Metabolic Syndrome in Childhood Cancer Survivors: Does Delta BMI Predict Risk in Lower-middle-income Countries?

DOI: https://doi.org/10.21203/rs.3.rs-747126/v1

Abstract

Purpose Metabolic syndrome (MetSyn) is an important late effect of childhood cancer. The combination of rising obesity and high prevalence of under-nutrition at diagnosis, make this a unique population to study in LMIC (lower middle-income countries).

Methods Children <18 years of age at cancer diagnosis, in a single-centre in a LMIC, who were disease free and had completed treatment at least 2 years prior to study were included. MetSyn was defined using International Federation for Diabetes criteria for Asian Indians. Logistic regression analyses were carried out to evaluate the influence of various risk factors, including delta BMI (increase in body mass index from diagnosis to evaluation), on MetSyn.

Results A high prevalence of MetSyn (12.2%), central obesity (33%), and dyslipidemia (61.8%) was found in a cohort of 500 Asian Indian childhood cancer survivors (CCS) at a median follow-up age of 17 years. Multivariable analysis revealed older age at diagnosis >10 years, OR 2.9(1.6-5), longer survival duration >10 years, OR 2.2(1.3-3.8), high BMI at diagnosis OR 3.2(1.5-6.9) and large delta BMI >50, OR 3.15(1.7-5.9) to be independent predictors of MetSyn.  Patients who were under-weight or normal at diagnosis with large Delta BMI > 50 had very high odds (OR, 12.5,1.7-92) of developing MetSyn compared to those with lower Delta BMI.

Conclusions and implications for cancer survivors: A high prevalence of MetSyn was observed in CCS with early age at onset. Timely screening and early intervention are proven to be beneficial and delta BMI could be a useful screening tool for LMIC.

Introduction

With improved survival of childhood cancers, late effects have emerged as a serious area of concern. As a result, survivors of childhood cancer are at an increased risk of chronic health conditions such as cardio-metabolic disorders, pulmonary problems, stroke, diabetes and second neoplasms. This is true even in the lower middle-income countries (LMIC) as evidenced by an ever-increasing number of survivors, particularly in Asian Indians [1-3]. Of concern is the substantially elevated risk of cardiovascular diseases (CVD) observed in numerous investigations worldwide [4-8] and Oeffinger et al observed this risk to be 10 times higher in childhood cancer survivors (CCS) in the US as compared to their siblings [8]. Metabolic syndrome (MetSyn) is an important predictor of CVD and constitutes a cluster of risk factors including obesity, glucose intolerance, insulin resistance, dyslipidemia, and hypertension [9]. Although the epidemiology of MetSyn is multifactorial, it is to a large extent determined by modifiable factors like obesity, dietary habits and sedentary life style. 

Recent data shows that obesity, is rising in every region of the world, and may in fact be increasing more rapidly in the LMIC than in the high-income countries (HIC) [10-12]. Furthermore, the majority of undernourished children live in LMIC [13]. Hence, child under-nutrition and adult obesity coexist in many LMIC (e.g., India), leading to sarcopenic obesity wherein deficits of muscle mass relative to fat persist into adulthood. This dual combination has been implicated as a risk factor for cardio-metabolic disorders unique to LMIC [14-17].  Recent studies indicate that even among those who are not overweight or obese, the increase in BMI (delta) during growth and development should be closely monitored, as it may predict early onset MetSyn [18]. Hence, we planned to study delta BMI in this context as it has not previously been evaluated among CCS. The present study aims to address this knowledge deficit by analyzing the prevalence of MetSyn in CCS in a LMIC setting and determine the risk factors associated with it.

Methods

Patient selection

This was a cross-sectional, observational, single-centre study, conducted over a period of 18 months, from July 2018 to December 2019, in a tertiary care cancer centre. The Pediatric Oncology department of our Institute runs an after completion of therapy (ACT) clinic, where all off-therapy patients are registered. The hospital and ACT e-database were mined to identify CCS who were ≤18 years at diagnosis, in complete remission and at least two years from treatment completion. Among 1430 patients registered in the hospital, data for 954 patients was available in the ACT e-database, of these 714 could be contacted telephonically; 500 consented and were evaluated at the ACT clinic. The evaluation comprised a complete history (including details of anti-hypertensive and anti-hyperglycemic medication), physical examination, anthropometry, and laboratory tests. Demographic, socioeconomic, disease and treatment details were extracted from the e-medical records. Exposure to various classes of chemotherapeutic agents (anthracycline, alkylating agent, anti-metabolite), steroids and cumulative equivalence doses of anthracycline were calculated. Radiation fields and doses were obtained and categorized as cranial, head and neck (not brain), chest, abdomen/pelvis, extremity and other). Assessment of urban/ rural residence and socioeconomic class was done based on the Kuppuswamy scale (based on overall family income, occupation and education) [19]. The study received approval from the Institute’s ethics committee and written informed consent was obtained from patients/parents (for minors).

