Study of metabolic syndrome indicators in newly diagnosed diabetes mellitus type 2 patients in Pakistani population

Abstract

Background

Metabolic syndrome (MetS) being a biological abnormality confers the highest risk of type 2 diabetes mellitus (T2DM).

Aim

To assess MetS indicators in the newly diagnosed T2DM Pakistani population.

Methods

A cross-sectional study (N = 123) with newly diagnosed T2DM patients (gender: both, Mean_Age: 49.24 ± 6.84 years) was selected from Medical OPD, Pakistan Institute of Medical Sciences, Islamabad. Basic profile, family history, and physical activity were recorded through a predesigned questionnaire, dietary intake through seven days Food Diary and Food Frequency Questionnaire. Blood pressure and selected clinical signs and symptoms were recorded. Anthropometric measurements included mid-upper arm circumference (MUAC), body mass index (BMI), waist circumference (WC), waist-and-hip ratio (WHR), waist-to-height ratio (WHtR), and conicity index. Lab parameters included fasting blood glucose, HbA1c, insulin levels, and lipid profile. Homeostatic model assessment of insulin resistance (HOMA-IR) was computed to assess insulin resistance.

Results

Almost all parameters related to MetS were higher than normal. The prevalence of MetS was 59.7%. BMI, MUAC, and WHtR were independently associated with HOMA-IR, but WC, WHR, and the conicity index had no relationship MetS indicators.

Conclusion

The prevalence rate of MetS was found to be 59.7% in newly diagnosed T2DM patients. MUAC was found to be a better parameter for the diagnosis of central obesity and insulin resistance in the selected population.

Keywords

Insulin resistance type 2 diabetes dyslipidemia metabolic syndrome HOMA-IR

Introduction

Metabolic syndrome (MetS) is a group of cardiometabolic risk factors. The main components of MetS include central obesity, insulin resistance, hyperglycemia, dyslipidemia, and hypertension (Aguilar-Salinas and Viveros-Ruiz, 2019). MetS increases the risk of type 2 diabetes mellitus (T2DM) (Aguilar-Salinas and Viveros-Ruiz, 2019; Piuri et al., 2021). Modification of MetS severity is associated with a concurrent reduction in the risk of T2DM.

In 2009, American Heart Association, with five other associations, arrived at a consensus for the definition of MetS, according to which MetS is diagnosed if a patient has any three of the following: elevated waist circumference (WC; population- and country-specific cutoff points), raised blood pressure (BP; systolic >130 and/or diastolic >85 mmHg or on drug treatment), raised fasting blood glucose (FBG; >100 mg/dL or on drug treatment), raised triglycerides (TG) level (>150 mg/dL or on drug treatment), lowered high-density lipoprotein (HDL) cholesterol (<40 mg/dL in men, and <50 mg/dL in women or on drug treatment) (Hoyas and Leon-Sanz, 2019).

European patients with T2DM were found to have 88.8% prevalence of MetS, and Chinese patients with diabetes had 68.1% prevalence of MetS (Li et al., 2019), but there is still a lack of studies to show the prevalence of MetS in South Asian populations with T2DM.

Methodology

This was a cross-sectional study, carried out in tertiary care hospital, Islamabad. The following formula was used (Pourhoseingholi et al., 2013):

Sample size = z^{2} \times p (1 - p) / E^{2} = (1.96)^{2} \times 0.114 (1 - 0.114) / (0.05)^{2} = 155

z = z-score of 1.96, when confidence level is set at 95%

p = Prevalence of 11.4% of Diabetes Mellitus in Pakistan (Sherin, 2015)

E = Margin of error = 5%

A randomly selected sample of 123 newly diagnosed T2DM patients was included in the study population, out of which 19 patients dropped out due to their personal reasons. Patients were selected from the Medical OPD, at the Diabetic clinic of the Pakistan Institute of Medical Sciences, after fulfilling all ethical considerations. Informed consent was taken from all the participants. These patients were diagnosed as diabetic on the basis of fasting plasma glucose (FPG) according to the criteria given by American Diabetes Association (2020). Inclusion criteria were newly diagnosed T2DM patients, willing to participate in the study, from both genders, within the age bracket of 40–65 years. Exclusion criteria were any other acute or chronic disease including hypertension and hypercholesterolemia. Patients already on the management of diabetes or its complications were also not part of the study.

