Abstract
Background:
Non-alcoholic fatty liver disease (NAFLD), the most common liver disease among youth with obesity, precedes more severe metabolic and liver diseases. However, the impact of the Sars-CoV-2 global pandemic on the prevalence and severity of NAFLD and the associated metabolic phenotype among youth with obesity is unknown.
Methods:
Subjects were recruited from the Yale Pediatric Obesity Clinic during (August 2020 – May 2022) and compared to a frequency matched control group of youth with obesity studied before the Sars-CoV-2 global pandemic (January 2017 – November 2019). Glucose metabolism differences were assessed during an extended 180-min oral glucose tolerance test. MRI-Proton Density Fat Fraction was utilized to determine intrahepatic fat content NAFLD (PDFF ≥ 5.5).
Results:
NAFLD prevalence increased in subjects prior to (37.3%) verses during the Sars-CoV-2 pandemic (60.9%), with higher PDFF values observed in NAFLD subjects (PDFF ≥ 5.5%) during versus pre-pandemic. An increase in visceral adipose tissue and a hyperresponsiveness in insulin secretion during the OGTT was also observed.
Conclusions:
Hepatic health differences were likely exacerbated by environmental and behavioral changes associated with the pandemic, which are critically important for clinicians to consider when engaging in patient care to help minimize the future risk for metabolic perturbations.
Keywords: NAFLD, Obesity, Oral Glucose Tolerance Test, SARS-CoV-2 Pandemic
Introduction
Over 35% of adolescent children in the United States are classified as overweight (body mass index [BMI] ≥ 85th percentile) or obese (BMI ≥ 95th percentile; 1). Pediatric obesity is often associated with a higher prevalence of adipose tissue insulin resistance (AT-IR) resulting from an unfavorable abdominal fat distribution (2, 3). Additionally, the altered abdominal fat distribution patterns and the decreased ability of insulin to suppress lipolysis results in the excess accumulation of ectopic fat within the liver that manifests as Non-Alcoholic Fatty Liver Disease (NAFLD; 3–7). NAFLD is the most common liver disease among children (8), and incidence rates have increased by over 60% during the past decade (9). Although many children with obesity are underdiagnosed with NAFLD, the presence of NAFLD is a metabolic risk factor that may precede whole body insulin resistance, the development of pre-diabetes, and the rapid progression into Type 2 diabetes (T2D) as well as more advanced liver diseases (9–11). Currently, no approved treatments exit, suggesting that the early diagnosis is imperative for the implementation of early strategies to prevent disease progression.
National and world-wide stay-at-home mandates and school closures in response to the novel severe acute respiratory syndrome coronavirus-2 (Sars-CoV-2) have exacerbated adolescent obesity rates and associated metabolic complications (12). Investigations into the phenotypic changes to central adiposity, the development of metabolic dysregulation, and changes in the prevalence and severity of NAFLD among youth with obesity are necessary to understand the potential impact of the Sars-CoV-2 global pandemic on overall metabolic health in youth and to provide clinicians with additional insights for more effective patient care strategies. Thus, this study in youth with overweight/obesity primarily aimed to examine the effect of Sars-Co V-2 pandemic on anthropometric and glucose metabolism measures, as well as intrahepatic fat content. During the Sars-CoV-2 pandemic, rising prevalence in NAFLD and its association with lipid and glucose dysmetabolism were hypothesized to be more closely associated with an unfavorable abdominal fat distribution patterns.
