Assessment of risk of depression and diabetes among overweight and obese subjects with unsuccessful efforts to reduce body weight: Observation from clinical trial participants of weight loss intervention

Ranakishor Pelluri1,2,4, Kongara Srikanth2, Jithendra Chimakurthy3, Vanitharani Nagasubramanian4* 1Department of Pharmacy Practice, Vignan Pharmacy College, Guntur, India. 2Department of Endocrinology, Endo-life Specialty Hospital, Guntur, India. 3School of Pharmaceutical Sciences, Vignans Foundation for Science Technology and Research, Deemed to be University, Guntur, India. 4Department of Pharmacy Practice, Sri Ramachandra Institute of Higher Education Research, Deemed to be University, Chennai, India.


INTRODUCTION
According to the World Health Organization (WHO) statistics, an estimate of 350 and 500 million people are suffering with depression and obesity, respectively, and these statics are alarming (World Health Organization, 2000). Worldwide one among six adults obese and approximately each year 2.8 million persons die because of obesity or over weight (Pradeepa et al., 2015). The investigations reported, to achieve and retain weight loss in peoples with obesity or overweight (OW) is arduous; hence, most of the peoples were failure to achieve the goal (Mariman et al., 2012;Rosenbaum et al., 2010;Sumithran et al., 2013). In addition, obesity and depression had contributed to the burden to the public including rise in the healthcare costs, morbidity and mortality (Chapman et al., 2005).
The conflicting outcomes between among obesity (OB) and depression (De Wit et al., 2010;Fabricatore et al., 2011), but some have not supported this conclusion in all subjects (Bin Li et al., 2004;Chang et al., 2012). The management of obesity in patients with depression is a difficult task. However, a moderate decrease in the body weight had contribution positively to improve the depressive symptoms (Jantaratnotai et al., 2011). The aim of this work was to investigate the burden of depression and type-2 diabetes between OW and obese subjects from the Clinical trial of weight loss intervention.

Study population
It was an observational study, the participants were enrolled after taking the informed consent form, and the study was enrolled in clinical trial registry, Reg. No. CTRI/2020/02/023329. Totally 165 subjects of either sex, with aged range from ≥19 years and less than 65 years with non-diabetic OB or OW data were collected at the first visit. The participants were screened for vital and medical examinations and biochemical parameters, such as lipid profile, homeostatic model assessment for insulin resistance (HOMA-IR) were measured at the Department of Endocrinology and Metabolism, India. Before data collection, all participants given informed consent, and the trial protocol was approved by the Institutional Ethics Committee of Endo-Life Specialty Hospital, Guntur, India.

Anthropometric measurements
The height and weight of the subjects was measured in centimeters and kilograms using a digital scale. The body mass index (BMI) of the subjects was calculated as the weight of the person in kilograms divided by the square of the height. The OW and obesity subjects were categorized as Class-I, II, and III according to WHO standards (World Health Organization, 2000) as follows: OW (BMI kg/m 2 between 25.0and 29.9), and obese (BMI >30.0), obese class-I (30.0-34.9), obese class-II (35.0-39.9) and obese class-III (≥40.0). The waist circumference was measured by using normal conventional (Non-Elastic) measuring tape.

Depression outcomes
In our study, the screening of depression was carried out by Patient Health Questionnaire (PHQ-9) scale. The PHQ-9 scale was used to screen the depressive symptoms (Maurer, 2012), and it is validated (Nease, 2003) and easy to complete by the individuals. Responses are scored from 0 to 3, representing "not at all," "several days," "more than half the days," and "nearly every day," respectively, with total scores ranging from 0 to 27. Scores ≥ 10 are usually used to describe the depression in clinical studies.

Diabetic risk outcomes
Assessment of diabetic risk was carried out by using a patient self-assessment diabetes screening score which contains total seven questionnaires. If the total score is greater than or equal to five, the subjects are at increased risk for having type-2 diabetes mellitus (T2DM) (American Diabetic Association, 2008). Insulin resistance (IR) was measured with HOMA-IR. This method is used for not only for assessing β-cell function but also for IR from basal (fasting) glucose and insulin. HOMA-IR was calculated using the following formula (Matthews et al., 1985). HOMA-IR = fasting plasma glucose (mg/dl) × fasting insulin (µU/ml)/405. Based on literature (Aydin, 2014;Calori et al., 2011) a cutoff value of ≥2.5 was selected for HOMA-IR to find IR.

Statistical analysis
Statistical Package for the Social Sciences (SPSS) version 22 (IBM Corporation, Chicago, IL) was used for conducting data entry and statistical analysis whereas the frequencies and percentages were determined using descriptive analysis for categorical variables. Central tendency and dispersion were calculated for quantitative variables. Independent sample t-test and Chi-square test or Fisher's exact test were employed for comparing continuous variables and categorical variables. The results of risk were expressed in 95% confidence intervals (CI) and odds ratio (OR). The level of significance was fixed at p < 0.05 for all the analysis.

