|Year : 2020 | Volume
| Issue : 3 | Page : 353-357
A case–control study on food frequency and meal pattern distribution in coronary artery disease patients attending tertiary care teaching hospitals
Siddharth Bhagvandas Agrawal, Dishant Bhavin Upadhyay, Aparajita Abhinava Shukla
Department of Community Medicine, Smt. NHL Municipal Medical College, Ahmedabad, Gujarat, India
|Date of Submission||04-Sep-2019|
|Date of Acceptance||16-Mar-2020|
|Date of Web Publication||1-Sep-2020|
Dr. Siddharth Bhagvandas Agrawal
201, Aryan Apartments, 37 Pratapgunj, Vadodara - 390 002, Gujarat
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Context: Coronary artery disease (CAD) is the blockage of coronary arteries, usually consequent to atherosclerosis. CAD is a lifestyle disease with an increasing disease burden in society. Evaluation of risk factors for CAD is crucial for its prevention. Lifestyle components like calorie consumption chronology, saturated fatty acid (SAFA) intake, reclining time, nocturnal eating and intermittent fasting were considered. Aims: To correlate calorie distribution, SAFA intake, reclining time, nocturnal eating and intermittent fasting with occurrence of CAD. Study Design/Materials and Methods: A case-control study consisting of 235 cases and 185 controls. Questionnaire was self-designed according to NIN guidelines. Study was ICMR funded and data analysis was done using Microsoft Excel and IBM SPSS. Results: Across case and control groups, total calorie consumption difference was insignificant (P = 0.42). Calories consumed in breakfast slot (P = 0.001) and dinner slot (P = 0.003) were significantly different possibly due to discrepancy among circadian variation in insulin sensitivity and calorie consumption distribution. Reclining time <1 h in afternoon (odds ratio [OR] = 2.24, 95%, 1.481–3.356) and night (OR = 2.05, 95% confidence limit [CL], 1.233–3.410), SAFA consumption (OR = 2.006, 95% CL, 1.214–3.316), intermittent fasting (OR = 1.748, 95% CL, 0.997–3.067) and nocturnal eating (OR = 1.291, 95% CL, 0.779–-2.141) are potential risk factors. Conclusions: Calorie consumption chronology, SAFA intake, Reclining time, Nocturnal eating and intermittent fasting emerged as significant risk factors.
Keywords: Calorie distribution, coronary artery disease, nocturnal eating, reclining time
|How to cite this article:|
Agrawal SB, Upadhyay DB, Shukla AA. A case–control study on food frequency and meal pattern distribution in coronary artery disease patients attending tertiary care teaching hospitals. Indian J Community Med 2020;45:353-7
|How to cite this URL:|
Agrawal SB, Upadhyay DB, Shukla AA. A case–control study on food frequency and meal pattern distribution in coronary artery disease patients attending tertiary care teaching hospitals. Indian J Community Med [serial online] 2020 [cited 2020 Sep 26];45:353-7. Available from: http://www.ijcm.org.in/text.asp?2020/45/3/353/294162
| Introduction|| |
Coronary artery disease (CAD) is the impedance or blockage of one or more arteries that supply blood to the heart usually due to atherosclerosis. Overall prevalence of CAD among adults based on the clinical and electrocardiogram (ECG) criteria is currently estimated as 9.7% and 2.7% in the urban and rural populations, respectively.,, Qualitative and quantitative aspects of meal consumption can have significant implications on prevention and prognosis of CAD. Incidence of CAD is higher in people following western diet than those following Mediterranean diet, hence.,,,, Circadian rhythm is governed by the perception of light; and the peripheral clocks of the body are governed by food intake,,, any disturbance between the coordination of these factors could lead to increased risk of CAD. Such disturbances are possible by alterations in meal patterns such as breakfast skipping and intermittent fasting (“Calorie restriction for more than fifteen hours continuously in one day”).,,,,, Time interval between meal and sleep (“Reclining Time”) and nocturnal eating (“Calorie consumption after dinner and before sleeping most commonly in form of snacks”) has significant correlation with digestion of food. Meal satiety also varies with time of day and that food intake during the night is less satiating and leads to greater daily caloric intake compared to morning hours. Night-shift workers have a higher prevalence of overweightness, abdominal obesity, elevated triglycerides, dyslipidemia, impaired glucose tolerance, and decreased kidney function compared to day workers., Reduction in intake of saturated fatty acid (SAFA) does not significantly decrease the incidence of CAD, rather when it was replaced by poly unsaturated fatty acid (PUFA); a significant decrease in risk of CAD was observed., Hence a study considering the above risk factors was conducted.