Methods
Study Participants
Subjects were selected for the current retrospective cross-sectional study from the Yale Study of the Pathophysiology of Prediabetes/T2D in Youth, an ongoing investigation based at the Yale Pediatric Obesity Clinic aiming to identify the underlying pathophysiology of prediabetes in youth (NCT01967849). We defined a Pre-Sars-CoV-2 (PS) period from January 2017 through November 2019, and during Sars-CoV-2 (S) period from August 2020 through May 2022. The intervening period from December 2019 through February 2020 was excluded from analysis because it overlapped the period between the initial identification of the Sars-CoV-2 outbreak in Wuhan, China and the implementation of pandemic-related lockdowns in the United States. As such, the differentiation subjects as falling within the PS vs S timeframe and the potential pathophysiological consequences was unable to be identified. Likewise, all clinical research activities at Yale University were put on hold from March 2020 to July 2020. One-hundred and five (105) subjects aged 8 to 20 years old with a BMI > 85th percentile were selected for the during pandemic period and frequency-matched for sex, race/ethnicity, age (± 2 years) and BMI (±2 kg/m2; Figure 1) with participants enrolled in the two years prior to the Sars-CoV-2 (PS). The frequency-matching was used to control for confounding variables stemming from demographic and anthropometric characteristics of subjects in the two pandemic timing periods. All subjects provided their written informed consent and assent to allow their collected data to be utilized for clinical research. Likewise, research clinicians at the Yale Center for Clinical Investigation (YCCI) obtained a complete medical history and physical examination. The study was approved by the Yale University Human Investigation Committee.
Figure 1.
Flow chart demonstrating the selection process for subjects examined in the two years prior (Jan. 2017-Nov. 2019) and during the Sars-CoV-2 pandemic (Aug. 2020-May 2022). Abbreviations: BMI: body mass index; OGTT: oral glucose tolerance test; MRI: magnetic resonance imaging; PYOD: pediatric youth obesity clinic; Sars-CoV-2: severe acute respiratory syndrome coronavirus-2.
Oral Glucose Tolerance Test
Following an overnight fast, subjects arrived at the YCCI and height, weight, and body fat percentage were immediately calculated by bioelectrical impedance (Tanita Scale, Arlington Heights, IL, USA). All subjects then underwent an OGTT performed at the YCCI. Whole blood samples were collected from an intravenous (IV) line inserted in the antecubital vein for the analysis of glucose, insulin, and C-peptide at baseline and at 10, 20, and 30 min, then every 30 min up to 180 min following oral glucose challenge (1.75 g/kg body weight: 75 g maximum). Homeostatic model assessment for insulin resistance (HOMA-IR; 17), whole-body insulin sensitivity index (WBISI; 18), and hepatic insulin resistance index (HIRI; 19) were calculated. during the OGTT. Finally, AT-IR was calculated by multiplying fasting insulin and fasting free fatty acid concentrations.
Abdominal Fat Distribution and Intrahepatic Fat Content by MRI-Proton Density Fat Fraction
Following the OGTT, a multi-section abdominal MRI was performed at the Magnetic Resonance Research Center (Siemens Sonata 3.0 Tesla System; Erlangen, Germany). Abdominal visceral (VAT) and subcutaneous adipose tissue (SAT) distribution was determined using a threshold to discriminate fat from soft tissue at the level of the L4/L5 disc space on a single slice. Deep (DeepSAT) and superficial SAT (SupSAT) were determined based on their division by the fascia superficialis (13). The ratios of VAT-to-total (VAT+SAT) and DeepSAT-to-SupSAT were also calculated to identify changes fat distribution patterns among subjects. Liver fat fraction content was measured by MRI using the proton density fat fraction (PDFF; 14). The advanced MRI-magnitude-based method used to estimate PDFF in children is a noninvasive assessment of liver health moderately associated with histological analysis, and in adults, it is an accurate and reproducible imaging-based biomarker for assessing steatosis and treatment response in nonalcoholic steatohepatitis (NASH) patients participating in RCT (15). For out study, NAFLD was defined as a PDFF ≥ 5.5 (16).
Biochemical Analyses
Glucose concentrations were examined bedside using a YSI2700-STAT-Analyzer (Yellow Springs Instruments, Yellow Springs, OH). Insulin and C-peptide concentrations from plasma isolated from whole blood by centrifugation were measured using radioimmunoassay (Linco, St. Charles, MO) and ALPCO-Immunoassays (Salem, NH), respectively. Adiponectin (R&D Systems; Minneapolis, MN) and nonesterified fatty acid (Wako; Richmond, VA) concentrations were determined by a standard spectrophotometric assay. Area-under-the-curves (AUCs) were calculated using the trapezoidal rule.
