Awareness About The Alarming Increases

Published Date: 02 Nov 2017

All the underlying disarray in the metabolic parameters has been well documented to be affected by the endocrine disturbances in the hypothalamo-pituitary-ovarian axis, either directly, or in addition to their effect on body composition and body fat redistribution (Vitale et al 2010, Mercuro et al 2010, Davis et al 2012). These endocrine disturbances peak during the menopausal transition and increase the risk of menopausal women of becoming prey to shifts in cardio-metabolic equilibrium and in addition cause a range of disturbances known as the menopausal syndrome.

In the present study, the prevalence of menopausal symptoms was around 29% of vasomotor symptoms, 22% of somatic symptoms, 20% of psychological symptoms and 17% urogenital symptoms. It was observed that the women attending the health check up facility had higher prevalence of menopausal symptoms than the free-living population. Corroborating these findings, Singh (2012) also reported a higher prevalence of menopausal symptoms (75% vasomotor, 62% somatic, 32% psychological and 15.5% urogenital) in women attending a clinic for check up. Apart from this, several Indian authors have also reported that while comparing the relative prevalence of menopausal symptoms, it is the vasomotor symptoms that are more prevalent than somatic, psychological or urogenital symptoms (Kaur, Walia and Singh 2004; Sharma, Tandon and Mahajan 2007; Kakkar et al 2007; Bairy et al 2009; Kapur Sinha and Pereira 2009). Alternatively, Foo-Hoe (2007) reports that in Aisan women the most prevalent symptom is joint pain, and amongst Asians, Indian women are more prone to experiencing symptoms than far eastern women, namely the Chinese, Japanese, Koreans and Philipino women.

Aside from the prevalence, vasomotor symptoms are the ones that form the main grounds for prescribing hormone therapy to menopausal women by physicians (Meherishi et al 2010). Vasomotor symptoms in Nepalese women were reported to be as high as 69.7% (Chunni and Sreeramareddy 2011), 35.8% in Bangladesh (Rahman, Salehin and Iqbal 2011), 41.6% in Malaysia (Rahman, Zainudin and Mun 2010) and the highest reported has been in 95.2% in Turkey (Ayranci et al 2010). The effects vasomotor symptoms exert on the biological systems have found to be highly deleterious. They are associated with significantly higher coronary artery calcification and development of atheromatous plaques (Allison et al 2010); greater subclinical cardiovascular disease including poorer endothelial function, that includes increased aortic calcification (Thurston et al 2008), as well as higher intima media thickness (Thurston et al 2011) and altered inflammatory processes (Yasui et al 2006, Bechlioulis et al 2010).

One particular metabolic disorder that aggravates around menopause is decline in the bone mineral density, especially around the hip bone and the hip-thigh bone joint (femoral neck). The present study found the prevalence of osteoporosis to be 11.9% and osteopenia to be 55%, thus a prevalence of low bone mass of 67%, indicating that more than half of the women studied had suboptimal bone mass, which is a cause of grave concern. Aggarwala et al (2011) have also reported a low bone mass prevalence of 53% in peri and post menopausal women in North India, whereas Unni, Garg and Pawar (2010) reported a low bone mass prevalence of 45.7% in women above 40 years, irrespective of menopausal status. Studies on lower income women have reported the prevalence of osteoporosis to be as high as 28% (Shatrugna et al 2005), while some studies report a prevalence as high as 50% in post menopausal women in south India (Paul et al 2008).

In the present study, the highest prevalence of low bone mass was found in peri menopausal women (87%), than in pre and post menopausal women (65% and 62% respectively), however, the difference in the prevalence was not statistically significant, suggesting the fact that decline in bone mass, once assumed to be aggravated during the onset of menopause, is no longer confined to the post menopausal category, but affects all the women equally and needs universal strategies for managements across all menopausal stages. The main determinants of bone mass in that have been reported in Indian women are BMI (Paul et al 2008, Kumar et al 2010, Kadam et al 2010), physical activity (Kumar et al 2010, Aggarwal et al 2011), calcium intake (Keramat et al 2008, Kadam et al 2010, Kumar et al 2010, Aggarwal et al 2011). Except BMI, Indian women fare poorly on most of these parameters, especially calcium intake (Keramat et al 2008, Paul et al 2008), which has been identified as the major contributor of low bone mass across all menopausal stages.