| Materials and Methods|| |
This case–control study assessed meal pattern distribution associated with occurrence of CAD in diagnosed cases of CAD or Myocardial infarction visiting tertiary care hospitals. After obtaining the institutional ethical clearance the study was conducted for a period of 6 months starting from March 10, 2019 to September 10, 2018. Two tertiary care hospitals were included one private and one government institute. Government and private institutes were included to accommodate a wide socioeconomic strata. Patients visiting the outpatient department (OPD) and those admitted in the intensive coronary care unit (ICCU) and medical wards were enrolled in the study and a dietary questionnaire based on National Institute of Nutrition (NIN) guidelines was administered in each subject after obtaining consent. A 24 h dietary recall method mentioned in NIN guidelines was adopted. The questionnaire included past 24 h dietary history in each OPD patient and routine dietary history in indoor patients. Confidentiality was maintained during the study. The cases and control were selected on the basis of consent given, diagnosis of CAD (cases) and ruling out CAD (control), >40 years age, ruling out mental instability. The patients refusing to give the consent and that of altered mental status were not included. All patients diagnosed or being treated under a cardiologist in ICCU and cardiology OPD for CAD using Angiography, ECG and Echocardiography were selected as subjects. Patients attending the OPD services of the hospital were selected as controls after matching socio-demographic data and clinical details and ruling out CAD in the selected individuals. All the dietary parameters were measured on weekly and daily basis in normal routine life of the subjects and frequency of each was obtained. Various clinical and sociodemographic parameters such as age, gender, occupation, co morbid illness and addiction history were considered to facilitate matching between both groups. Data was collected using questionnaire with three parts: Part 1: Sociodemographic details, Part 2: Clinical history, Part 3: dietary details (according to “NIN guidelines”). Calorie consumption pattern along with oil and fat intake were recorded (weekly and daily basis). Confounding factors such as recent hospitalizations, recent dietary changes, or any other parameter limiting the target of this study were eliminated as subjects. Also incomplete questionnaires were also excluded. The data were compared between case and control groups after limiting confounding factors. As the data was purely based on subject's compliance and recall this study was limited by subjective bias and recall bias. Sample size was 420 with 235 in case (males = 195, females = 40) and 185 in control (males = 152, females = 33) group and was obtained during a time period of 6 months. In this “Time bomb” study all cases and controls visiting the selected tertiary care teaching hospitals during the study duration of 6 months were selected and enrolled. Each subject after being diagnosed or treated by a cardiologist for CAD was selected as a case after obtaining consent. Controls were selected by ruling out CAD and obtaining consent. All incomplete forms (n = 50) were excluded in data entry and analysis. All forms which had important data (“sociodemographic, clinical or dietary.”) missing in any of the three parts of the questionnaire or did not fulfill the matching and inclusion criteria were excluded as subjects. Quantitative variables obtained were coded and were entered in Microsoft Excel 2013 and results were derived using IBM SPSS software (IBM Statistical Package for the Social Sciences, Armonk, New York, United States of America). Various statistical tests such as odds ratio (OR), Chi square, Unpaired t-test, Spearman and Pearson correlations, frequency tables, crosstabs, mean and median were adopted. Matching was done using sociodemographic and clinical history. All tests were performed at 95% confidence limit (CL) [Figure 1].