Statistical Analyses
Categorical variables were compared between groups (pandemic timing: PS vs. S, and presence of fatty liver disease: NAFLD versus no-NAFLD) using Pearson χ2 test. Differences in continuous data were determined by Student’s t-test or one-way ANOVA, as well as Mann-Whitney U or Kruskal-Wallis tests. Bonferroni correction was used to examine pairwise comparisons by pandemic timing among children with NAFLD and among children with no-NAFLD. Hypothesis tests were conducted using the two-sided alpha of 0.05 and inference was supplemented by the magnitude of the estimated effect sizes.
Results
Demographics Characteristics of the Pre-Sars-CoV-2 (PS) and Sars-CoV-2 (S) Cohorts.
Patients in PS cohort (n = 105) and those in the S cohort (n = 105) were, by design, frequency-matched for sex (boys: n = 49; girls: n = 56 in each group), race/ethnicity (White: n = 27; African American: n = 26; Hispanic: n = 40; Asian American and Pacific Islander: n = 5; unknown or not documented: n = 7 in each group), age (p = 0.955), and BMI (p = 0.888; eTable 1). Despite the similar demographics and indices of body composition, the two cohorts differed in their abdominal fat distribution, intrahepatic fat accumulation, and markers of insulin resistance and transaminase levels (eTable 2).
Prevalence of NAFLD in the PS and S Cohorts
The prevalence of NAFLD increased from 36.2% in patients prior to the Sars-CoV-2 pandemic to 60.9% in subjects who participated in the study throughout the Sars-CoV-2 pandemic (p < .001; Figure 2A). The median PDFF was higher in subjects examined throughout the Sars-CoV-2 pandemic compared to those examined prior to the Sars-CoV-2 pandemic (p < 0.001; eFigure 1). The shift in PDFF distribution was pronounced among patients with NAFLD, but not among children without NAFLD (p = 0.547; Figure 2B).
Figure 2.
The prevalence of NAFLD (No = PDFF < 5.5; Yes = PDFF ≥ 5.5) increased from 37.3% of subjects examined in the two years prior to the Sars-CoV-2 pandemic (Jan. 2017-Nov. 2021) to 60.9% in pair-matched subjects examined during the Sars-CoV-2 pandemic (Aug. 2020-May 2022; Panel A). In contrast to subjects without NAFLD, PDFF increased in subjects examined during the Sars-CoV-2 pandemic compared to those examined in the two years prior (panel B). Data in the boxplot are presented as medians (with interquartile range: 25th and 75th percentile with min and max). The * indicates a difference among groups with p ≤ 0.05 determined from either Persons χ2 analysis (panel A) or Mann-Whitney U test (panel B). Abbreviations: NAFLD: non-alcoholic fatty liver disease; PDFF: proton density fat fraction; Sars-CoV-2: severe acute respiratory syndrome coronavirus-2.
Anthropometric and Cardiometabolic Profiles of PS and S Cohorts Stratified by the Presence of NAFLD
Sex was distributed equally across all four groups (p = .211; Table 1), with the percentage of males with NAFLD increasing from 42.8% to 69.4% and the percentage of females with NAFLD increasing from 30.4% to 53.6% during the Sars-CoV-2 pandemic. In contrast, race/ethnicity was unequally distributed (p < 0.001). Differences in distribution of race/ethnicity are largely due to the low prevalence of NAFLD observed among African Americans (7.7%) and the higher prevalence rates observed among White (37.0%) and Hispanic subjects (55.0%) prior to the Sars-CoV-2 pandemic, findings which are consistent with our previous reports (20). Although the prevalence of NAFLD increased among each race/ethnicity throughout the Sars-CoV-2 pandemic, the prevalence remained lower (26.9%) in African Americans compared to other populations (all ≥ 67%). No differences in age were observed across the groups.