It has been recently studied that menopause is associated with increased total body fat as well as increased abdominal fat, leading to increased incidence of obesity and abdominal obesity (Ghosh and Bhagat 2010, Davis et al 2012) . In the present study as well, the aggregate prevalence of obesity though very high (67.4%) the prevalence specific to post menopausal women (75%) was significantly higher (p<.0.05) than in pre menopausal women (53%). The Jaipur Heart Watch 5 (Gupta et al 2012), a large scale surveillance study of cardiovascular risk factors in India reported the prevalence of obesity in women to be 50%. Gupta et al (2012) in a large scale migration survey of 4608 individuals, reported a prevalence of 46.7% in urban women, while Ebrahim et al (2010) in a population based survey of 6510 participants, reported a prevalence of 53.5%. The analysis of results of the data on urban Indians of the large scale study Chennai urban rural epidemiology study/ CURES (Deepa et al 2009) on revealed a prevalence of 47% in women as compared to 43% in men.

Abdominal obesity was found to be the highest prevalent problem in the subjects studied, with a prevalence of elevated WSR of 91.8% and high WC prevalence of 90.5%. The prevalence was found to be higher in the free-living population (WC>80cm-93.5%, WSR>0.5 -95.7%) than in the population at the health check up clinic (WC>80cm – 87.5%, WSR>0.5 -91.8%). The analysis of the CURES urban arm (Deepa et al 2009) reported a prevalence of 56% in women (versus 35% in men, p<0.001). Khokhar, Kaur and Sidhu (2010) reported abdominal obesity (WC>80cm) to be 81% in middle aged women in Punjab, while Jyothi and Nayak (2010) report abdominal obesity to be 82.3% in working women in Karnataka.

Such high prevalence of obesity can be partly explained by the snacking patterns and lack of sufficient physical activity by the subjects. As high as 41% of the subjects consumed fried snacks more than once a week and almost 40% of then consumed sweets/bakery/confectionery items more than once a week. The fat intake of the subjects was way higher than the recommended daily intake (176% of the RDA), and for 78% of the subjects, more than 30% of the calories came from fat. Bhagat and Ghosh (2011), who studied the consumption of type of fats in rural Indian population, have reported that females with had significantly higher (p<0.001) SFA: MUFA and SFA: PUFA consumption than their male counterparts and had higher mean BMI than the males studied (22.1kg/m2 versus 21.9kg/m2 respectively). Udipi et al (2006) reported the fat consumption to be higher than the recommended allowance in 72% in Kolkata, 32 and in Ghaziabad. Conversely, the dietary intake surveys led by National Nutrition Monitoring Bureau (NNMB) in the 10 states of India have reported the visible fat intake to be in a range of 9–13 g only.

To add to this anomaly in the dietary patterns of the subjects in this study, the proportion of those who engaged in more than 3 hours of regular physical activity was less than one-third. Singh and Pella (2007) have reported the prevalence of sedentary behavior as 59.3% among women, in a cross sectional population survey of 6940 individuals across 5 cities: Moradabad, Trivandrum, Kolkatta, Mumbai and Nagpur. Authors also reported a significant increasing trend in sedentary behavior in women after the age of 35-44 years. Sedentary behavior was significantly (P < 0.05) greater in Trivandrum, Calcutta, and Bombay compared to Nagpur and Moradabad.

In addition to the above discussed lifestyle factors, Indians are inherently predisposed to obesity on account of their body composition which is characteristically higher in body fat and lower in muscularity. For the same BMI, Asian Indian men have reported to have 7-8% points higher % body fat compared to Caucasians, while Pacific Islanders have 4% points % body fat compared to Caucasians (Rush et al 2004). Compared to European men for the same %BF, BMI was 2-3 units higher for Pacific Islanders and 3-6 units lower for Asian Indian. In addition to percent body fat, it has also been found that Indians have larger stores of metabolically active central adipose fat depot than peripheral adipose tissue, as compared to Caucasians and other races (Chowdhury, Lantz and Sjostrom 1996, Sniderman et al 2007, Rush et al 2004), which is a predisposing factor and partially the explanation of higher prevalence of metabolic anomalies in south east Asians.