A basic profile, family history of diabetes, and physical activity were noted through a predesigned questionnaire. Dietary intake was measured through seven (07) days Food Diary and Food Frequency Questionnaire. For physical activity level, patients were asked if they did any type of exercise. They were also asked to mention how much time they spent doing exercise. Clinical assessment included BP, along with other symptoms of diabetes. Anthropometric measurements included height and weight to compute body mass index (BMI), and WC and hip circumference to compute waist-and-hip ratio (WHR), and waist-to-height ratio (WHtR). Conicity index (CI) was also computed through the formula:

CI (Conicity Index) = \frac{Waist circumference (in m)}{0.109 \times \sqrt{\frac{Body weight (in kg)}{Height (in m)}}}

Mid-upper arm circumference (MUAC) was measured with a measuring tape. All these anthropometric measurements were done according to the standard international anthropometric procedures.

Lab parameters included FBG, HbA1c, insulin level, and lipid profile, which included total cholesterol, HDLs, low-density lipoproteins (LDLs), very LDLs (VLDL), and TG were conducted as per protocol, once in all patients. Homeostatic model assessment of insulin resistance (HOMA-IR) has been used as the substitute marker of insulin resistance, and was computed by the formula (Khawaja et al., 2018):

\begin{aligned} HOMA - IR \\ = \frac{Fasting plasma glucose (\frac{mmol}{L}) \times Fasting insulin (\frac{μ IU}{mL})}{22.5} \end{aligned}

Statistical analysis was done in SPSS version 20. Frequencies and percentages of all categories were calculated. Correlations between various continuous variables were calculated. Binary regression analysis was done among various variables.

Results

Of 104 participants who completed the study, 58 (55.8%) were males and 46 (44.2%) were females. The age range was 40–65 years. Of all, 81 (77.9%) of the subjects were educated at some level, and the rest of them were illiterate. Most of the patients were doing their own businesses, many were in a government job, and the rest were unemployed, retired, or homemakers. About three-fourths of the study population had a positive family history of T2DM (Table 1).

Table 1.

Characteristics of study population (N = 104).

Parameters	Frequency	Percent	Parameters	Frequency	Percent
Demographic data
Gender			Age
Male	58	55.8	40–45 years	36	34.6
Female	46	44.2	45–55	43	41.3
			55–65	25	24.0
Education			Profession
Illiterate	23	22.1	Unemployed/home-makers/retired	21	20.1
Literate	81	77.9	Govt job	35	33.7
			Entrepreneurship	48	46.2
Family history of T2DM
Yes	77	74.04
No	27	25.96
Lifestyle & dietary intake data
Roti intake			Rice intake
Never	0	0	Never	12	11.6
Daily	104	100	Daily	7	6.7
Weekly	0	0	Weekly	85	81.7
Fruits Consumption			Fruit portions/day consumption
Never	5	4.8	Never	46	44.2
Daily	29	27.9	Small	49	47.1
Weekly	41	39.4	Medium	6	5.8
Sometimes	29	27.9	Large	3	2.9
Vegetables Consumption			Vegetable portions/day consumption
Never	0	0	Never	0	0
Daily	87	83.7	Small	91	87.5
Weekly	17	16.3	Medium	10	9.6
Sometimes	0	0	Large	3	2.9
Sweet meats, bakery items, & drinks Intake			Physical activity
Never	16	15.3	No exercise	67	64.4
Daily	23	22.1	Walk	37	35.6
Weekly	41	39.4	Others	0	0
Sometimes	24	23
Clinical signs & symptoms
Systolic Blood pressure			Diastolic Blood pressure
<130 mmHg	41	39.4	<80 mmHg	74	71.2
≥130 mmHg	63	60.6	≥80 mmHg	30	28.8
Visual defect			Burning feet/tingling sensations
Yes	90	86.5	Yes	68	65
No	14	13.5	No	36	35
Fatigue/aches and pains in the body			Slow wound healing
Yes	88	86.5	Yes	5	4.8
No	16	13.5	No	99	95.2

All of the subjects were eating wheat chapatti daily, and more than 80% of the patients consumed rice on weekly basis only. Less than one-third of the patients ate fruits daily, most ate fruits weekly, and the rest ate occasionally. The majority was taking only small portions of fruits (Table 1). About 90% of the patients were eating vegetables on daily basis, but the majority were taking only a small portion of these vegetables (Table 1). Less than one-fourth of the patients consumed simple sugars (in the form of sweetmeats, bakery products, and fizzy drinks) daily, and most of them consumed it on weekly basis (Table 1). The majority of these patients had a sedentary lifestyle, and about one-third was doing walk and jogging. They were not involved in any other type of exercise (Table 1).