Table 1:
Subject Descriptive Characteristics
| Variable | No NAFLD (PDFF < 5.5) | NAFLD (PDFF > 5.5) | p-value | ||
|---|---|---|---|---|---|
| Pre Sars-CoV-2 (n = 67) | Sars-CoV-2 (n = 41) | Pre Sars-CoV-2 (n = 38) | Sars-CoV-2 (n = 64) | ||
| Sex (B/G) | 28/39 | 15/26 | 21/17 | 34/30 | ^0.258 |
| Ethnicity (Wh/AA/His/AAPI/Oth) | 17/24/18/2/6 | 6/19/13/1/2 | 10/2/22/3/1 | 21/7/27/4/5 | ^< 0.001 |
| Age | 15 (13, 16) | 14 (12, 16) | 14 (11.75, 16) | 14 (13, 16) | 0.778 |
| Height (m) | 1.64 ± 0.10 | 1.65 ± 0.10 | 1.62 ± 0.09 | 1.67 ± 0.11 | 0.114 |
| Weight (kg) | 95.60 (79.40, 110.00) | 98.10 (85.00, 112.25) | 93.3 (79.23, 109.4) | 97.15 (79.635, 115.6) | 0.640 |
| BMI (kg/m2) | 34.69 (31.25, 40.78) | 35.62 (31.39, 42.21) | 35.99 (32.16, 41.44) | 35.06 (31.54, 39.07) | 0.517 |
| z-score | 2.35 (2.07, 2.58) | 2.47 (2.02, 2.63) | 2.42 (2.16, 2.71) | 2.36 (2.08, 2.64) | 0.599 |
| Percentile | 99.07 (98.08, 99.50) | 99.33 (97.84, 99.58) | 99.23 (98.48, 99.67) | 99.09 (98.11, 99.59) | 0.599 |
| Body Fat (%) | 43.28 ± 9.39 | 46.07 ± 9.28 | 46.22 ± 9.84 | 44.52 ± 9.00 | 0.367 |
| Lean Body Mass (kg) | 55.03 ± 14.71 | 53.90 ± 10.31 | 57.87 ± 17.41 | 54.02 ± 11.76 | 0.533 |
| Fat Mass (kg) | 37.92 (31.56, 51.07) | 45.31 (32.30, 57.05) | 46.51 (32.37, 55.78) | 41.30 (33.66, 54.84.) | 0.371 |
| Waist-to-Hip Ratio | 0.945 (0.896, 0.983) | 0.937 (0.881, 0.991) | 0.944 (0.917, 1.000) | 0.949 (0.908, 1.00) | 0.395 |
| HbA1c | 5.55 (5.4, 5.8) | 5.5 (5.4, 5.975) | 5.6 (5.4, 5.8) | 5.6 (5.3, 5.85) | 0.966 |
| Total Cholesterol (mg/dL) | 152 (139, 178) | 156 (148, 171) | 171 (149, 210) | 168.5 (142.25, 180.5) | 0.369 |
| HDL (mg/dL) | 42 (36, 49) | 42 (36.5, 49.5) | 45 (38, 51) | 39 (36, 45.75) | 0.229 |
| LDL (mg/dL) | 96.65 ± 26.41 | 94.00 ± 28.57 | 98.69 ± 28.34 | 98.30 ± 27.39 | 0.919 |
| Triglycerides (mg/dL) | 106 (78, 164) | 75.5 (62.25, 111.5) | 115 (89, 190) ‡ | 135 (101.25, 179.5) ‡ | 0.001 |
| Triglycerides:HDL Ratio | 2.29 (1.41, 3.14) | 1.99 (1.28, 2.85) | 2.54 (1.72, 7.28) | 3.635 (2.600, 4.786)* ‡ | < 0.001 |
| Free Fatty Acids (mEq/L) | 0.56 (0.41, 0.64) | 0.49 (0.38, 0.63) | 0.62 (0.47, 0.74)‡ $ | 0.52 (0.41, 0.59) | 0.026 |
| Fasting Glucose (mg/dL) | 88 (85.75, 92.25) | 88 (83, 94.5) | 91.5 (87.5, 100) | 89 (86, 94) | 0.184 |
| 2-hour Glucose (mg/dL) | 116 (102.5, 133) | 121 (103.5, 137) | 125 (115, 141.5) | 129 (111, 141) | 0.125 |
| Fasting Insulin (μU/mL) | 24 (16 – 38.5) | 31 (18 – 39.5) | 30 (25, 45.5) | 36 (30, 52)* ‡ | < 0.001 |
| 2-hour Insulin (μU/mL) | 126 (66, 186.5) | 139 (69, 216) | 144.5 (102.25, 211.25) | 223 (152.75, 349.67)* ‡ | < 0.001 |
| Fasting C-peptide (pmol/L) | 991 (765.5, 1391.25) | 1040 (821, 1410) | 1217 (988.25, 1850.25) * | 1400 (1235, 1903)* ‡ | < 0.001 |
| 2-Hour C-peptide (pmol/L) | 3720 (2790, 5013.5) | 3490 (2560, 5186.5) | 4627 (3280, 5840) | 5360 (4513, 6333)* ‡ | < 0.001 |