This trait if body composition in southeast Asians has links to various polymorphisms associated with multiple genes that are involved in lipid metabolism and storage. Bhatt and co-workers (2012) have reported polymorphisms in Myostatin gene to be associated with increased abdominal fat mass and less of lean body mass in Asian Indians. Radha et al (2007) have reported single gene polymorphisms in lipoprotein lipase gene may be implicated in predisposing Indians to obesity. In addition, a Q223R SNP in the leptin receptor gene has also been found to be associated with obesity in Indians (Murugesan 2012). In the present study too, the prevalence of aberrations like hypertension, impaired glucose metabolism and lipid metabolism and clustering of all these aberrations was disturbingly high, as is discussed below.

Hypertension

The infamous silent killer that is responsible for 57% of all types of deaths related to stroke and almost one-fouth of all deaths related to cardiovascular events, stands at 48.2% in urban women in India, as reported in the most recent large scale survey involving 4,608 women in Delhi, Haryana, Jaipur, Pune, Kolkata, Kochi and Gandhigram (Gupta et al 2012), implicating that every other woman in urban India suffers from hypertension. The present study estimated the prevalence of hypertension to be almost the same: 48.3% which included the existing cases and the ones that were diagnosed during the study. The Jaipur Heart Watch 5 study however reported a prevalence of 24.6%, which is almost half of found in this study. Another large sale survey of 88,653 individuals from Mumbai, reported a prevalence of 48.4% of hypertension in women, and the mean SBP was higher in women in all age groups compared to men (Gupta P, Gupta R and Pednekar M 2004).

Major determinants of hypertension that have been documented include age, height, weight, BMI, low education, female gender, smoking, diabetes and high fat diet (Gupta P, Gupta R and Pendse M 2004; Devi et al 2012, Gupta et al 2012). Other predictors include gene polymorphisms namely mineralocorticoid receptor gene (Sia et al 2012), β 1 adrenoreceptor (Kong et al 2012), methylene tetrahydrofolate reductase (Alghasham et al 2012), inducible nitric oxide synthase (Oliveira-Paula et al 2012), serine-threonine kinase (Maatta et al 2012), angiotensin II receptor (Katsuya and Morishita 2012), PPAR α (Ding et al 2012) to name a few. The main determinants of hypertension in the present study were identified to be almost the same as above: age>40, no college education, overweight/obesity, abdominal obesity, high blood sugar and high levels of atherogenic index of plasma (AIP>0.21); nonetheless an additional determinant turned out to be post-menopausal status, which was the strongest predictor of hypertension in the study (crude OR 4.6, age adjusted OR 2.0). A number of studies disputing the role of menopause in precipitating hypertension have been reported (van Beresteyn, van t Hof, De Waard 1989; Casiglia et al 1996; Luoto et al 2000) which state that the effect of age cannot be ruled out while considering the strength of association of menopause and hypertension. However, even after adjusting for age, the odds ratio of menopause and hypertension was still significant (p<0.01) in the present study.

The major effects of menopause on hypertension have been attributed to vasomotor symptoms. Hot flashes have been associated with increased SBP even after controlling for age, race, ethnicity, BMI and even menopausal status (Gerber et al 2007, Gallicchio et al 2010, Sadeghi et al 2011). Part explanation of this association can be attributed to relation of both to central sympathetic activity. During the bouts of hot flalshes, the peripheral vasoconstriction and increased cardiac output, both caused by baroreflex dysfunction, might also have been responsible for accompanying increments in SBP. It has been reported (by Hart, Charkoudian and Miller 2011) that sex hormones interact to modulate neuroeffector mechanisms including integrated regulation of the Sry gene and direct effect of sex steroid hormones on synthesis, release and disposition of monoamine neurotransmitters, and that distribution and sensitivity of their receptors in brain areas associated with autonomic control. Thus, timely management of vasomotor symptoms appears relevant to control the blood pressure so that the person is not rendered a hypertensive by the end of the menopausal transition.