| Results|| |
It is a case–control study of 420 people (cases: 235, controls: 185). The mean age in case and control group was 57.86 ± 10.112 and 57.19 ± 10.714 respectively. All individuals enrolled in the study were aged between 40 years and 75 years. The median age in both groups was 57. The case and control group had 40 females-195 and 33 females-152 males respectively. In the case group 67.7% had no addiction, 2.6% were chronic alcoholics, 7.2% were chronic smokers, 13.6% were chronic tobacco chewers and 9%-more than one addictions while in the control group 64.3% had no addiction, 2.7% were chronic alcoholics, 5.4% were chronic smokers, 16.2% were chronic tobacco chewers and 11.4%-more than two addictions [Table 1]. In the case group 7.2% had a family history of CAD, 9.4% of DM, 10.6% of HT, while 8.1% had a positive family history of two or more than two risk factors mentioned above. Family history in control group was positive in 1.6% for CAD, 7.6% for DM, 13% for HT and 2.7% for two or more than two risk factors mentioned above [Table 2]. In case and control group 5.1% and 8.6% were diabetics, 7.7% and 14.1% were hypertensives, 19.7% and 7% had two co-morbid illnesses and 5.5% and 1.1% had more than two co-morbid illnesses respectively [Table 3]. In the comparison of calorie distribution in the case and the control group a statistical significant difference is found in the breakfast slot 4:00 am–10:00 am (P = 0.001) and dinner slot 16:00–22:00 (P = 0.003). In contrast there is little significant difference in time slots 10:00 am–16:00 and 22:00–4:00 am [Table 4]. The fat analysis show that 64 out of 235 individuals consume oil rich in SAFA and poor in PUFA in the case group while 33 out of 185 individuals do so in control group (OR = 2.006, 95% CL, 1.214–3.316). SAFA intake was estimated qualitatively consisting of the brand and type of edible oil consumed by both population. Furthermore frequency of ghee and butter intake was also measured. Content of SAFA was obtained from standardized charts and compared in both groups. In the case group 47 out of 235 people have a habit of nocturnal eating whereas in the control group 30 out of 185 individuals have a habit of nocturnal eating (OR = 1.291, 95% CL, 0.779–2.141). In the case group Reclining time (afternoon) is <1 h in 111 people and is more than 1 h in 124 people. In the control group it is <1 h in 53 people and more than 1 h in 132 people (OR = 2.24, 95%, 1.481–3.356). In the case group reclining time (night) is <1 h in 59 people and is more than 1 h in 176 people. In the control group it is <1 h in 26 people and more than 1 h in 159 people (OR = 2.05, 95% CL, 1.233–3.410). In the case group 43 out of 235 individuals have a habit of intermittent fasting while in the control group 21 out of 185 individuals have a habit of intermittent fasting (OR = 1.748, 95% CL, 0.997–3.067).
| Discussion|| |
Total calorie intake for an entire day of each subject was analyzed. No statistically significant difference was found (P = 0.420) in overall calories consumed daily between case and control groups.,, Thus, the effect of total dietary intake seems to have no significant effect on cardiovascular health. Hence chronology of the calorie intake was considered. Calorie intake of all the participants was calculated during four time slot. The results suggest that individuals having less calorie intake during breakfast slot significantly increase risk of CAD (P = 0.001). A positive correlation was obtained between increased calorie intake in the breakfast slot and preserved cardio-metabolic health (“Pearson correlation = +0.134, alpha error = 0.01”). People consuming greater amount of calories during dinner slot showed a greater occurrence of CAD (P = 0.003). A negative correlation was obtained between increased calorie intake in the dinner slot and preserved cardio-metabolic health (“Pearson correlation = −0.105, alpha error = 0.01”). Sharma et al. has documented how breakfast skipping increases the risk of CAD by 34% and that it emerged as stronger risk factor than obesity and sedentary life style in Indian population and showed close association with presence of hypertension. Our findings are thus, consistent with previous study conducted in the same target population. Breakfast skipping and taking lower calorie intake during morning hours of 04:00 to 10:00 can increase the risk of CAD. Thus the diurnal variation of glucose tolerance and circulating free fatty acid (FFA) levels appears to be responsible for the same. Insulin sensitivity is significantly higher in the morning than the evening. It has been demonstrated that evening insulin response pattern in normal subjects resembles to that of a diabetic patient., Having frequent meals outside of the high insulin sensitivity phase of the day might expose the patient on a daily basis to a transient phase of adverse metabolic conditions leading to CAD. Having greater part of the daily calories within the 'high' insulin sensitivity phase can prevent a period of increase in circulating FFA levels, thereby protecting against progression of CAD.,, Also circadian misalignment leads to metabolic instability. Thus calorie distribution appears to be a more important marker of cardio metabolic health rather than the diet type or amount of total calories consumed daily.,, There is a long fasting time before the breakfast slot mainly due to sleep often leading to hypoglycemic state on waking up. Hence not compensating the glucose deficit by skipping breakfast can cause metabolic disturbances and circadian misalignment often speeding the atherosclerotic process.
