Prevalence of Metabolic Syndrome in Prediabetes

Abstract

Aims:

To study the prevalence of metabolic syndrome and its components in prediabetes and to compare the anthropometric indices [waist circumference (WC), hip circumference, waist–hip ratio, waist–height ratio] as predictors of metabolic syndrome in prediabetes.

Methods:

A total of 300 subjects (200 prediabetic cases and 100 matched normoglycemic controls) in the age group of 18–70 years were recruited for the study. Among the cases, there were 38% of males and 62% of females; and there were 33% of males and 67% of females in the control group. Prediabetes was diagnosed using the American Diabetic Association (ADA) criteria, and metabolic syndrome was diagnosed using the International Diabetes Federation criteria.

Results:

Prevalence of metabolic syndrome was 63% among the cases and 26% among the normoglycemic controls. Among cases, 56.5% of males and 66.9% of females, and among the controls, 36.3% of males and 20.9% of females had metabolic syndrome. The prevalence of metabolic syndrome increased with age and increasing body mass index among both the cases and controls. Central obesity was found to be the most common component of metabolic syndrome among prediabetic males (80.2%) and females (82.2%). The most common cluster of abnormality among the cases and controls was found to be increased WC plus low high-density lipoprotein plus increased triglycerides. Logistic regression analysis was applied to anthropometric indices, and it was found that increased WC had the maximum predictive power for metabolic syndrome among the subjects with prediabetes.

Conclusion:

Metabolic syndrome was more prevalent in the prediabetic population in comparison to the normoglycemic individuals with increased WC being the most prevalent component. Increased WC had the maximum predictive power for occurrence of metabolic syndrome among prediabetic subjects.

Introduction

Prediabetes is a state of impaired glucose homeostasis that precedes diabetes mellitus. According to the American Diabetic Association (ADA), prediabetes is defined as an impaired fasting glucose (IFG) if the fasting plasma glucose is in the range of 100 to 125 mg/dL; impaired glucose tolerance (IGT) if the 2-hr postprandial blood glucose is in the range of 140 to 199 mg/dL and hemoglobin A1c levels in the range of 5.7% to 6.4%.¹ The prevalence of prediabetes in India ranges from 6% to 14.7% according to the recent ICMR-INDIAB study.² The ADA expert panel suggests that around 70% of individuals with prediabetes will eventually develop diabetes with an annual conversion rate of 5%–10%.^3,4 Also, it is said that a similar proportion of subjects will revert back to normoglycemia annually if proper and timely interventions are taken. Hence, the identification and management of prediabetes is of paramount importance to determine the future burden of diabetes.

Metabolic syndrome refers to an assembly of risk factors of metabolic origin that lead to the development of type 2 diabetes mellitus and atherosclerotic cardiovascular diseases (ASCVD). Several organizations have formulated various definitions of metabolic syndrome over the last few years for its diagnosis and use in regular clinical practice.^5

–10 The International Diabetes Federation (IDF) estimates the prevalence of metabolic syndrome to be 20%–25% around the world, which varies according to region, extent of urbanization, lifestyle pattern, socioeconomic, cultural factors, and the definition of metabolic syndrome used.^11,12 As both develop in the setting of obesity and insulin resistance, a strong association is observed between prediabetes and metabolic syndrome. The prevalence of metabolic syndrome in prediabetes has not been studied in India. The primary aim of this study was to assess the prevalence of metabolic syndrome in prediabetes in an adult sample recruited from a medical center in India.

Materials and Methods

This was a cross-sectional study conducted at the Department of Endocrinology and Medicine Unit V of Pt. B.D. Sharma Postgraduate Institute of Medical Sciences (PGIMS), Rohtak, in which 200 diagnosed cases of prediabetes were recruited as cases and 100 normoglycemic subjects were recruited in the study as controls. Ethical clearance for the study was obtained from the Institutional Ethics Committee of Pt. B.D. Sharma PGIMS, Rohtak. A written consent was obtained from all participating subjects, and sociodemographic details regarding age, sex, education, occupation, socioeconomic status, physical activity, history of hypertension, type 2 diabetes, dyslipidemia, or any chronic illness were recorded in a printed questionnaire. Subjects having diabetes, cirrhosis of liver, chronic renal, pancreatic diseases, or any other severe illness and subjects taking lipid-lowering drugs, steroids, nicotinic acid, or other medications that cause dysglycemia were excluded from the study. The ADA criteria were used for diagnosis of prediabetes.¹ Fasting plasma glucose >100 and <126 mg/dL was taken as IFG and a value of postprandial plasma glucose level >140 and <200 mg/dL after oral glucose tolerance test was taken as IGT. Subjects were advised to remain overnight fasting (for at least a period of 8 hr), and venous blood samples were taken for assessment of their lipid profile. Serum lipid levels were estimated on principles of spectrophotometry¹³ using Randox Suzuka Random Access Autoanalyzer.