Diabetes

The metabolic anomaly that has many a researchers and treatment providers still searching for answers is diabetes, which not only continues to soar in India but the secondary complications of which also prey on a considerable proportion of those affected. The 2012 update by International Diabetes Federation reveals that the burden of diabetes in India is 63 million diabetics. The latest analysis of CURES study where the researchers assessed the comparative prevalence of diabetes by FBS and by glycated Hb in 2188 individuals, reported the prevalence of diabetes to be 6.1%, excluding the existing diabetic cases (Nazir et al 2012). The same prevalence in the present study came out to be 5.5%. Another recent survey on 1178 individuals from the East Indian population (Prasad et al 2012) revealed a prevalence of 15.7%, which is three times the prevalence found in the present study. The Jaipur Heart Watch 5 has reported a prevalence of 10.8% diabetes in urban women, which is twice the prevalence found in the present study. The prevalence of insulin resistance in the study was found to be 25.7%, which was considerably higher in the population attending the health check up section (31.7%), compared to the free living population (21%). However, the prevalence of diabetes was found to be higher in the free living population (6.4%), compared to the subjects attending the health check-up (4.7%). Peterson et al (2006) reported insulin resistance across various ethnic groups using the Insulin sensitivity index (ISI); among them, it was found that Asian Indians had significantly higher insulin resistance (59%) compared to Blacks (33%), Eastern Asians (30%), Caucasians (20%) and Hispanic (18%). Kumar et al (2005) reported the prevalence of insulin resistance in north Indian population using HOMA, to be 12.8%; the same prevalence according to ISI was 37.8%. The authors also reported that ISI was found to as promising an indicator as HOMA, on account of it poor specificity. The Chennai Urban Population Study (CUPS) conducted on 1262 adult subjects from two residential areas in Chennai, found a prevalence of 11.2% according to HOMA (Deepa et al 2002). Meng Khoo et al (2011) reported a comparative account of insulin resistance across Asians, where they found that Asian Indians had highest mean HOMA levels (3.18 ± 3.18), compared to Chinese (1.58 ± 1.43) and the Malays (2.28 ± 1.84), the difference being highly significant (P<0.001).

Major determinants of diabetes identified in several studies are family history, age, male gender, BMI, WHR, blood pressure, serum cholesterol levels (Rahman, Rahim and Nahar 2007; Ramachandran et al 2001, Ajay et al 2008, Ravikumar et al 2011, Bharati et al 2011). The genetic factors that play a role in making south Asians susceptible to diabetes include body composition and central adiposity. Wannamethe et al (2010) in a 7 year prospective study, showed that adiposity measures including BMI, WC and WHR were significant predictors of diabetes and insulin resistance in women, with significant adjusted relative risks being 4.10 (95% CI 2.16–7.79), 12.18 (95% CI 4.83–30.74) and 5.61 (95% CI 2.84–11.09) for BMI, WC and WHR, respectively. In the present study itself, anthropometric parameters were found to have significant correlations with blood sugar values (WC-0.12*, WSR-0.14*), in addition to it, the percent prevalence of high FBS showed a significant trend of increase when analyzed across quintiles of WSR, WC and BMI. However, anthropometric parameters have greater significant association with insulin values and insulin resistance than FBS values itself (correlation between Insulin and WC - 0.35***, Insulin and WSR - 0.34***, HOMA2 and WC - 0.36***, HOMA2 and WSR – 0.34***). Visceral fat depot has been found to be associated with insulin resistance. Increased secretion of free fatty acids, inflammatory cytokines and decreased secretion of adiponectin are molecules mediating obesity and insulin resistance [18,19 ]. Few studies have directly addressed the relationship between waist circumference and insulin resistance or hyperinsulinemia [20,21]. A small cross-sectional study reported a linear increase in the prevalence of hyperinsulinemia across the deciles of waist circumference in 185 healthy men in Canada [20]. In a cross-sectional study of 2746 volunteers aged 18–72 years, including 798 men, waist circumference was strongly correlated with HOMA-IR.