Oil and fat analysis
A risk of developing CAD in association with increased consumption of oil rich in SAFAs (OR = 2.006, 95% CL, 1.214–3.316). 26% consumed groundnut oil in the cases as compared to 15% in controls. Groundnut oil has high concentration of SAFA and poor concentration of PUFA. Reduction in intake of SAFA does not significantly decrease the incidence of CAD, rather when it is replaced by PUFA; a significant decrease in risk of CAD was observed.,, Increased SAFA consumption leads to greater turnover of fat in body leading to obesity and hypertension hastening the atherosclerotic process.
Nocturnal eating as defined as calorie consumption most commonly snacks after dinner and before sleeping. Nocturnal eating increased risk by 29.1% of CAD (OR = 1.291, 95% CL, 0.779–2.141). These results are in accordance with the study that in healthy adults, meal satiety also varies with time of day and that food intake during the night is less satiating and leads to greater daily calorific intake as compared to morning hours. Also due to diurnal secretion of insulin the insulin sensitivity during night hours is similar to that of a diabetic person hence calories consumed during late night could worsen cardio-metabolic health.,
Reclining time is defined as the time interval between meal and sleep. The groups were formed by dividing the population into people having reclining hours less than one and people having reclining hours more than one. After comparison the results showed significant statistical difference in afternoon (OR = 2.24, 95%, 1.481–3.356) and in night (OR = 2.05, 95% CL, 1.233–3.410). Thus it could be said that people going to sleep within 1 h of having meal showed a greater occurrence of CAD and can be considered as a risk factor. There could be many possible reasons that would lead to such a finding. Disturbed gastric emptying,, and greater propensity for sedentary life style in those having a shorter reclining time may be the reason for this increased occurrence. Gastroparesis due to reclining worsens metabolic profile by causing insulin resistance, dyslipidemia and malabsorption. Also skipping meals in the morning could lead to increased calorie intake during lunch and thereby worsening postlunch dip and reduced reclining time and increasing cardio-metabolic risk.
Intermittent fasting is defined as more than 15 h of continuous calorie restriction in 1 day. Intermittent fasting is considered as a healthy approach in the western countries but in Indian population intermittent fasting is often consequent to religious beliefs rather than dietary goals therefore the results obtained are quite contrary. The results showed association of occurrence of CAD with intermittent fasting (OR = 1.748, 95% CL, 0.997–3.067). These results may be due to an increased calorie intake prior to or after the fast. This could be explained by the study that disturbance in the coordination of the circadian rhythm and the peripheral clocks which are governed by intake of food. Thus intermittent fasting could be considered a risk factor as far as CAD is concerned as it disturbs the chronology of calorie intake.
| Conclusions and Limitations|| |
Increasing burden of CAD in the society especially the urban areas could be prevented at the primary level by intervention in the chronology of the dietary habits. Calorie distribution with maximum calories consumed during the dinner slot, oil rich in SAFA and poor in PUFA, Nocturnal eating, Reclining time <1 h and intermittent fasting mainly on religious grounds increased the risk of CAD and other comorbid illnesses worsening the cardio-metabolic prolife. The study was limited by small sample size, selective bias and recall bias. Dietary variations due to morbidity also limited the scope of the study. Future studies with larger sample size are recommended as rural population, young CAD cases, patients aged more than 75 years of age were not included. Inclusion of above-mentioned population will produce results for a bigger population.
Sheth Vadilal Sarabhai General Hospital, Care Institue of Medical Sciences (CIMS), Ahmedabad.
Financial support and sponsorship
Indian Council for Medical Research.
Conflicts of interest
There are no conflicts of interest.
| References|| |
Krishnan MN. Coronary heart disease and risk factors in India - on the brink of an epidemic? Indian Heart J 2012;64:364-7.
Gupta R, Mohan I, Narula J. Trends in coronary heart disease epidemiology in India. Ann Glob Health 2016;82:307-15.
Enas EA, Yusuf S, Mehta JL. Prevalence of coronary artery disease in Asian Indians. Am J Cardiol 1992;70:945-9.
Oikonomou E, Psaltopoulou T, Georgiopoulos G, Siasos G, Kokkou E, Antonopoulos A, et al
. Western dietary pattern is associated with severe coronary artery disease. Angiology 2018;69:339-46.