BMI was calculated as: BMI= weight (kg)/ height(m2). Nutritional status was defined as per Centre for Disease Control (CDC) and WHO criteria using Asia-Pacific guidelines for South Asians: age 5-19 years, BMI <5th percentile was defined as underweight, BMI = 5th to <85th percentile defined as normally nourished, BMI > 85th to <95th percentile as overweight and >95th percentile as obese, adjusted for age and sex; age >19 years, BMI <18.5 kg/m2 were defined as underweight, normal BMI range was 18.5-22.9 kg/m2, BMI >23 kg/mto <25 kg/m2   was defined as overweight and those with BMI >25 kg/mas obese [20, 21]. Delta BMI was calculated as the percentage change in BMI z-score from diagnosis to follow-up evaluation. Delta BMI = [(BMI z-score at evaluation - BMI z-score at diagnosis) / BMI z-score at diagnosis] x 100. Waist circumference (WC) was measured midway between the lowest rib and the top of the iliac crest at the end of expiration in a standing position using a fiberglass tape with an accuracy of 1 mm. For survivors <18 years of age at evaluation, age- and sex-specific reference percentiles for WC reported in Indian children by Khadilkar et al. were utilized as normative data for defining central obesity [22]. International Diabetes Federation (IDF) criteria for Asian Indians were utilized for adults [23]. Blood pressure (BP) was measured in the right upper arm in sitting position using mercury sphygmomanometer with an appropriate size cuff. Hypertension (HT) was defined as systolic or diastolic values ≥90th percentile for age, sex, and height. Blood biochemistries included fasting blood glucose (FBG) and lipid profile after 12 hours of overnight fasting.

Definition of MetSyn

Using IDF criteria cut-offs for Asian Indians, MetSyn was defined [23] as follows for children 10 to 15 years of age as having central obesity (WC≥90th percentile) plus two or more of the following criteria: (1) Triglycerides (TGs) >150 mg/dL, (2) High-density lipoprotein cholesterol (HDL-c) <40 mg/dL, (3) systolic BP (SBP) >130 mm Hg or diastolic BP (DBP) >85 mm Hg, (4) FBG >100 mg/dL. For those >16 years of age, MetSyn was defined as having central obesity (WC≥90 cm for men and >80 cm for women), plus 2 or more of the following criteria: (1) TGs 150 mg/dL, (2) HDL-c <40 mg/dL in males and <50 mg/dL in females, (3) SBP ≥130 mm Hg or DBP 85 mm Hg, (4) FBG 100 mg/dL. As per IDF guidelines children 6 to < 10years of age were identified as high risk if their WC≥90th percentile and had a family history; in which case further measurements were carried out [24].

Statistical analysis

The participants’ demographic, disease and treatment details were analysed by descriptive statistics and mean (± standard deviation, SD) or median (inter quartile range, IQR) were used as appropriate. Fischer’s exact test and chi-square test were utilized for categorical variables. Quantitative variables were compared using the Student t-test or the Mann-Whitney test. Analysis of variance (ANOVA) was used to compare delta BMI among various nutrition categories (underweight and normal versus overweight and obese). Logistic regression analysis was done to investigate the influence of various pre-treatment and treatment related factors on MetSyn among the survivors. Adjusted OR was calculated to study the impact these factors on individual components of MetSyn.  Factors with p value <0.05 on univariate analysis were included for multivariable analysis. A receiver operating characteristic (ROC) curve was plotted to determine the cut-off value of delta BMI to predict MetSyn. The results were interpreted using odds ratio (OR) and 95% confidence interval (CI), and the significance threshold was set as p-value of <0.05. Statistical analysis was performed using the SPSS software (version 23.0, 2011; IBM, Armonk, NY, USA).

Results

Five hundred CCS were enrolled in the present study; 78% (389) were male, 85% (424) belonged to the middle class and 74% (370) were from urban areas. At the time of evaluation, the median age of the survivors was 17 years, 49% (245) were adults >18 years of age, median survival duration was 7 years and 32.6% (163) had survived for >10 years. Based on BMI at diagnosis, the patients were categorized as underweight (130, 26%), normal (295, 59%), overweight (36, 7.2%) and obese (39, 7.8%). The two most common primary diagnoses were acute lymphoblastic leukemia (ALL) (226, 45.2%) and Hodgkin lymphoma (82, 16.4%). Treatment exposures included chemotherapy in all, steroids in 53% (265), radiotherapy (RT) in 32.4% (162), definitive surgery in 23.4% (117) and bone marrow transplant (BMT) in 2.8% (14) of patients (Table 1). 