More than 50% of the patients in this group had systolic BP ≥ 130 mmHg, but three fourth of the patients had diastolic BP < 80 mmHg (Table 1). Visual defects were present in most patients, feet burning and/or tingling sensations in many, and fatigue and/or aches and pains were present in the majority of patients (Table 1). Only a few patients gave a history of slow wound healing (Table 1).

Mean BMI, WC, WHR, WHtR, MUAC, and CI were higher than normal levels in males (Table 2), but in females mean BMI, WC, WHtR, and MUAC were higher than normal, but WHR and CI were within the normal range. Mean FPG, HbA1c, insulin level, HOMA-IR, TG level, LDL, and VLDL were higher than normal in both genders, but HDL was lower than the normal limit in females, and at the lower limit in males (Table 2). Serum cholesterol was normal in both genders (Table 2).

Table 2.

Gender-wise distribution of all measures and criteria for MetS.

Measures	Mean	SD	Cutoff	Level	Frequency* (%percent)	%age of MetS criteria
Males: Criteria for MetS (N = 58)
WC (inches)	39.42	3.59	<37.15^a	↑	39 (67.2%)	58.6% patients had 4/5 criteria fulfilled.
FPG (mg/dL)	149.60	22.95	<101	↑	58 (100%)
Triglycerides (mg/dL)	194.78	129.34	<150	↑	33 (56.9%)
HDL (mg/dL)	40.02	11.40	>40	—	—
Systolic BP (mmHg)	132.8	16.15	<130	↑	40 (69%)
Males: Distribution of all other measures (N = 58)
BMI (kg/m²)	26.85	4.15	<23^b	↑	49 (84.5%)
WHR	0.99	0.06	<0.95^a	↑	48 (82.8%)
WHtR	0.58	0.06	<0.54^a	↑	48 (82.8%)
CI	1.36	0.07	<1.32^a	↑	43 (74.1%)
MUAC (cm)	30.5	2.4	<29^c	↑	43 (74.1%)
HbA1C (%)	7.82	1.23	<6.5	↑	49 (84.5%)
Insulin (µU/mL)	31.61	24.86	<13.85	↑	23 (39.7%)
HOMA-IR	11.5	9.11	<2.5	↑	54 (93.1%)
Cholesterol (mg/dL)	189.29	38.13	<200	—	23 (39.7%)
LDL (mg/dL)	112.07	40.15	<100	↑	39 (67.2%)
VLDL (mg/dL)	38.96	25.87	<30	↑	33 (56.9%)
Females: Criteria for MetS (N = 46)
WC (inches)	39.86	4.67	<37.59^a	↑	30 (65.2%)	60.8% patients had 4/5 criteria fulfilled.
FPG (mg/dL)	150.15	29.10	<101	↑	46 (100%)
Triglycerides (mg/dL)	191.50	101.97	<150	↑	29 (63%)
HDL (mg/dL)	38.61	12.14	>50	↓	35 (76.1%)
Systolic BP (mmHg)	127.6	14.9	<130	—	—
Females: Distribution of all other measures (N = 46)
BMI (kg/m²)	30.35	5.58	<23^b	↑	44 (95.7%)
WHR	0.97	0.08	<0.97^a	—	—
WHtR	0.65	0.07	<0.64^a	↑	21 (45.7%)
CI (F)	1.35	0.08	<1.39^a	—	—
MUAC (cm)	31.5	3.9	<28^c	↑	41 (89.1%)
HbA1C (%)	7.84	0.97	<6.5	↑	43 (93.5%)
Insulin (µU/mL)	24.75	22.77	<13.85	↓	12 (26.1%)
HOMA-IR	9.19	8.17	<2.5	↑	44 (95.7%)
Cholesterol (mg/dL)	190.41	33.31	<200	—	17 (37%)
LDL (mg/dL)	114.41	31.68	<100	↑	30 (65.2%)
VLDL (mg/dL)	38.30	20.39	<30	↑	29 (63%)
						Total = 59.7%

BMI: body mass index; MetS: metabolic syndrome; N: number; SD: standard deviation; WC: waist circumference; FPG: fasting plasma glucose; HDL: high-density lipoproteins; BP: blood pressure; HOMA-IR: homeostatic model assessment of insulin resistance; WHR: waist-and-hip ratio; VLDL: very low-density lipoprotein.