Kafatos A, Diacatou A, Voukiklaris G, Nikolakakis N, Vlachonikolis J, Kounali D, et al
. Heart disease risk-factor status and dietary changes in the Cretan population over the past 30 y: The Seven Countries Study. Am J Clin Nutr 1997;65:1882-6.
Kant AK, Graubard BI. 40-year trends in meal and snack eating behaviors of American adults. J Acad Nutr Diet 2015;115:50-63.
Garaulet M, Gómez-Abellán P. Timing of food intake and obesity: A novel association. Physiol Behav 2014;134:44-50.
Carroll KF, Nestel PJ. Diurnal variation in glucose tolerance and in insulin secretion in man. Diabetes 1973;22:333-48.
Sharma K, Shah K, Brahmbhatt P, Kandre Y. Skipping breakfast and the risk of coronary artery disease. QJM 2018;111:715-9.
Hansen AP, Johansen K. Diurnal patterns of blood glucose, serum free fatty acids, insulin, glucagon and growth hormone in normals and juvenile diabetics. Diabetologia 1970;6:27-33.
Song WO, Chun OK, Obayashi S, Cho S, Chung CE. Is consumption of breakfast associated with body mass index in US adults? J Am Diet Assoc 2005;105:1373-82.
Horne BD, Muhlestein JB, May HT, Carlquist JF, Lappé DL, Bair TL, et al
. Relation of routine, periodic fasting to risk of diabetes mellitus, and coronary artery disease in patients undergoing coronary angiography. Am J Cardiol 2012;109:1558-62.
Goo RH, Moore JG, Greenberg E, Alazraki NP. Circadian variation in gastric emptying of meals in humans. Gastroenterology 1987;93:515-8.
Kinsey AW, Ormsbee MJ. The health impact of nighttime eating: Old and new perspectives. Nutrients 2015;7:2648-62.
Van Cauter E, Désir D, Decoster C, Féry F, Balasse EO. Nocturnal decrease in glucose tolerance during constant glucose infusion. J Clin Endocrinol Metab 1989;69:604-11.
Nettleton JA, Brouwer IA, Geleijnse JM, Hornstra G. Saturated fat consumption and risk of coronary heart disease and ischemic stroke: A science update. Ann Nutr Metab 2017;70:26-33.
Assmann G, de Backer G, Bagnara S, Betteridge J, Crepaldi G, Fernandez-Cruz A, et al
. International consensus statement on olive oil and the Mediterranean diet: Implications for health in Europe. The olive oil and the Mediterranean diet panel. Eur J Cancer Prev 1997;6:418-21.
Nutrition and Health, Park's Textbook of Preventive and Social Medicine. Ch. 10. Hyderabad: Nutritive Value of Indian Foods, National Institute of Nutrition; 1980.
Dontas AS, Zerefos NS, Panagiotakos DB, Vlachou C, Valis DA. Mediterranean diet and prevention of coronary heart disease in the elderly. Clin Interv Aging 2007;2:109-15.
St-Onge MP, Ard J, Baskin ML, Chiuve SE, Johnson HM, Kris-Etherton P, et al
. Meal timing and frequency: Implications for cardiovascular disease prevention: A scientific statement from the American Heart Association. Circulation 2017;135:e96-121.
Ekmekcioglu C, Touitou Y. Chronobiological aspects of food intake and metabolism and their relevance on energy balance and weight regulation. Obes Rev 2011;12:14-25.
Verma A, Mahajan A, Shah N. Evaluation of prevalence of daytime sleepiness and its association with chronotype in undergraduate medical and paramedical students. Natl J Integr Res Med 2019;10:10-7.
Yusuf S, Hawken S, Ounpuu S, Dans T, Avezum A, Lanas F, et al
. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): Case-control study. Lancet 2004;364:937-52.
Mertens E, Markey O, Geleijnse JM, Givens DI, Lovegrove JA. Current Neurology and Neuroscience Reports. U.S. National Library of Medicine; 2017.
Katayose Y, Tasaki M, Ogata H, Nakata Y, Tokuyama K, Satoh M. Metabolic rate and fuel utilization during sleep assessed by whole-body indirect calorimetry. Metabolism 2009;58:920-6.
Manchanda SC, Passi SJ. Selecting healthy edible oil in the Indian context. Indian Heart J 2016;68:447-9.
[Table 1], [Table 2], [Table 3], [Table 4]