Table 1 

Characteristics of childhood cancer survivors with and without metabolic syndrome (MetSyn)

Characteristics

No MetSyn 

N=439(%)

MetSyn 

N=61(%)

P value

Sex 

               Male

               Female                                                                                                                  

 

335 (76.3)

            104 (23.7)                                                                                  

 

54 (88.5)

7 (11.5)

0.031

Median age at diagnosis (years) 

8 (IQR:4-12)

11 (IQR:6-15)

0.003

Age at diagnosis



 

<10 years

278 (63.3)

23 (37.7)

<0.001

10-18 years

161 (36.7)

38 (62.3)

Median age at evaluation (years) 

17 (IQR:12-21)

22 (IQR:6-15)

<0.001

<18 years

240 (54.7)

15 (24.6)

<0.001

> 18 years

199 (45.3)

46 (75.4)

Median duration from treatment completion (years) 

6.7±3.6

9.2±4.8

<0.001

Survival time from diagnosis



 

< 10 years

306 (69.7)

31 (50.8)

0.005

> 10 years

133 (30.3)

30 (49.2)

BMI at diagnosis 



 

Underweight, normal

385 (87.7)

40 (65.6)

<0.001

Obese, overweight

54 (12.4)

21 (34.4)

Delta BMI




≥50

48 (10.9)

17 (27.9)

0.008

<50

391 (89.1)

44 (72.1)

Primary diagnosis 



Hematological malignancy

 319 (72.7)

48 (78.7)

0.357

Solid tumor & brain tumor

 120 (27.3)

13 (21.3)

Hematological malignancy

 

 

 

Acute lymphoblastic leukemia

198 (45.5)

28 (43.1)

0.906

Acute myeloid leukemia

17 (3.9)

3 (4.6)

0.696

Non-hodgkin lymphoma

35 (8)

4 (6.2)

0.699

Hodgkin lymphoma

69 (15.9)

13 (20)

0.269

Solid tumor

 

 

 

Osteosarcoma/ Ewing sarcoma

51 (11.7)

6 (9.2)

0.681

Rhabdomyosarcoma

23 (5.3)

0 (0)

0.06

Wilms tumor

15 (3.4)

3 (4.6)

0.555

Neuroblastoma

7 (1.6)

0 (0)

0.32

Germ cell tumor

17 (3.9)

3 (4.6)

0.696

Others

3 (0.7)

0 (0)

0.517

Brain tumor

4 (0.9)

1 (1.5)

0.592

Treatment Exposure

Chemotherapy

Alkylating

 

 

405 (92.25)

 

 

58 (95.1)

 

 

0.429

 

Antimetabolite

271 (61.7)

37 (60.65)

0.871

 

Steroid

 

 

 

 

Yes

233 (10.9)

32 (27.9)

0.928

 

No

206 (89.1)

29 (72.1)

 

 

Radiotherapy

 

 

 

 

Brain

68 (15.6)

14 (21.5)

0.14

 

Head & neck not brain

24 (5.5)

4 (6.2)

0.728

 

Abdomen & pelvis

21 (4.8)

1 (1.5)

0.261

 

Chest

10 (2.3)

3 (4.6)

0.224

 

Extremity

4 (0.9)

3 (4.6)

0.012

 

Others

9 (2.1)

1 (1.5)

0.829

 

Surgery 

107 (24.4)

10 (16.4)

0.1713

 

Bone marrow transplant

12 (2.7)

2 (3.3)

0.8092

 

IQR, interquartile range; BMI, body mass index. Delta BMI is percent change in BMI z-score from diagnosis to evaluation.

Prevalence of MetSyn and its components

The prevalence of MetSyn in the CCS was 12.2% (61) at a median age of 22 years, their characteristics are detailed in categorical Table 1. The prevalence was lower in children/ adolescents (<18 years, 6%) and higher in adults >18 years (19%). Figure 1 clearly demonstrates the age-wise increment in both males and females. Among the children 6-10 years of age, 28.5% (13/49) were obese by BMI and 22.4% (11/49) had central obesity (WC> 90th centile). Those with family history for cardiovascular risk factors were further assessed and 3 (all were male) of these survivors were observed to have HT (> 90th centile for age and height), elevated TG (>150 mg/dL) and low HDL-c (< 40 mg/dL), hence for the purpose of this analysis they were considered to have MetSyn.

Derangements in individual components [central obesity, impaired fasting glycemia (IFG), hypertriglyceridemia (high TG), low HDL-c, HT] of MetSyn were observed in 68.6% (343) of CCS, one component was abnormal in 40.2% (201) and two components in 16.2% (81). Low values for HDL-c and high WC (central obesity) were the most frequently observed derangements (Table 2). Their frequency and mean values were significantly higher among survivors with MetSyn and in those with high delta BMI (Table 2). It is noteworthy that, 40/61 survivors who developed MetSyn were non-obese/overweight at diagnosis. 