From Pakistani study.

Asian BMI.

From South African study.

Our study included all newly diagnosed cases of T2DM, and the study population fulfilled almost all criteria of MetS, except for diastolic BP (Table 2). Out of 58 males, 58.6% of patients fulfilled four out of five criteria, and out of 46 females, 60.8% of patients met four out of five criteria of MetS (Table 2).

Age was found to be weakly and negatively correlated with BMI, MUAC, and HOMA-IR, and weakly but positively correlated with CI (Table 3A). MUAC was found to be moderately/strongly and positively correlated with WC, WHtR, and BMI, in the group as a whole, as well as in both genders (Table 3A). MUAC was also found to be weakly and positively correlated with insulin level, and HOMA-IR (Table 3A). WC and WHtR were positively and strongly correlated with BMI in all of our study groups, but WHR was not found to be correlated with BMI. A weak positive correlation of age was found with CI in the male group, but not in females (Table 3A). No correlation of CI was found between insulin resistance and glucose level. No relationship of FPG was found with any anthropometric measure or biochemical marker, but a weak and positive correlation of FPG was found with HDL in the male group, and a moderate and positive correlation with HbA1c in the female group (Table 3A).

Table 3A.

Correlation between different MetS indicators among males and females.

Measures	Males (n = 58)		Females (n = 46)		Total (n = 104)
Measures	Pearson's correlation	P-value	Pearson Correlation	P-value	Pearson correlation	P-value
Age with BMI	—	—	−0.31	0.031	−0.21	0.032
Age with MUAC	—	—	—	—	−0.21	0.032
Age with CI	0.29	0.024	—	—	0.22	0.022
Age with Insulin level	−0.26	0.046	—	—-	−0.22	0.025
Age with HOMA-IR	—	—	—	—	−0.22	0.026
BMI with MUAC	0.69	0.000	0.81	0.000	0.77	0.000
BMI with WC	0.78	0.000	0.82	0.000	0.77	0.000
BMI with WHtR	0.80	0.000	0.83	0.000	0.84	0.000
MUAC with WC	0.58	0.000	0.74	0.000	0.68	0.000
MUAC with WHtR	0.50	0.000	0.67	0.000	0.63	0.000
MUAC with Insulin level	0.36	0.005	0.33	0.023	0.29	0.003
MUAC with HOMA-IR	0.31	0.015	0.32	0.031	0.26	0.006
Insulin level with HbA1c	—	—	—	—	0.20	0.037
HOMA-IR with HbA1c	—	—	—	—	0.21	0.031
FPG with HbA1c	—	—	0.416	0.004	—	—
FPG with HDL	0.28	0.033	—	—	—	—

In binary regression analysis, MUAC was found to be independently associated with central obesity indicator, WC (OR = 1.76 [95% CI: 1.04, 2.99], p = 0.035), after adjusting for age, gender, and all other obesity indicators (WHR, WHtR, and BMI). Regression analysis also showed that MUAC alone was independently associated with HOMA-IR (OR = 1.5 [95% CI: 1.00, 2.27], p = 0.047) after adjusting for age, systolic BP, FPG, TG level, cholesterol level, LDL level, and HDL level (Table 3B). Similarly, binary regression analysis showed that BMI alone was independently associated with HOMA-IR (OR = 1.4 [95% CI: 1.00, 2.00], p = 0.046) after adjusting for age, systolic BP, FPG, TG, cholesterol, and HDL levels (Table 3B).

Table 3B.

Binary regression analysis of obesity indicators with HOMA-IR and with each other.