Height was measured by using a measuring tape in the subjects without shoes with their back against the wall, heel together, and the subjects were asked to look straight at the eye level. The vertical height of the marker from the ground was recorded. Weight was measured by a standard spring balance weighing machine. Subjects were made to stand straight on the machine platform without any support. Then, the weight was recorded in kilograms. Body mass index (BMI) was calculated using Quetelet's formula: BMI = weight in kilograms/(height in meters)². Waist circumference (WC) was measured at the midpoint between lower border of ribcage and upper border of iliac crest till the nearest centimeter with the subject wearing minimum clothes. Hip circumference was measured to the nearest centimeter at the level of greater trochanter. Blood pressure was measured in the right arm in sitting position after 5 min of rest, using standard adult mercury sphygmomanometer. Two readings were taken 5 min apart and the average of systolic and diastolic blood pressure was recorded.

For diagnosis of metabolic syndrome, the IDF criteria were used.⁹ This included central obesity defined as WC >90 cm for males and >80 cm for females plus any two of the following four criteria, that is, increased triglycerides (TG) (>150 mg/dL), low high-density lipoprotein (HDL) (<40 mg/dL for males and <50 mg/dL for females), hypertension defined as >130/85 mmHg, and fasting plasma glucose >100 mg/dL.

Results were analyzed using SPSS version 20.¹⁴ Continuous data were presented as mean ± standard deviation, and categorical data as count and percentage. All statistical assessments were two-tailed, and a P value of <0.05 was considered significant. Quantitative data were analyzed using Student's t-test, whereas qualitative data were analyzed by chi-squared test and Fisher's exact test. Multiple logistic regression analysis was performed to assess the contribution of various anthropometric and demographic factors as predictors of metabolic syndrome among the cases and controls.

Results

In this study, 200 prediabetic and 100 normoglycemic subjects were analyzed for the prevalence of metabolic syndrome. There were 76 (38%) males and 124 (62%) females in the prediabetes group and 33 (33%) males and 67 (67%) females in the control group. The baseline demographic and anthropometric profiles of study population are summarized in Table 1. Subjects with prediabetes were found to have significantly higher fasting plasma glucose, postprandial plasma glucose, diastolic blood pressure, weight, BMI, WC, waist–hip ratio (WHR), and waist–height ratio (WHtR). However, age, systolic blood pressure, and height were comparable between the two groups.

Table 1.

Comparison of the Baseline Demographic and Anthropometric Characteristics Among Cases (Prediabetic Subjects) and Controls (Normoglycemic Subjects)

Variable	Cases	Controls	P
Age (years)	42.14 ± 11.77	41.54 ± 11.64	0.672
Systolic blood pressure (mmHg)	123.53 ± 10.85	122.35 ± 10.84	0.371
Diastolic blood pressure (mmHg)	79.81 ± 8.04	77.75 ± 8.60	0.040
Fasting plasma glucose (mg/dL)	102.25 ± 6.45	85.71 ± 6.34	<0.001
Postprandial plasma glucose (mg/dL)	143.03 ± 18.02	107.19 ± 12.32	<0.001
Height (cm)
Male	169.48 + 6.27	168.97 ± 5.41	0.682
Female	160.01 ± 5.18	159.18 ± 4.83	0.277
Weight (kg)
Male	77.18 ± 11.43	71.21 ± 10.01	0.010
Female	64.95 ± 7.79	59.90 ± 6.99	0.001
Waist circumference (cm)
Male	94.43 ± 5.74	88.23 ± 7.52	0.001
Female	84.75 ± 7.33	78.62 ± 6.69	0.001
Hip circumference (cm)
Male	104.04 ± 5.12	98.94 ± 6.87	0.001
Female	95.97 ± 6.39	90.93 ± 6.25	0.001
Waist–hip ratio
Male	0.90 ± 0.021	0.88 ± 0.037	0.010
Female	0.87 ± 0.030	0.85 ± 0.032	0.001
Waist–height ratio
Male	0.56 ± 0.034	0.52 ± 0.043	0.001
Female	0.53 ± 0.049	0.49 ± 0.046	0.001
Body mass index
Male	26.72 ± 3.00	24.85 ± 2.85	0.003
Female	25.36 ± 2.89	23.85 ± 3.63	0.002

Metabolic syndrome was found in 126 of 200 subjects with prediabetes (63%) and in 26 of 100 controls (26%); the difference of which was significant (P < 0.001) and revealed that those with prediabetes were found to be almost two and half times more likely to have metabolic syndrome compared with euglycemic controls [odds ratio (OR) 2.37, 95% confidence interval (CI) 1.68–3.48]. Among the prediabetic subjects, metabolic syndrome was present in 43 (56.5%) males and 83 (66.9%) females. Whereas among the controls, metabolic syndrome was present in 12 (36.3%) males and 14 (20.9%) females. It was observed that the prevalence of metabolic syndrome increased with increasing age and BMI in both groups.