Table 2 

Prevalence of components of metabolic syndrome (MetSyn) and their mean values among childhood cancer survivors by presence of MetSyn and delta body mass index (BMI) 

Components of MetSyn

No MetSyn N=439(%)

MetSyn N=61(%)

P- value

Delta BMI <50

N=435 (%)

Delta BMI ≥50

N=65 (%)

P- value

Prevalence of individual components

Central obesity (WC)

104 (23.7)

61 (100)

<0.001

87 (20.0)

40 (61.5)

<0.001

Hypertriglyceridemia 

19 (4.3)

29 (47.5)

<0.001

36 (8.3)

12 (18.5)

0.021

Low HDL-c

242 (55.1)

55 (90.2)

<0.001

250 (57.5)

47 (72.3)

0.030

IFG

8 (1.8)

13 (21.3)

0.002

13 (3.0)

8 (12.3)

0.003

Hypertension

23 (5.2)

39 (63.9)

<0.001

      50 (11.5)

12 (18.5)

0.153

Mean values

Mean±SD

Mean±SD


Mean±SD

Mean±SD


WC

75.1±12.8

96.7±8.0

<0.001

76.2±13.9

92.1±8.7

<0.001

TG

77.3±36.6

132.6±65.6

<0.001

81.2±41.8

103.1±59

<0.001

HDL-c

40.1±9.1

34.9±6.2

<0.001

39.9±9.1

36.5±7.7

<0.001

FBG

83.8±7.0

94.8±26.2

0.002

84.5±8

89.4±24.9

<0.001

SBP

108.2±13.1

126.5±12

<0.001

109.8±13.4

116.5±11.7

<0.001

DBP

67±9.4

78.3±9

<0.001

68±9.6

72±8.4

<0.001

No of components

 

 

 

 

 

 

0

157 (35.8)

0 (0)

<0.001

154 (35.4)

4 (6.2)

<0.001

1

201(45.8)

0 (0)

<0.001

180 (41.4)

21 (32.3)

0.18

2

81 (18.5)

0 (0)

<0.001

58 (13.3)

26 (40)

<0.001

≥3

0 (0)

61 (100)

<0.001

43 (9.9)

14 (21.5)

<0.001

SD, standard deviation; WC, Waist circumference; IFG, Impaired fasting glycemia; TG, Triglycerides; HDL-c, High-density lipoprotein cholesterol; FBG, fasting blood glucose; SBP, Systolic Blood Pressure; DBP; Diastolic Blood Pressure.

Delta BMI and Nutritional status

Calculation of means and SD of percent BMI z-score increment from diagnosis to evaluation (delta BMI) revealed that the highest delta BMI occurred among those who were underweight (74+2) and normally nourished (123+53) at diagnosis, followed by those who were obese (15.8+1.4) and overweight (7.9+1.5), p, 0.04 (Supplemental Table 1). The ROC curve analysis (Supplemental Figure 1) revealed that underweight and normal patients who had >50 percent increase in delta BMI, had 12.5 (1.69-92) times higher odds of MetSyn compared to those with a smaller delta BMI (<50); and the odds were 43 (5.8-315) times for those with delta BMI of 108 percent (Table 3). 

Table 3 

Metabolic syndrome by nutritional status at diagnosis and Delta BMI

 BMI at Diagnosis

Prevalence of MetSyn

p value

OR (95% CI) of MetSyn when Delta BMI >50

Delta BMI <50

Delta BMI >50

Under-weight, Normal (N=428)

7.4% (28/377)

23.5%   (12/51)

0.0002

12.5 (1.7-92.2)

Overweight, Obese (N=72)

27% (16/58)

35% (5/14)

0.5

2.0 (0.6-8.1)

BMI, body mass index; OR, odds ratio; CI; confidence interval. Delta BMI is percent change in BMI z-score from diagnosis to evaluation.

Risk Factors for MetSyn and its individual components

Of the various risk factors studied, multivariable analysis revealed older age at diagnosis (OR 2.78,1.43-5.4), longer survival duration (OR 2.74, 1.43-5.26), high BMI (obese) at diagnosis (OR 3.2, 1.64-6.2) and high delta BMI (OR 2.26, 1.13-4.55) to be independent predictors of MetSyn (Table 4, supplemental Table 2). 

Table 4 

Univariate and multivariable logistic regression analysis: risk factors for metabolic syndrome 

Variable 

Univariate

 

Multivariable

 


OR

95% CI

P

OR

95% CI

P

Gender Female

1

 

 



 

Gender Male

2.4

1.1-5.4

0.04

1.93

0.83-4.52

0.13

Age at diagnosis <10 years

1.0

 

 



 

Age at diagnosis 10-18 years

2.9

1.6-5

0.00

2.78

1.43-5.4

0.00

Age at eval <18 years

1.0

 

 



 

Age at eval >18 years

3.7

2.0-14.5

0.82




Survival time from diagnosis < 10 years

1.0

 

 



 

Survival time from diagnosis > 10 years

2.2

1.3- 3.8

0.00

2.74

1.43-5.26

0.00

BMI at diagnosis, underweight, normal

1.0

 

 



 

BMI at diagnosis, Obese, overweight

3.2

1.5-6.9

0.00

3.19

1.64-6.2

0.00

Delta BMI <50

1

 