Obesity indicator	Models	P-value	Odds ratio	(95%, CI)
HOMA-IR with MUAC (cm)	Systolic BP + FPG + TAG + HDL	0.023	1.57	1.06, 2.31
HOMA-IR with MUAC (cm)	Systolic BP + FPG + TAG + HDL + Age	0.047	1.5	1.00, 2.27
HOMA-IR with BMI (kg/m²)	Systolic BP + FPG + TAG + HDL + Age	0.046	1.4	1.00, 2.00
HOMA-IR with WHtR	Systolic BP + FPG + TAG + HDL + Age	0.040	—	—
MUAC (cm) with WC (inch)	BMI + WHR, + WHtR	0.043	1.59	1.01, 2.49
MUAC (cm) with WC (inch)	BMI + WHR + WHtR + Age + Gender	0.035	1.76	1.04, 2.99
MUAC (cm) with BMI (kg/m²)	WC + WHR, + WHtR	0.001	2.2	1.37, 3.60
MUAC (cm) with BMI (kg/m²)	WC + WHR + WHtR + Age + Gender	0.002	2.15	1.31, 3.51

BMI: body mass index; WC: waist circumference; WHtR: waist-to-height ratio; MUAC: mid-upper arm circumference; CI: conicity index; p: significance; MetS: metabolic syndrome; BP: blood pressure; HOMA-IR: homeostatic model assessment of insulin resistance; FPG: fasting plasma glucose; HDL: high-density lipoproteins; TAG: triglycerides; WHR: waist-and-hip ratio.

Discussion

The mean age of the study population was around 50 years. The majority of the subjects were educated. Most of the patients were doing their own businesses. About three-fourths of the study population had a positive family history of T2DM.

The dietary habits of the respondents showed that there was a regular intake of wheat chapatti as compared to rice, which had a lower glycemic index as compared to rice. Fruit and vegetable intakes were very low, as compared to the recommendations (DeSalvo, 2016). Consumption of sweet food items (including fizzy drinks) was present in more than 25% population (Table 1). In the literature, an inverse relationship of adherence to healthy diet patterns and a direct relationship of Western dietary patterns with the risk of T2DM were found (Beigrezaei et al., 2019). Although the diet of this population was not westernized, but sure it was not healthy. Similarly, the majority of the patients had a sedentary lifestyle, and those doing some physical activity had a very low level of activity (Table 1) when compared to recommendations to prevent obesity and chronic, noncommunicable diseases (DeSalvo, 2016). An inverse relationship of energy expenditure in any leisure time or any intensity was found with MetS in the literature. Moderate and vigorous PAs during leisure time, transport, and at work were associated with a reduction in MetS (Serrano-Sánchez et al., 2019).

Hypertension is diagnosed as systolic BP ≥130 and/or diastolic BP ≥80 mmHg (Flack and Adekola, 2020). About 60% of the patients had high systolic BP (Table 1). T2DM and hypertension mostly coexist. If hypertension is present in a diabetic person, it increases the risk of cardiac disease, nephropathy, retinopathy, and stroke. Hyperinsulinemia and MetS can be important causes of hypertension (Mitchell, 2021). Hence, keeping an optimal glycemic profile in diabetic patients is a good idea (Sun et al., 2019).

Visual defects, tingling sensations, aches and pains, and fatigue were present in most patients (Table 1), although most of these symptoms were expected to present later in the disease (Fasil et al., 2019). But very few patients gave a history of slow wound healing (Table 1).

Body mass index was found to be significantly associated with prognosis in T2DM (Al-Mendalawi, 2021). The BMI cut point for defining overweight for Asian Americans was lowered from 25 to 23 kg/m², according to American Diabetic Association (ADA), in 2015 (Samuel, 2018). The first and only study in Pakistan (Nadeem et al., 2013) found in the literature reported predictive values of various anthropometric indices as a prediabetic state. The cutoff value of BMI in Pakistani subjects was found to be 25.04 kg/m² in males and 28.05 kg/m² in females. According to Asian criteria, the mean BMI of both genders was higher than normal BMI (>23 kg/m²) in our study (Table 2). Our study also showed a weak negative correlation between age with BMI in the whole group and in female subjects, which was contrary to the fact that BMI increases with age (Abdella et al., 2019). No correlation of age was found with BMI in males. BMI was found to be strongly and positively correlated with WC, WHtR, and MUAC in the group as a whole, as well as in both genders (Table 3A).