| HOMA-IR | 6.77 (3.59, 8.963) | 7.07 (3.97, 8.92) | 9.08 (4.89, 10.58) | 9.51 (6.27, 11.60)* ‡ | < 0.001 |
| WBISI | 2.1 (1.25, 2.65) $ | 1.97 (1.05, 2.83) $ | 1.5 (0.98, 1.95) | 1.19 (0.81, 1.50) | < 0.001 |
| AT-IR | 11876.39 (6283.38, 15409.13) | 13375.79 (7156.13, 16814.50) | 18011.06 (10677.49, 22334.63)* ‡ | 19593.33 (12028.00, 25370.00)* ‡ | < 0.001 |
| HIRI | 15.37 (9.19, 18.94) | 15.69 (8.31, 21.09) | 24.61 (12.66, 30.30) | 22.06 (14.57, 26.86)* ‡ | < 0.001 |
| Adiponectin (μg/mL) | 6.78 (5.00, 10.30) | 6.77 (5.02, 9.66) | 6.73 (5.12, 9.22) | 5.22 (3.84, 6.65)* # ‡ | 0.002 |
| ALT (U/L) | 20 (16, 28.5) | 22.5 (17, 40.25) | 27 (20, 43.5) | 45 (30, 59)* # ‡ | < 0.001 |
| AST (U/L) | 21 (18, 25.25) | 23 (20.5, 30.5) | 23 (20, 32) | 31 (25, 50.5)* # ‡ | < 0.001 |
| Alkaline Phosphatase | 128 (89.25, 219.5) | 193 (77.75, 282.5) | 140 (86, 198.5) | 154 (95.75, 245.25) | 0.581 |
valueNote: Data are presented as means ± S.D or as medians (Interquartile range: 25th, 75th percentile).
The * indicates a significant increase compared to PS-nN;
the # indicates an increase with p < 0.05 compared to PS-NAFLD;
the ‡ indicates an increase with p < 0.05comapred to S-nN;
the $ indicates an increase with p < 0.05comapred to S-NAFLD (one-way ANOVA or Kruskal-Wallis test; Bonferroni post-hoc analysis).
The ^ indicates p-valued determined from Persons χ2 analysis.
Abbreviations: ALT: alanine transaminase; AST: aspartate transaminase; AT-IR: adipose tissue insulin resistacne; BMI: body mass index; HbA1c; hemoglobin A1c; HDL: high-density lipoprotein; HIRI: hepatic insulin resistance index; HOMA-IR: homeostatic model assessment for insulin sensitivity; LDL: low-density lipoprotein; NAFLD: non-alcoholic fatty liver disease; PDFF: proton density fat fraction; Sars-CoV-2: severe acute respiratory syndrome coronavirus-2; SAT: subcutaenous adipose tissue; VAT: viscercal adipose tissue; Ethnicities: WBISI: whole-body insulin sensitivity index; Wh = White, non-Hispanic; AA = African American, non-Hispanic; His = Hispanic; AAPI = Asian American and Pacific Islander; Oth = Other, non-Hispanic
Abdominal Fat Distribution Patterns of the PS and S Cohorts Stratified by the Presence of NAFLD
A shift in the distribution of abdominal fat was observed across all cohorts. Although no differences in DeepSAT or SAT were present across the four groups, SupSAT was greater in PS-nN compared to S-NAFLD patients (p = 0.011), whereas the ratio of DeepSAT-to-SupSAT (p < 0.001), VAT (p < 0.001), and the ratio of VAT-to-total (VAT+SAT; p < 0.001) were lower in PS-nN compared to all other groups (Figures 3A–F). Furthermore, S-NAFLD subjects exhibited greater VAT than S-nN and a greater VAT-to-total ratio compared to all other groups.