 

 

 

 

Delta BMI ≥50

3.15

1.7-5.9

0.00

2.26

1.13-4.55

0.02

Primary Diagnosis

 

 

 



 

Hematological malignancy

1.0

 

 



 

Solid tumor & brain

0.7

0.4-1.4

0.32



 

Treatment exposure*

 

 

 

 

 

 

Chemotherapy

Steroid

Surgery

1.0

0.9

0.6

 

0.6-1.7

0.3-1.2

 

0.90

0.17



 

RT

1.7

1-2.9

0.07



 

BMT

1.2

0.3-5.5

0.81



 

OR, Odds ratio; CI, confidence interval; P, p-value; eval, evaluation; BMI, body mass index; RT, radiotherapy; BMT, bone marrow transplant.* All patients received chemotherapy.

 Results of chemotherapy exposure showed that use of steroids, anthracyline (>120mg/m2), alkylators or antimetabolites did not influence the occurrence of MetSyn. Patients receiving RT at various sites (cranial, abdominal or pelvic), had similar prevalence of MetSyn. Childhood cancer survivors with ALL treated with cranial RT (CRT) had a low OR (1.9, 0.85-4.24) for developing MetSyn, except in very young children (< 5 years), OR (5.48, 1.5-20), p, 0.01), when compared to those not exposed to CRT of the same age.

On multivariable analysis, adjusted OR revealed that HT (OR 3.9,1.6-9.6) and obesity (OR 2,1-3.8) were more likely in adult survivors (compared to children); hypertriglyceridemia was more in males than females (OR, 3.8;1.1-12.6) and IFG was more likely in survivors of solid tumor (OR 3.7, 1.4-9.6) .Central obesity (OR 2.4, 1.3-4.7) and low HDL-c  (OR 1.7, 1-2.8) were more common in the longer surviving patients(Supplemental Table 2).

Discussion

The present study demonstrates a high prevalence of MetSyn and its components in long term Asian Indian survivors of various childhood cancers. The prevalence of MetSyn was age dependent (Figure 1), being lower among children and adolescents (6%) and higher among the adult (19%) survivors. The data from published National studies on healthy individuals revealed that the prevalence of MetSyn in children/adolescents (1-3%) and adults of similar age (11-13%) was significantly lower than in the present analysis [25-30]. It is well established that MetSyn increases with increasing age, however the higher prevalence in survivors compared to the healthy population is noteworthy and concerning [29-33].

Furthermore, the prevalence of MetSyn and its components in CCS are known to vary with ethnicity, and duration of follow-up, in addition to age. Compared to published literature from Asia, and Europe (France, Netherlands) the current analysis reveals a higher prevalence of MetSyn and at an earlier age [34, 35]. However, it is lower than that reported from US by the St Jude Lifetime Cohort Study (34%) although they had a much longer follow-up and older age at evaluation [6]. The present study demonstrates a 12.2% prevalence of MetSyn in a cohort of 500 CCS in a tertiary care centre in a LMIC at a median follow-up age of 17 years at evaluation. This is higher than that previously reported from Asia (including India), wherein MetSyn was observed in 1.3-8% of pediatric and 10-12% of adult CCS [36-38]. This may be partly explained due to the fact that these studies had a shorter follow-up and younger age at evaluation. High prevalence of MetSyn at an early age among CCS in our study and its potential adverse implications on health and quality of life reinforces the need for increasing awareness for its early identification. This assumes greater significance as it is often an ignored and underestimated late effect and early institution of preventive strategies and lifestyle modification may significantly delay or reverse the associated long-term morbidities.

Components of MetSyn

Two MetSyn components, low HDL-c (61.8%) and central obesity (33%) were observed in a large number of CCS and were much higher than values reported from HIC [6, 35]. This observation may be explained by ethnic differences in patterns of dyslipidemia and obesity in Asian Indians and also the trend of increasing obesity in LMIC in recent times. Investigators have shown that compared to other ethnic groups, children with ancestral origin in South Asia manifest central adiposity, insulin resistance and metabolic perturbations earlier in life and these derangements are of higher magnitude than white Caucasian children [30]. Both these components are associated with high odds of developing MetSyn and known to predispose to atherosclerotic heart disease [6, 31, 35]. Therefore, regular assessment of these individual components may aid in early identification of CCS at risk of developing MetSyn. This may also facilitate streamlining ACT activity and allocation of adequate manpower and resources to institute frequent monitoring, and counselling for aggressive risk-reduction strategies.  