BMI, WHR, WC, and WHtR all probably have similar predictive powers for the risk of T2DM (Al-Mendalawi, 2021; Sun et al., 2019). In the Indian population, WC, WHR, and WHtR were found to be better than any other anthropometric measures for predicting T2DM. Optimal cutpoints for WHR, WHtR, and WC were found to be 0.96, 0.56, and 86 cm (33.8 inches) for males, and 0.88, 0.54, and 83 cm (32.6 inches) for females, respectively, in this study (Al-Mendalawi, 2021). In the study in Pakistan (Nadeem et al., 2013), predictive values for WC were found to be 94.37 cm (37.15 inches) in males and 95.5 cm (37.59 inches) in females, WHR were 0.95 in males and 0.97 in females, and for WHtR were 0.54 in males and 0.64 in females for the prediabetic state. According to all these criteria, means for WC and WHtR were high in both genders, and WHR was high in males and at borderline in females in the current study (Table 2). WHtR was found to be significantly higher in females (Table 2). WC and WHtR were positively and strongly correlated with BMI in all of our study groups (Table 3A), but WHR was not found to be correlated with BMI. No significant correlation of age was found with these parameters. Similarly, no correlation of these parameters was found with insulin resistance and glucose level.

Conicity index is a simple anthropometric method used to assess abdominal obesity. CI has been used in various studies to predict diabetes mellitus (Andrade et al., 2016; Wang et al., 2017; Zhang et al., 2018). Pakistani study calculated CI and set cutoff points for CI at 1.32 and 1.39 for males and females, respectively (Nadeem et al., 2013). The CI values in this group were found to be high in males, but normal in females (Table 2). In males, BMI was found to be the best indicator of insulin resistance, while in females, the CI seemed to be the best indicator (Nadeem et al., 2013). However, CI values in our group were found to be high in males, but normal in females (Table 2). The study showed a weak positive correlation of age with CI, in the group as a whole, and male group, but not in females (Table 3A). No other significant correlation of CI was found.

MUAC was found to be effective in the determination of central obesity, and it was found to be positively correlated with BMI, WC, and WHR (Hou et al., 2019; Zhu et al., 2020). In our study, MUAC was found to be strongly and positively correlated with BMI, WC, and WHtR. MUAC is commonly used in children for the detection of malnutrition, but a cutoff value for obesity in Asian (especially Pakistani) adults could not be found. In a study done in South Africa, MUAC >29 cm was set for overweight and >29.9 cm for obesity in men, and MUAC >28 cm for overweight and >29.4 cm for obesity in women (Gerber et al., 2019). Using these cutoff values, MUAC was found to be within the obesity range in both genders, and more so in females (Table 2). In our study, MUAC was found to be strongly and positively correlated with BMI, WC, and WHtR. MUAC was found to be weakly and negatively correlated with age (Table 3A), which was consistent with our findings in the literature that MUAC level decreased with age (Hou et al., 2019), where muscle mass reduced with a substantial increase in visceral fat, even if the bodyweight remained unchanged. In our study, MUAC was found to be weakly and positively correlated with insulin level and HOMA-IR (Table 3A), which was found to be consistent with our findings in the literature (Hou et al., 2019; Zhu et al., 2020), but it was not found to be correlated with FPG, HbA1c, or lipid profile.

Hyperglycemia was defined as FPG 5.6 mmol/L (101 mg/dL) or HbA1c ≥6.5% (48 mmol/mol) according to the American Diabetes Association (2020). Findings from the second NDSP (National Diabetes Survey of Pakistan) showed that the HbA1c threshold for prediabetes and newly diagnosed diabetes was lower in our country. The optimal HbA1c cutoff point of 5.7% (39 mmol/mol) could identify people with undiagnosed diabetes and 5.1% (32 mmol/mol) was found to have an increased risk of developing T2DM, as compared to recommended HbA1c values by ADA (Basit et al., 2020). Although the participants compared by both criteria, that is, oral glucose tolerance test (OGTT) and HbA1c levels, were showing that HbA1c had a non-similar performance as a screening tool for diabetes and prediabetes as compared to OGTT, results were consistent with previous studies, which showed that FPG (OGTT or fasting only) measurement performs better than HbA1c for screening NDD and prediabetes (Aguilar-Salinas and Viveros-Ruiz, 2019; Basit et al., 2020). In the current study, all participants had much higher values of FPG and HbA1c than ADA cutoff values (Table 2). No relationship of FPG was found with any anthropometric measure or biochemical marker. Only a positive and weak correlation of FPG was found with HDL in the male group, and a moderate and positive correlation with HbA1c in the female group (Table 3A).