Among the subjects having prediabetes, the prevalence of metabolic syndrome was maximum among subjects having both IFG and IGT, followed by isolated IFG and isolated IGT, respectively (Table 2). The prevalence of increased WC, increased TG, and fasting plasma glucose was significantly higher among cases in comparison to the controls, but no significant difference was found in the prevalence of hypertension and low HDL among the groups (Table 3). On evaluating the prevalence of the cluster of components of metabolic syndrome, it was found that all five components of metabolic syndrome that is, increased WC, fasting plasma glucose, low HDL, high TG, and hypertension were present in 16 subjects among the prediabetic (5 males and 11 females) and in none of the controls. The most common cluster of components of metabolic syndrome observed among both groups was of increased WC plus low HDL plus high TG. Logistic regression analysis was carried out to evaluate the predictive power of various anthropometric parameters (WC, WHtR, WHR, and BMI) in determining the occurrence of metabolic syndrome among the subjects. It was observed that increased WC had the maximum predictive power for the subjects with prediabetes (area under ROC curve = 0.75) followed by increased WHtR (Table 4). Finally, when prediabetes was itself evaluated as a predictor of metabolic syndrome in comparison to the controls, a positive result was obtained (OR 2.68, 95% CI 1.41–3.88).

Table 2.

Prevalence of Metabolic Syndrome in Subjects with Impaired Fasting Glucose, Impaired Glucose Tolerance, and Subjects with Both Impaired Fasting Glucose and Impaired Glucose Tolerance

Variable	Mean fasting plasma glucose values (mg/dL)	Mean postprandial plasma glucose values (mg/dL)	Total no. of subjects	Subjects having metabolic syndrome
IFG	104.55 ± 3.00	127.01 ± 6.85	98	61 (62.2%)
IGT	94.29 ± 2.42	158.33 ± 9.63	62	36 (58%)
IFG+IGT	109.10 ± 4.35	158.58 ± 11.46	40	29 (72.5%)

IFG, impaired fasting glucose; IGT, impaired glucose tolerance.

Table 3.

Prevalence of the Various Components of Metabolic Syndrome Among the Cases (Prediabetic Subjects) and Controls (Normoglycemic Subjects)

Variable	Males			Females
Variable	Cases (76), %	Controls (33), %	P	Cases (124), %	Controls (67), %	P
Increased waist circumference (males ≥90 cm) (females >80 cm)	80.2	51.5	0.001	82.2	32.8	<0.001
Low HDL-C (males <40 mg/dL) (females <50 mg/dL)	36.8	30.3	0.204	55.6	49.2	0.202
Increased triglycerides (≥150 mg/dL)	68.26	56.5	0.018	53.6	32.8	<0.001
Hypertension (≥130/85 mmHg)	30.2	27.2	0.812	36.2	31.3	0.142
Fasting plasma glucose (≥100 mg/dL)	68.4	0	<0.001	69.3	0	<0.001

HDL-C, high-density lipoprotein cholesterol.

Table 4.

Predictive Power of Various Anthropometric Parameters for Metabolic Syndrome Among Subjects with Prediabetes

Anthropometric parameter	Area under the ROC curve	Standard error	95% confidence interval
Increased waist–hip ratio	0.642	0.0348	0.571–0.708
Body mass index	0.655	0.03	0.584–0.720
Increased waist–height ratio	0.735	0.0328	0.668–0.794
Increased waist circumference	0.75	0.0293	0.684–0.808

Discussion

Both prediabetes and metabolic syndrome are risk factors for type 2 diabetes mellitus and ASCVD. In this study, a comparison was performed between subjects with prediabetes (cases) with those having normoglycemia (controls), where metabolic syndrome was found in 63% of subjects with prediabetes, which was statistically significant in comparison to 26% prevalence among the controls. A similar study was performed in Karachi, Pakistan, where metabolic syndrome was found in 57.5% of subjects having prediabetes and 22.5% of subjects having normoglycemia, according to the NCEP ATP III criteria with a relative risk of 1.9 (95% CI 0.91–4.13).¹⁵ Another study performed in Cameroon showed that 56% of prediabetic women had metabolic syndrome in comparison to 23% of normoglycemic women according to the NCEP ATP III definition, with a relative risk of 2.43.¹⁶ A study in a European population found that IFG with metabolic syndrome has twice the prevalence than IFG alone.¹⁷

Metabolic syndrome was present in 56.5% of males and 66.9% of females among cases, and in 36.3% of males and 20.9% of females among controls. Women have a metabolically healthy obese phenotype compared with men, due to the higher amount of visceral fat in men. The onset of dysglycemia appears to diminish the favorable cluster of risk factors more in females compared with males, leading to greater risk of dyslipidemia, coagulation disorders, and inflammation between women with and without diabetes rather than in men.¹⁸ Also, it has been observed that women with diabetes are more obese than men with diabetes in most studies and show a stronger association between increase of BMI and diabetes risk.