The risk factors 

Delta BMI

It is well established that obesity is the central factor in the pathogenesis of insulin resistance and MetSyn [35]. However, it is noteworthy that most of our CCS who developed MetSyn were non-obese/overweight at diagnosis and a high delta BMI at follow-up strongly predicted MetSyn (OR 12.5[1.7-92]). Our results indicate that an 'increasing trend in BMI' reported as delta BMI may be of greater value, than a 'spot BMI' to look at risk of obesity and MetSyn [18, 32, 33]. Hence we believe that the trajectory of delta BMI should be monitored during follow-up visits to the ACT clinic and any recent large change in BMI should prompt evaluation for MetSyn. Therefore the observed mean increment in BMI z-score as a risk factor for development of MetSyn in CCS needs to be validated in a larger prospective study. Furthermore, it highlights the fact that monitoring should not be limited to only those who are obese/ overweight. 

Among the other potential risk factors, our study revealed older age at diagnosis, longer survival and high BMI at diagnosis to be the only independent predictors of MetSyn and similar results have been reported by other investigators [6, 35, 39]. While age at diagnosis and survival duration both reflect ageing and its ineliminable consequences, high BMI (obesity) is a modifiable fore-runner of MetSyn. 

Although we observed a higher prevalence of MetSyn and hypertriglyceridemia in males, on univariate analysis, sex was not found to be an independent prognostic factor. While the French L.E.A. study has reported a higher incidence in males [35], the St Jude Life Time Cohort Study found a higher prevalence in females, although statistically non-significant [6]. Another study in Asian Indians by Mohapatra et al has also observed male predominance [36]. Gender bias and preferential male treatment (overprotectiveness and overfeeding) in this part of the world may have been a contributory factor. 

We observed the prevalence of MetSyn to be similarly high across the spectrum of various childhood cancers (10-16%), including ALL survivors (12%) (Table 1). Hence, type of primary cancer as also the nature of treatment exposure did not significantly influence risk of MetSyn. Others have reported survivors of sarcomas, germ cell tumors, those treated with abdominal RT and BMT to be at higher risk of MetSyn. Furthermore, published studies have implicated CRT in patients with brain tumor and ALL as risk factors for the development of MetSyn [6, 34, 40-42]. Despite having a large cohort of ALL survivors we were unable to elicit cranial RT as a risk factor. The primary mediator of MetSyn associated with CRT is believed to be growth hormone deficiency leading to obesity and dyslipidemia and has been reported with RT doses of 18-24 Gy for ALL and 25 Gy for brain tumor. The CRT doses administered to majority of our ALL patients was lower (12.6 Gy) and may explain our observation that, except for exposure to CRT in very young children (<5 years) with ALL, we did not observe it to be a risk factor. Furthermore, our cohort included small numbers of survivors of brain tumor and BMT that precluded meaningful analysis. 

The main limitation of our study is that it was a cross-sectional cohort study and lacked serial, annual BMI assessments during follow-up. Furthermore we did not have age- and sex-matched control group for comparison. Nonetheless it has a number of strengths, it is one of the largest studies from a single LMIC centre wherein MetSyn was classified based on IDF criteria. It provides valuable insight into the risk factors of MetSyn in CCS and highlights  the novel predictive role of delta BMI in LMIC.

Conclusion

The present study demonstrates Asian Indian CCS to be at high risk for MetSyn and its components at an early age. Obesity and dyslipidemia were the two major contributing factors for its development and are both modifiable with lifestyle changes. Monitoring delta BMI during follow-up can be a useful tool for early identification of non-obese/overweight CCS, at risk of MetSyn that needs validation in larger longitudinal cohort. Due to the manifold increased risk of morbidity and mortality as a consequence of MetSyn, there is an overwhelming medical and ethical imperative for its early detection and timely intervention. Further research into the genetic landscape of MetSyn may help to better predict risk and individualize cancer treatment thereby leading to lower toxicities in the future.

Abbreviations

Abbreviation

expanded form

MetSyn

Metabolic syndrome

CCS

Childhood cancer survivors

LMIC

Lower middle-income countries

BMI

Body mass index

WC

Waist circumference

IDF

International Diabetes Federation 

TGs

Triglycerides

SBP

Systolic blood pressure

DBP

Diastolic blood pressure

FBG

Fasting blood glucose

HDL-c

High-density lipoprotein cholesterol

IFG

Impaired fasting glycemia

HT

Hypertension 

SD

standard deviation

IQR

Inter quartile range

ANOVA

Analysis of variance

OR

Odds ratio

ROC

Receiver operating characteristic

CI

Confidence interval

ALL

Acute lymphoblastic leukemia

RT

Radiotherapy

CRT

Cranial radiotherapy

BMT

Bone marrow transplant

ACT

After completion of therapy

 

Declarations

• Funding: 

Not applicable 

• Conflicts of interest/Competing interests: 

none 

• Ethics approval (include appropriate approvals or waivers): 

The study was approved by the ethical committee of the institute

• Consent to participate: 

Informed consent was obtained from all individual participants/guardians included in the study.

• Consent for publication: 

The participant has consented to the submission of the data in the manuscript to the journal.

