological associations of NonObPCOS features and NAFLD. Though screening for NAFLD is very restricted by currant guidelines on this pathology [102]. Liver biopsy remains a gold standard for diagnosis of NAFLD, but diagnostics ultrasound and transient elastography proved to be effective for noninvasive diagnosis [106].

Thus, there are many unsolved clinical issues of detection crucial alterations in NonObPCOS like mild IR, postprandial dysglycemia, and NAFLD. But still regarding existing scientific data search of solutions of these issues are among primary goals on the way to more effective NonObPCOS management.

6. Management of NonObPCOS

There is not enough evidence to make specific prevention recommendations for NonObPCOS women since most of the studies on lifestyle modification included either overweight/obese or mixed populations. Taking into account all the above-mentioned data on specifics of NonObPCOS physiology it is reasonable to educate patients on the risks of early dyslipidemia, NAFLD, and metabolic syndrome. It is reasonable to monitor these conditions on a regular basis and to raise patient’s awareness of the importance of healthy lifestyle especially eating behavior including food frequency, respect of circadian rhythms as well as food characteristics [107]. These recommendations remain actual for NonObPCOS women taking combined hormonal contraceptives. Until evidence-based specific dietary recommendations become available women with NonObPCOS can be recommended to keep Mediterranean diet principles as it proved to be protective from cardiometabolic risks in different populations including PCOS [108]. Also, control of fructose intake is highly recommended in view of NAFLD risks [109]. In the absence of definitive data on types of exercise favorable for muscle metabolism of NonObPCOS general recommendations from 2018 guidelines should be translated to every patient [1]. The arrival of new diagnostic methods for steroid metabolome, different metabolites (ceramides, bile acids, fatty acids, etc.), and gut microbiota assessment are promising in reaching targeted approaches for symptoms relief and MS prevention in NonObPCOS [110]. A number of pharmacological agents are promising for affecting main pathogenic mechanisms of NonObPCOS (insulin signaling defects, mitochondrial dysfunction, oxidative stress) and their consequences (IR, hyperinsulinemia, postprandial dysglycemia, ovarian dysfunction, dyslipidemia, NAFLD) including nutritional products and complex synthetic molecules. Among them are vitamin D, inositols, quercetin, resveratrol, L-carnitine, thiazolidinediones, GLP-1 receptor agonists, antihyperlipidemic drugs [111, 112, 113, 114, 115, 116, 117, 118, 119]. But all these groups of medications need an evaluation of their efficacy in properly designed clinical trials.

7. Conclusion

The prevalence of NonObPCOS is probably underscored. In the absence of high BMI unrecognized metabolic risks lead to their delayed diagnosis and interventions in NonObPCOS. Existing data on NonObPCOS suggests that intrinsic alterations in adipose and muscle tissue function might be the starting point of key pathogenic factor – IR and consequent hormonal and metabolic derangements. There is a need for deeper investigation and improvement of diagnostic approaches to mild IR, steatohepatosis, and androgen excess sources for better management of NonObPCOS.

Acknowledgments

We would like to express our sincere gratitude to organizations, that housed our research and practical work on hyperandrogenism and supported us in writing and publishing this chapter: Ukrainian Association of Endocrine Gynecology, National Academy of Medical Sciences, Institute of Pediatrics, Obstetrics and Gynecology and Centre of Innovative Medical Technologies. Taking into account historical conditions in which completion of this text took place we express deepest gratitude to Ukrainian government and all people who have been heroically opposing Russian war attack.