Figure 3.
Abdominal adipose tissue distribution patterns among each subject group. Data in boxplots are presented as medians (with interquartile range: 25th and 75th percentile with min and max). The * indicates an increase with p < 0.05 compared to PS-nN; the # indicates an increase with p < 0.05 compared to PS-NAFLD; the ‡ indicates an increase with p < 0.05 compared to S-nN; and the $ indicates an increase with p < 0.05 compared to S-NAFLD determined by One-Way ANOVA (Post-Hoc: Bonferroni) or Kruskal-Wallis test for non-parametric data. Abbreviations: Sars-CoV-2: severe acute respiratory syndrome coronavirus-2; SAT: subcutaneous adipose tissue; VAT: visceral adipose tissue.
No differences in HbA1c and total, LDL, and HDL cholesterol emerged among the four groups, whereas triglycerides and the ratio of triglycerides-to-HDL concentrations were greater in the two NAFLD compared to the two nN groups (p = 0.001 and p < 0.001, respectively; Table 1). Finally, concentrations of adiponectin (p < 0.002) and the liver enzymes ALT (p < 0.001) and AST (p < 0.001), but not alkaline phosphate (p = 0.581), were elevated in the S-NAFLD compared to all other groups (Table 1).
OGTT Responses in the PS and S Cohorts by the Presence of NAFLD
Fasting concentrations of glucose were not different among the groups (p = 0.184; Figure 4A), whereas fasting insulin and C-peptide were elevated in S-NAFLD compared to PS-nN and S-nN patients (p < 0.001 and p < 0.001, respectively; Figures 4B and C). In response to oral glucose challenge, no differences were noted in fasting or 2-hour glucose. However, a greater rise in glucose excursion was noted in the PS-NAFLD and S-NAFLD groups compared to PS-nN subjects (min 20, 30, and 60; p = 0.022; Figure 4D). The insulin response to the oral glucose challenge in the S-NAFLD group was also greater compared to all other groups (p < 0.001; Figure 4E). Finally, C-peptide concentrations were greater in both PS- and S-NAFLD compared to PS- and S-nN subjects (p < 0.001; Figure 4F). Consistent with these findings, HOMA-IR (p < 0.001), AT-IR (p < 0.001), and HIRI were elevated (p < 0.001), whereas WBISI was lower (p < 0.001; Table 1) in S-NAFLD compared to both PS- and S-nN subjects.
Figure 4.
180-minute OGTT results among each subject group. Data in the line plots are presented as means (± standard error of the means; panels A-C) and data in the boxplots are presented as medians (with interquartile range: 25th and 75th percentile with min and max; panels D-F). The * indicates an increase with p < 0.05 compared to PS-nN; the # indicates an increase with p < 0.05 compared to PS-NAFLD; the ‡ indicates an increase with p < 0.05 compared to S-nN; and the $ indicates an increase with p < 0.05 compared to S-NAFLD determined by Kruskal-Wallis test for non-parametric data. Abbreviations: AUC: area-under-the-curve; OGTT: oral glucose tolerance test; Sars-CoV-2: severe acute respiratory syndrome coronavirus-2.
Discussion
The current study examined the immediate impact of the Sars-CoV-2 pandemic and associated environment in a sample of youth with overweight and obesity undergoing detailed metabolic phenotyping of glucose and insulin metabolism and advanced MRI-magnitude-based assessment of intrahepatic fat accumulation and abdominal fat distribution. As a result, the prevalence of NAFLD was shown to increase from 37.3% in subjects prior to the Sars-CoV-2 pandemic to 60.9% in subjects studied throughout the Sars-CoV-2 pandemic. These findings among our clinical population during the Sars-CoV-2 pandemic indicates a clinically significant shift in the severity of PDFF elevation was observed among those with NAFLD.