• Availability of data and material (data transparency): 

present 

• Code availability (software application or custom code): 

not applicable

• Disclosure: 

Presented at annual national conference PHOCON 2018 held at Bangalore, November 2018 (abstract published): Agarwal A, Kapoor G, Jain S. Risk factors for metabolic syndrome in long-term survivors of childhood cancer: a single centre study. Pediatric Hematology Oncology Journal. 2018; 3(3):S1. DOI: 10.1016/j.phoj.2018.11.002.

• Authors' contributions:

AA: Data collection, analysis of data, writing the first draft manuscript

GK: Designing and conceptualizing the study, analysis and interpretation of data, drafting the manuscript, proofreading manuscript

SJ: Drafting and proof-reading the manuscript

PM: Drafting and proofreading the manuscript 

AS: Data analysis and statistics for the study

References

  1. Seth R, Singh A, Seth S, Sapra S (2017) Late effects of treatment in survivors of childhood cancers: A single-centre experience. Indian J Med Res 146:216–223
  2. Rajendranath R, Veeraiah S, Ramesh A, Sagar TG (2014) Late effects of treatment in survivors of childhood cancer from a tertiary cancer center in South India. South Asian J Cancer 3(1):60–65
  3. Prasad M, Arora B, Chinnaswamy G et al (2015) Nutritional status in survivors of childhood cancer: Experience from Tata Memorial Hospital, Mumbai. Indian J Cancer 52:219
  4. Henson KE, Reulen RC, Winter DL et al (2016) Cardiac Mortality Among 200 000 Five-Year Survivors of Cancer Diagnosed at 15 to 39 Years of Age: The Teenage and Young Adult Cancer Survivor Study. Circulation 134:1519–1531
  5. Mulrooney DA, Hyun G, Ness KK et al (2020) Major cardiac events for adult survivors of childhood cancer diagnosed between 1970 and 1999: report from the Childhood Cancer Survivor Study cohort. BMJ 368:l6794
  6. Nottage KA, Ness KK, Li C, Srivastava D et al (2014) Metabolic syndrome and cardiovascular risk among long-term survivors of acute lymphoblastic leukaemia - From the St. Jude Lifetime Cohort. Br J Haematol 165:364–374
  7. Feijen E, Font-Gonzalez A, Van der Pal HJH et al (2019) Risk and temporal changes of heart failure among 5-year childhood cancer survivors: a DCOG-LATER study. J Am Heart Assoc 8:e009122
  8. Oeffinger KC, Mertens AC, Sklar CA et al (2006) Chronic health conditions in adult survivors of childhood cancer. N Engl J Med 355:1572–1582
  9. Poyrazoglu S, Bas F, Darendeliler F (2014 Feb) Metabolic syndrome in young people. Curr Opin Endocrinol Diabetes Obes 21:56–63
  10. World Health Organisation. WHO factsheet 2020 [01/04/2020]. Available from: http://www. https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight
  11. Ford ND, Patel SA, Narayan KM (2017) Obesity in low and middle-income countries: burden, drivers, and emerging challenges. Annu Rev Public Health 38:145–164. doi:10.1146/annurev-publhealth-031816-044604 PMID:28068485
  12. Swinburn BA, Sacks G, Hall KD et al (2011) The global obesity pandemic: shaped by global drivers and local environments. Lancet 378:804–814
  13. Perez-Escamilla R, Bermudez O, Buccini GS et al (2018) Nutrition disparities and the global burden of malnutrition. BMJ 361:k2252
  14. Fox A, Feng W, Asal V (2019) What is driving global obesity trends? Globalization or “modernization”? Global Health 15:32. https://doi.org/10.1186/s12992-019-0457-y
  15. Kandala N-B, Emina JBO (2015) The dual burden of nutrition transition among women in sub-Saharan Africa: a case study of underweight in Nigeria. J Biosoc Sci 48:486–501
  16. Kirunda BE, Fadnes LT, Wamani H et al (2015) Population-based survey of overweight and obesity and the associated factors in peri-urban and rural Eastern Uganda. BMC Public Health 15:1168
  17. Doak CM, Adair LS, Bentley M et al (2004) The dual burden household and the nutrition transition paradox. Int J Obes 29:129–136
  18. Liu B, Li Y, Guo J et al. Body Mass Index and Its Change from Adolescence to Adulthood Are Closely Related to the Risk of Adult Metabolic Syndrome in China. Int J Endocrinol. 2021 Feb 18;2021:8888862. doi: 10.1155/2021/8888862. PMID: 33679975; PMCID: PMC7906799
  19. Sharma R (2017) Revised Kuppuswamy’s Socioeconomic Status Scale: Explained and Updated. Indian Pediatr 54:867–870
  20. World Health Organization (2000) The Asia Pacific Perspective- Redefining Obesity and Its treatment. WHO, Geneva