Hyperinsulinemia is a marker of insulin resistance. Hyperinsulinemia was found in obese who were newly diagnosed diabetics as compared to the normal population (Dutta and Bhatt, 2019). According to WHO definition (1999), hyperinsulinemia was present if fasting serum insulin and/or 2-h serum insulin level was ≥90th percentile (Nie et al., 2018). According to WHO standard, the cutoff points of normal glucose tolerance hyperinsulinemia in Nanshan of Shenzhen (China) were found to be fasting serum insulin ≥13.85 mU/L (µU/mL) and/or 2-h serum insulin ≥74.97 mU/L (µU/mL) (Al-Mendalawi, 2021; Nie et al., 2018). Our study participants had high fasting insulin levels according to this criterion. For HOMA-IR, a cutoff value of 2.5 was used to divide subjects into insulin-sensitive or insulin-resistant categories (Severeyn et al., 2019). Mean values of HOMA-IR were found to be very high in both genders in our study (Table 2). Out of all anthropometric measures, only a significant and weak positive correlation was found between MUAC with insulin level and HOMA-IR. Our study showed a weak negative correlation of age with insulin level and HOMA-IR (Table 3A), which was contrary to our expectation, because MetS was usually supposed to appear in middle age, and worsen with advancing age (Grundy, 2020). No correlation between insulin and HOMA-IR was found with FPG, but both were weakly and positively correlated with HbA1c (Table 3A). This finding was also consistent with the early stages of diabetes.

Our study population fulfilled almost all criteria of MetS. The prevalence of MetS was found to be 59.7% in these newly diagnosed T2DM patients, as compared to European patients with 88.8% prevalence of MetS, and Chinese patients with 68.1% prevalence of MetS in T2DM patients (Li et al., 2019).

BMI, MUAC, and WHtR were found to be independently associated with HOMA-IR, but WC, WHR, and CI failed to show this effect (Table 3B). MUAC was found to be better than BMI and WHtR as shown by odds ratios. MUAC was also found to be independently associated with the central obesity indicator, WC. This finding was consistent with other studies done on central obesity and insulin resistance in the Chinese population (Li et al., 2019; Zhu et al., 2020). Hence, MUAC could be used in place of WC in the classical model used to predict MetS.

Conclusion

Evaluation of MetS indicators can be helpful for the early management of T2DM. Prevalence of MetS was found in more than half of these newly diagnosed T2DM patients.

Supplemental Material

sj-docx-1-nah-10.1177_02601060221144140 - Supplemental material for Study of metabolic syndrome indicators in newly diagnosed diabetes mellitus type 2 patients in Pakistani population

Supplemental material, sj-docx-1-nah-10.1177_02601060221144140 for Study of metabolic syndrome indicators in newly diagnosed diabetes mellitus type 2 patients in Pakistani population by Hajra Ahmad, Zaheer Ahmed, Seemin Kashif, Saba Liaqat and Asma Afreen in Nutrition and Health

Footnotes

Acknowledgements

The author are grateful to all the study participants for cooperation.

Author's contribution

Hajra Ahmed (Conceptulization, Methodology, Project Administration, Writing - Original Draft, Writing - Review & Editing), Zaheer Ahmed (Supervision, Project Administration, Conceptulization, Writing-Review & Editing, Investigation), Seemin Kashif (Statistical Analysis), Saba Liaqat (Writing - Review & Editing, Software, Formal Analysis), and Asma Afreen (Writing - Review & Editing).

Data availability statement

The data that support the findings of current study would be provided on request.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethical approval

This study was approved by Pakistan Institute of Medical Sciences, Islamabad, Institutional Review Board (IRB). File attached in supplementary material.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Zaheer Ahmed

Supplemental material

Supplemental material for this article is available online.

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Supplementary Material

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