RESULTS
The characteristics of the 165 participants (63, male and 103 women) and the participants were stratified by presence of OW (<30 kg/m 2 ) and obesity (≥ 30 kg/m 2 ). Participants of gender, age, height and lipid profile were not significant (p < 0.05) among OW subjects as compared to OB. (Table 1). The continuous variables (Supplementary Table S1) are statistically non-significant and the mean values were nearly equivocal among the both genders, but the mean values of low density lipoprotein (LDL), T2DM risk score was high in males. Table 2 depicts the participants with PHQ-9 score were stratified by moderate to severe depression (≥10) and the non depression to mid depression (<10). Except alcoholics (p < 0.05), remaining all characteristics were significantly (p > 0.05) associated with depressive subjects (PHQ-9 score ≥ 10). The odds of depression were 3-5 times more among the subjects with obesity, hypertension, unmerited status, and smokers, respectively. Table 3 represents the risk of T2DM, the subjects with low income and alcoholics were not have significant association with T2DM (p < 0.05), remaining all parameters were associated (p > 0.05) with T2DM. The proportionate analysis (Chi square test) explains the highest significant association between BMI and depression (p < 0.05) and significant association between BMI and T2DM (p > 0.05) (Tables 4 and 5). We were also analyzed the prediction of risk of diabetes among the obese subjects with depression ( Table 6). The subjects were stratified into PHQ-9 <10 score and ≥10 score respectively. The IR and DM risk (≥ 5) score were not statistically associated with depression.

DISCUSSION
OB and OW are common morbidities with overlapping pathophysiology whose co-existence is associated with adverse  health outcomes. In fact, depression was improved with successful intervention in many OB or OW subjects after reducing their weight (Jantaratnotai et al., 2017). Present study demonstrates the risk of depression and type-2 diabetes among OW and obese subjects, especially pronounced in the subjects with unsuccessful efforts to reduce their body weight. Additionally we examined, weather the obesity and depression additively associated with risk of diabetes. The mean score of HOMA-IR and PHQ-9 score were significantly high in obese subjects (Table 1) and OW, OB, hypertension, and alcohol consumption were not risk factors for depression in our study subjects (  Table S3). The mean PHQ-9 scale was found to be 7.55 ± 2.45 in males and 7.79 ± 2.57 were observed in females (Supplementary Table S1). The similar results were observed in (Cui et al., 2018;Rathee, 2017;Stunkard et al., 2003). The number of subjects with PHQ-9 score (depression severity score) <10 was observed in 44 (37.28%) males and 18 females (38.30%). The depression severity score ≥10 was observed 74 (62.72) in females and 29 (61.70) in males, which indicates the high risk of depression in females. The subjects with <10 PHQ-9 score have the mean BMI of 29.9 ± 2.7 and ≥10 had 33.90 ± 2.88 BMI, respectively. These results suggest, the likely hood of depression was more common in OB individuals (Supplementary Table S2). Similarly, being OW (OR: 1.39. 95% CI: 1.03-1.87) is a noticeable risk factor for depression (Smarr et al., 2011). The chi-square analysis shown in Table 4 has significant association between BMI and depression score (p =  0.001). Significant relation (p = 0.048) was observed in between PHQ-9 score and DM risk score, and inverse relation with HOMA-IR (Supplementary Tables S6 and S5). The mean FPG value is high 93.70 ± 10.63 among the subjects with ≥10 PHQ-9 score. The similar study conducted in rural China, reported depressive symptoms were negatively associated with metabolic syndrome (MetS) (Yu et al., 2017). The BMI verses PHQ-9 and DM risk were statistically significant (p = 0.0001) (Tables 4 and 5) among our subjects. Recent research indicates that there is a longitudinal association between obesity and depression reported that a higher BMI tended to cause depression and vice versa (Konttinen et al., 2014). The insignificant relation between HOMA-IR and PHQ-9 score (p = 0.31) was showed in (Supplementary Table S4), but the mean value of HOMA-IR was high in ≥10 PHQ-9 score group, which indicates IR is likelihood to associated for depression. The mean HOMA-IR values were equivocal in both genders (Supplementary Tables S1 and S2). In Indian adolescents, the IR was amplified gradually from normal weight to obese in both genders (Singh et al., 2013). There is an inverse relationship of the female gender with obesity and they were more likely to develop MetS of the atherosclerotic risk in community study (Bradshaw et al., 2013).    *PHQ Score (0-4) = No risk; (5-9) = Moderate risk; (10-14) moderate risk, (>15) = Severe risk (16) (Spitzer et al., 1999). *DM risk score (<4) = no risk; DM risk score (=4) = high risk for undiagnosed/pre-diabetes; DM risk score (≥5) high risk for undiagnosed diabetes (17)   The p value < 0.05 were considered to be statistically significant. The p value < 0.05 were considered to be statistically significant. The p value < 0.05 were considered to be statistically significant.