Explanations of our findings may be rooted in the environmental changes imposed by the pandemic to minimize COVID exposure could have still accelerated the worsening of NAFLD which may put youth with obesity at greater risk for poor cardiometabolic outcomes. Our group has previously shown that hepatic fat fraction (HFF) values do not significantly change within individuals across a time of about 2 years (21), suggesting that the rate of progression from NAFLD diagnosis to more severe stages of liver disease is slow. However, stay-at-home mandates and school closures reduced household economic security, limited access to safe and routine physical activity, increased food insecurity, and prevented social interactions (22, 23). These changes may have contributed to increases in De Novo Lipogenesis and triglyceride content in the liver (24–27). and the significant environmental changes caused by the pandemic may have accelerated the timeframe in which our subjects progress to more severe stages of liver disease. Likewise, the near 62% increase in the number of subjects observed to have a PDFF ≥ 5.5 underscores the importance of early and ongoing intervention for children and supports for need for innovative virtual weight management programs alongside in-person programming as strategies to mitigate the negative long-lasting effects of increased obesity and risk for co-morbidities (28, 29).
One potential phenotypic change associated with the worsening of NAFLD observed within the present study is a significant shift in the distribution of abdominal fat observed in S-NAFLD subjects and highlighted by a greater VAT than S-nN and a greater VAT-to-total ratio compared to all other groups. A previous study from our group suggested that an imbalance between the visceral and subcutaneous fat depots and a corresponding dysregulation of the adipokine milieu is associated with excessive accumulation of fat in the liver (2). Although our study cannot establish the cause of the increased prevalence of hepatic steatosis during the Sars-CoV-2 pandemic, the finding that an increase in the visceral depot in the absence of changes to the SAT depot and the overall degree of obesity reinforces the role of body fat distribution in the onset of fatty liver and insulin resistance in youth with obesity.
Compared to all other groups, the S-NAFLD group also showed a hyperresponsiveness in insulin secretion following the OGTT and markers of insulin resistance where higher in the S-NAFLD group compared to both PS- and S-nN groups. More recent studies using the large pediatric NAFLD-CRN cohort followed longitudinally over 3.8 years found that T2D developed in 16% of children during a follow-up of 3 years (30). Although, the pathophysiological mechanisms contributing to T2D development in youth with NAFLD remains unclear, Newton et al. recently demonstrated that approximately 60% of adolescents with NAFLD developed NAFLD before the development of diabetes (30). The severity of hepatic histology at baseline in children with NAFLD without T2D has also been shown to be a significant risk factor for subsequent T2D as both steatosis grade and fibrosis stage independently increased risk for incident T2D (30). Previous studies by Cali et al. using MRI-HFF in youth with obesity found that intrahepatic triglyceride content equal or greater than 10% was inversely correlated with insulin sensitivity and beta-cell function (11). Our present current study provides further support that in the context of NAFLD, the liver may directly set the stage for abnormal glucose and lipid metabolism. Therefore, a longitudinal follow-up study is necessary to determine whether these participants do develop T2D over time.
Limitations to this study include the exposure-control nature of our investigation which may reflect a selection bias. Although we aimed to control for potential selection bias by frequency-matching on important demographic and anthropometric characteristics, those who participated in the research investigation during the pandemic might still represent a population who is at a greater metabolic health risk and more likely to seek medical care than the PS patient population. Likewise, we do not have access to the number of subjects during the pandemic who became infected, and thus the hypothesis that viral infection altered the metabolic phenotype (i.e., whole body insulin resistance) and contributed to increased NAFLD is not testable in the current cohort. Another potential limitation involves the use of MRI-PDFF. The overall accuracy for PDFF in predicting pediatric steatosis stage determined by histology is 56%, and the accuracy of PDFF as related to histological grading is greatest among patients with low (< 5%) and high (> 25%) PDFF values (31). The MRI-PDFF test is also expensive, and access is often limited to tertiary academic centers. Therefore, its utilization is often individualized and ordered by the liver specialist (32). Despite these limitations, the rigorous measures of intrahepatic fat content by MRI-PDFF coupled with as assessment of β-cell function and insulin sensitivity using an extended 180min-OGTT with repeated measurements of glucose, insulin and c-peptide, and the use of surrogate biomarkers of insulin resistance such as the AT-IR, HIRI, and adiponectin levels remains strengths of this study.