Partial explanation for this association of abdominal obesity can be the characteristic elevated levels of circulating plasma free fatty acids/ FFAs (Koutsar and Jensen 2006). In normal situations,, insulin is able to suppress the intracellular adipocyte lipolysis through the hormone sensitive lipase, and at the same time stimulates intravascular adipose tissue lipoprotein lipase (LPL) for enhancement of circulating triglycerides hydrolysis and subsequent capture and storage of the fatty after a meal (Berger and Bernard 1999, Coppack, Jensen and Miles 1994, Lewis et al 2002). Thus, a diminished ability for re-esterification of fatty acids seems be an one of the important mechanisms of excess availability of FFAs in abdominal obesity. Peripheral insulin resistance is thus a consequence of elevated circulating

FFAs (Groop et al 1991; Riemens, Sluiter and Dullaart 2000; Heilbronn, Smith and Ravussin 2004). Similarly, visceral adipose tissue is specifically responsive to lipolytic stimuli, which creates the potential for an elevated FFA delivery to the liver. As a result, there is minimal or no suppression of hepatic glucose production, while the hepatic insulin clearance is not inhibited either (Golay et al 1987, Boden et al 2002).

Another major predictor of diabetes in the present study came out to be post menopausal status (crude odds ratio 5.4, 95% CI: 1.7 – 19.1, p<0.001), which was observed consistently in both the free living population and the subjects attending health check up facility. Several animal model molecular studies have demonstrated the beneficial effect of estrogen in insulin sensitivity and euglycemia. Insulin-stimulated glucose uptake in skeletal muscle, mediated by the glucose transporter isoform GLUT4, is suppressed in the absence of estrogen receptor/ ERα (Bryzgalova et al 2006). In addition, ERβ acts as an inhibitor of PPARγ activity, a major inhibitory regulator of glucose and lipid metabolism (Foryst-Ludwig et al., 2008). Estrogens have been found to increase hepatic insulin sensitivity by decreasing gluconeogenesis and glycogenolysis (Ahmed-Sorour and Bailey, 1981), and increasing insulin release in islets of Langerhans (Alonso-Magdalena et al., 2008).

Apart from the non-modifiable risk factors and body composition, the major modifiable determinants of diabetes identified in several studies include, blood pressure, serum cholesterol levels (Rahman, Rahim and Nahar 2007; Ramachandran et al 2001, Ajay et al 2008, Ravikumar et al 2011, Bharati et al 2011). A recent review has reported several dietary factors associated with insulin resistance among South Asians, such as higher intakes of carbohydrate, saturated fatty acids, trans-fatty acids and n-6 PUFA, and lower intakes of n-3 PUFA and fiber, hence the Asian diet may be an important contributory factor for the high disease prevalence [Riccardi, Giacco and Rivellese 2004].

Thus, given the significant ability of abdominal abesity to predict and precipitate insulin resistance and eventually diabetes, it is of utmost importance that Southeast Asians control the amount of buildup of belly fat as a preventive measure

Dyslipidemia

The most pressing problem identified in the present study came out to be dyslipidemia, with the prevalence of high TC being 33.8%, elevated TAG being 17%, decreased HDL being as high as 47.3% and the most prevalent problem being elevated LDL which was found in, more than 65% of the subjects. The difference in the free living subjects in the study and the subjects attending the health check up was that the prevalence of low HDL was higher in the free living (54.8% in free living versus 39% health check-up) and the prevalence of high LDL was higher in health check up subjects (72.9% versus 57.5%) Various studies have looked at dyslipidemia as one of the risk factors while studying metabolic syndrome, diabetes and cardio vascular disease, however, the focus remains on the former conditions, even as lipid aberrations continues to be the most prevalent. A recent large scale survey on 6198 individuals across 11 cities in India reported the aggregate prevalence of high total cholesterol levels to be 25.3% among women (Gupta et al 2012), which was lower than that found in men (24.8%), even though marginally so. Yet another large scale comparative study on the rural and urban divide conducted on 4624 women reported the prevalence of high TC to be 27.7% in urban women, compared to a significantly lower 13.5% in the rural counterparts (Pandey et al 2011). The Jaipur Heart Watch 5 (Gupta et al 2012) reported the recent prevalence of high TC to be 32.7% in women and low HDL to be 25.1%.