The average age of cases in our study was much lower compared with estimates of Western counterparts¹⁹ since dysglycemia is observed to set in earlier among Indians compared with the Western population. The prevalence of metabolic syndrome in our study was seen to increase in subjects with increasing age, which shows a positive dose–response relationship between age and the prevalence of metabolic syndrome. Similar findings were also found in the study by Bhansali,²⁰ where increasing age was positively associated with increased risk of metabolic syndrome. This is because the prevalence of all the components of metabolic syndrome such as obesity, hypertension, dyslipidemia, and hyperglycemia has been shown to increase with age. Similar observations have been made in multiple studies from Europe, United States, and China, where it was found that the prevalence peaks around the age of 60–75 years, where after it decreases, likely to be explained by differential survival of those with and without the metabolic syndrome.²¹

In our study, metabolic syndrome was most prevalent among the subjects with IFG+IGT. IFG and IGT are considered as events on a continuous spectrum of disease process that eventually lead to diabetes mellitus. While IFG corresponds to the existence of hepatic insulin resistance and IGT is consistent with the presence of muscle insulin resistance, the presence of both IFG and IGT indicates an advanced level of dysglycemia and is also associated with a higher prevalence of metabolic risk factors compared with IFG or IGT alone.²²

Evaluation of the individual components of metabolic syndrome revealed that the most common abnormality among prediabetic subjects was increased WC. The most prevalent abnormality among the males of the control group was increased TG and among the females was low HDL levels. The findings were similar to some studies by Bhansali and coworkers.^20,23 The high prevalence of low HDL among the females can be attributed to the higher prevalence of central obesity among women compared with men, sedentary lifestyles of women, and the stringent criteria used for low HDL level in women.²³ The most common cluster of components of metabolic syndrome among both groups was found to be increased WC+low HDL+high TG. Cheal et al.²⁴ found that being overweight, in combination with high plasma TG and/or low HDL, was a powerful predictor of having metabolic syndrome. Similar findings were also observed in a study by Pradhan.²⁵ Occurrence of the various components of metabolic syndrome has been said to be only by chance, thereby fitting it into the definition of a syndrome. But the higher prevalence of a particular cluster of components signifies that an underlying mechanism may exist, which predisposes one set of clusters to occur more frequently than others. Several other studies also emphasize upon this uncanny association, but the cause and mechanisms remain to be established.^26
–28

Logistic regression analysis was applied, and adjusted ORs for the various anthropometric and demographic data were evaluated. In the study, increased WC was found to have the maximum predictive power for metabolic syndrome to occur among the prediabetic subjects. Since abdominal obesity is a risk factor for metabolic syndrome, anthropometric measurements such as WC have been seen to correlate more aptly with regional body fat distribution than BMI. This is more obvious in some racial groups, especially Asians in whom different cutoff values are used to assess increased WC. The WHtR has also been found to be an effective measure of central obesity, as the height of an individual affects the distribution of body fat.^29,30 As men are taller than women, they are expected to have higher values for WC. Hence, when adjustments for height are applied, WHtR becomes a measure of adiposity, which is comparable among men and women and the same cutoff value can be used for both genders.³¹

One of the strengths of the study is that it was a population-based study, thus making the results more generalizable. Also, the availability of a comparison group of people from the general population of the same area ensured better matching of unknown confounders. In our study, a systematic screening with IFG and IGT was carried out for diagnosis of prediabetes rather than medical records or anamnestic data. Finally, a wider age group range was included, that is, 18–70 years, and hence, the temporal trends could be better understood. Some limitations of the study were that it was regional and so the findings could not be extrapolated upon a larger population with regional variations. This was a cross-sectional study and did not allow us to draw any causal inferences. Finally, hemoglobin A1c was not included as a criterion for diagnosis of prediabetes as per the recent ADA guidelines.

To conclude, it was observed that the prevalence of metabolic syndrome was more among prediabetic subjects and was positively associated with age, BMI, and other anthropometric indices such as WC, HC, WHtR, and WHR. The maximum predictive value for the prevalence of metabolic syndrome among subjects with prediabetes was that of increased WC.

Footnotes

Author Disclosure Statement

No conflicting financial interests exist.

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