  21. Using the BMI-for-Age Growth Charts. [accessed on September 25, 2020]. Available from: http://www.cdc.gov/nccdphp/dnpa/growthcharts/training/modules/module1/text/module1print.pdf
  22. Khadilkar A, Ekbote V, Chiplonkar S et al (2014) Waist circumference percentiles in 2–18 year old Indian children. J Pediatr 164:1358–1362.e2
  23. Alberti KG, Zimmet P, Shaw J (2005) The IDF epidemiology task force consensus group: Metabolic syndrome: A new worldwide definition. Lancet 366:1059–1062
  24. Zimmet P, Alberti G, Kaufman F et al. International Diabetes Federation Task Force on Epidemiology and Prevention of Diabetes. The metabolic syndrome in children and adolescents. Lancet. 2007 Jun 23;369(9579):2059-61. doi: 10.1016/S0140-6736(07)60958-1. PMID: 17586288
  25. Gupta A, Sachdeva A, Mahajan N et al (2018) Prevalence of Pediatric Metabolic Syndrome and Associated Risk Factors among School-Age Children of 10–16 Years Living in District Shimla, Himachal Pradesh, India. Indian J Endocrinol Metab 22:373–378
  26. Bhat RA, Parray I, Ahmad Z (2014) Prevalence of the Metabolic Syndrome among North Indian Adolescents Using Adult Treatment Panel III and Pediatric International Diabetic Federation Definitions. J Diabetes Metab 5:352
  27. Usha SMR, Chandrika N, Shetty HV, Reena R (2014) A study of the components of metabolic syndrome in young adults. Biomed Res 25(1):45–50
  28. Gupta R, Deedwania PC, Gupta A et al (2004) Prevalence of metabolic syndrome in an Indian urban population. Int J Cardiol 97:257–261
  29. Zafar KS, Pious T, Singh PS et al (2017) Prevalence of metabolic syndrome in a rural population- a cross sectional study from Western Uttar Pradesh, India. Int J Res Med Sci 5:2223–2228
  30. Bhardwaj S, Misra A, Khurana et al (2008) Childhood obesity in Asian Indians: A burgeoning cause of insulin resistance, diabetes and sub-clinical inflammation. Asia Pac J Clin Nutr 17(Suppl 1):172–175
  31. Joshi SR, Anjana RM, Deepa M et al, ICMR-INDIAB Collaborative Study Group (2014) Prevalence of dyslipidemia in urban and rural India: the ICMR-INDIAB study. PLoS One 9(5):e96808
  32. Nielsen TRH, Fonvig CE, Dahl M et al (2018) Childhood obesity treatment; Effects on BMI SDS, body composition, and fasting plasma lipid concentrations. PLoS One 13(2):e0190576
  33. Birch L, Perry R, Hunt LP et al (2019) What change in body mass index is associated with improvement in percentage body fat in childhood obesity? A meta-regression. BMJ Open 9:e028231
  34. van Waas M, Neggers SJ, Pieters R, van den Heuvel-Eibrink MM (2010 May) Components of the metabolic syndrome in 500 adult long-term survivors of childhood cancer. Ann Oncol 21(5):1121–1126. doi:10.1093/annonc/mdp414. Epub 2009 Oct 22
  35. Saultier P, Auquier P, Bertrand Y et al (2016 Dec) Metabolic syndrome in long-term survivors of childhood acute leukemia treated without hematopoietic stem cell transplantation: an L.E.A. study. Haematologica 101(12):1603–1610
  36. Mohapatra S, Bansal D, Bhalla AK et al (2016) Is there an increased risk of metabolic syndrome among childhood acute lymphoblastic leukemia survivors? A developing country experience. Pediatr Hematol Oncol 33:136–149
  37. Tamilselvan ST, Scott JX, Sneha L, Divyalakshmi J (2017) Metabolic syndrome in childhood cancer survivors. J Clin Oncol 35:145
  38. Dhir AA, Daddi A, Sawant S, Prasad M (2018) Metabolic syndrome in adult survivors of childhood cancer. J Clin Oncol 36:e22102–e22102
  39. Oudin C, Berbis J, Bertrand Y et al (2018) Prevalence and characteristics of metabolic syndrome in adults from the French childhood leukemia survivors’ cohort: a comparison with controls from the French population. Haematologica 103:645–654
  40. Trimis G, Moschovi M, Papassotiriou I et al (2007) Early indicators of dysmetabolic syndrome in young survivors of acute lymphoblastic leukemia in childhood as a target for preventing disease. J Pediatr Hematol Oncol 29:309–314
  41. Oeffinger KC, Mertens AC, Sklar CA et al (2003) Obesity in adult survivors of childhood acute lymphoblastic leukemia: a report from the Childhood Cancer Survivor Study. J Clin Oncol 21(7):1359–1365
  42. Gunn HM, Emilsson H, Gabriel M et al (2016) Metabolic Health in Childhood Cancer Survivors: A Longitudinal Study in a Long-Term Follow-Up Clinic. J Adolesc Young Adult Oncol 5:24–30. doi:10.1089/jayao.2015.0036