The trend that the authors have observed over a 20 year period through a series of successive Jaipur Heart Watch studies (Gupta et al 2012) indicates that there was an increasing trend in prevalence of high TC, TAG and high non-HDL (ptrend <0.001). Global trends in serum cholesterol (Farzadfar et al 2011) indicated that in 2008, age-standardized mean total cholesterol worldwide was 4•64 mmol/L (95% uncertainty interval 4•51-4•76) for men and 4•76 mmol/L (4•62-4•91) for women. These values did not change much globally between 1980 and 2008, falling by less than 0•1 mmol/L per decade in men and women. Total cholesterol fell in the high-income region consisting of Australasia, North America, and western Europe, and in central and eastern Europe; the regional declines were about 0•2 mmol/L per decade for both sexes. The mean total cholesterol increased in only in the south east and East Asia and Pacific by 0•08 mmol/L per decade (-0•06 to 0•22, posterior probability=0•86) in men and 0•09 mmol/L per decade (-0•07 to 0•26, posterior probability=0•86) in women. This steep increase in the dyslipidemia in the populations may be the one of the pivotal causes of rise in metabolic anomalies in the population.

The determinants of hyperlipidemia have been reported in population of South Western China, where age, alcohol consumption, a preference for meat and animal products, regular dining out, and BMI were found to be the main determinants of hyperlipidemia in women, while high prevalence of salt intake was associated with hyperlipidemia in men (Deng et al 2012). A study on determinants of hyperlipidemia in Turkish populations (Erem et al 2008) reported that dyslipidemia was significantly associated with age, BMI, WC (except for TC and LDL-C), hypertension (only for LDL-C and TG), FBS (only for LDL-C and TG), education level, cigarette smoking (only for HDL-C and TC/HDL-C ratio), alcohol consumption (except for HDL-C and TC/HDL-C ratio), occupation (especially housewives), marital status (widows and widowers), and a family history (for only TC). Another study reported the effect of intake of SFA intake on the serum lipids in two APOE polymorphism genotypes- rs429358 and rs7412 (Petkeviciene et al 2012). The findings indicated that age, genotype APOE2 (rs7412), SFA intake, and body mass index (BMI) were significant determinants of TC and LDL-C level (with p values ranging from 0.043 to 0.001) as assessed by multivariate linear regression analysis.

Thus, it is evident that the rise in hyperlipidemia is quite steep and needs to be contained through whatever modifiable determinants that affect it.

Metabolic Syndrome

The condition representing utmost risk with regard to cardio-metabolic health, is the metabolic syndrome (MS), which is cause of concern worldwide on account of its rapidly acceleration among all populations. The findings in the present study indicated, much to the dismay that more than one third of the subjects suffered from metabolic syndrome (35.8%), with the prevalence being higher in the subjects from the free living population (41.9%), as compared to the subjects attending the health check up facility who had a prevalence of 30.5%. A city based survey by on 548 subjects Samant et al (2011) in Mumbai, revealed that the prevalence of metabolic syndrome in the women to be 12.5%, which is less than half of that found in this study. Another large scale survey by Prasad et al (2012) on East Indian urban population reported the prevalence of metabolic syndrome in females to be 42.3% in females, compared to 24.9% in males. A yet another survey from Mumbai (Pandey et al 2010) conducted on 498 women reported the prevalence of metabolic syndrome to be 55% in post menopausal women and 45% in premenopausal women, which higher than the prevalence found in this study. Rampal et al (2012) studied the comparative prevalence of metabolic syndrome across Asian populations settled in Malaysia. The findings revealed an prevalence of 27.5% in the mixed group, with prevalence being least in the Chinese and highest among Indians, with the prevalence ratios compared to Malays being 1.25 for Indians and 0.86 for Chinese and 0.94 for the indigenous Sarawakians. Sinha et al (2012) reported the prevalence in 300 women from south Delhi to be 29.6%.

Older age, female gender, general obesity, inadequate fruit intake, BP, HbA1c, hypercholesterolemia, and middle-to-high socioeconomic status significantly contributed to increased risk of metabolic syndrome (Pandey et al 2010; Das, Pal and Ghosh et al 2011;Prasad et al 2012; Sinha et al 2012). The present study found the main determinants of metabolic syndrome to be FBS, elevated lipid levels. In addition, fasting insulin levels and insulin resistance were also found to eb significant predictors (p<0.05 and p<0.001 respectively).