In this direct comparison of two cohorts of youth with obesity studied in two very different Sars-CoV-2 pandemic-related time periods, critical differences in the prevalence of NAFLD and the severity of the associated metabolic profiles were clearly observed. Differences in hepatic health were likely exacerbated by environmental and behavioral changes associated with the pandemic (22, 23). These likely drivers of NAFLD among youth are critically important for clinicians to consider when engaging in patient care to help minimize the risk for metabolic perturbations in the future. Likewise, future studies should include larger cohorts with racial and ethnic diversity to thoroughly examine the metabolic pathophysiology and the potential impact of racial health disparities on adolescent health outcomes throughout the Sars-CoV-2 pandemic within the United States.
Supplementary Material
What is already known about the subject?
Non-alcoholic fatty liver disease (NAFLD) is the most common liver disease among youth with obesity and precedes more severe metabolic and liver disease.
Investigations into the impact of the severe acute respiratory syndrome coronavirus-2 (Sars-CoV-2) global pandemic on changes in the prevalence and severity of NAFLD and associated metabolic phenotype among youth with obesity are necessary.
What are the new findings in your manuscript?
NAFLD prevalence increased from 37.3% to 60.9% in pair-matched subjects studied prior to verses during the Sars-CoV-2 pandemic, with significant differences in PDFF values observed in subjects with NAFLD (PDFF ≥ 5.5%).
Phenotypic changes associated with the increased prevalence of NAFLD during the Sars-CoV-2 pandemic included an increase in visceral adipose tissue and a hyperresponsiveness in insulin secretion during the OGTT.
How might these results change the direction of research or focus of clinical practice?
Differences in hepatic health were likely exacerbated by environmental and behavioral changes associated with the pandemic.
These likely drivers of NAFLD among youth are critically important for clinicians to consider when engaging in patient care to help minimize the risk for metabolic perturbations in the future.
Funding:
Sonia Caprio: National Institutes of Health (NIH; grants: R01-HD028016 and R01-DK111038); Daniel Vatner: NIH (grant: RO1-DK-124272); Jordan Strober: NIH (grant: T32-DK-007058); Aaron L. Slusher: Robert E. Leet and Clara Guthrie Patterson Trust Mentored Research Award.
Footnotes
Conflicts of Interest: The authors declared no conflict of interest.
References
- 1.Fryar CD, Carroll MD, Afful J. Prevalence of overweight, obesity, and severe obesity among children and adolescents aged 2–19 years: United States, 1963–1965 through 2017–2018. NCHS Health E-States. 2020. [Google Scholar]
- 2.Taksali SE, Caprio S, Dziura J, Dufour S, Calí AM, Goodman TR, Papademetris X, Burgert TS, Pierpont BM, Savoye M, Shaw M, Seyal AA, Weiss R. High visceral and low abdominal subcutaneous fat stores in the obese adolescent: a determinant of an adverse metabolic phenotype. Diabetes 2008;57:367–371. [DOI] [PubMed] [Google Scholar]
- 3.Kursawe R, Eszlinger M, Narayan D, Liu T, Bazuine M, Cali AM, D’Adamo E, Shaw M, Pierpont B, Shulman GI, Cushman SW, Sherman A, Caprio S. Cellularity and adipogenic profile of the abdominal subcutaneous adipose tissue from obese adolescents: association with insulin resistance and hepatic steatosis. Diabetes 2010;59:2288–2296. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Prokopowicz Z, Malecka-Tendera E, Matusik P. Predictive Value of Adiposity Level, Metabolic Syndrome, and Insulin Resistance for the Risk of Nonalcoholic Fatty Liver Disease Diagnosis in Obese Children. Can J Gastroenterol Hepatol 2018;2018:9465784. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Morandi A, Di Sessa A, Zusi C, Umano GR, El Mazloum D, Fornari E, Miraglia Del Giudice E, Targher G, Maffeis C. Nonalcoholic Fatty Liver Disease and Estimated Insulin Resistance in Obese Youth: A Mendelian Randomization Analysis. J Clin Endocrinol Metab 2020;105:dgaa583. [DOI] [PubMed] [Google Scholar]
- 6.Scapaticci S, D’Adamo E, Mohn A, Chiarelli F, Giannini C. Non-Alcoholic Fatty Liver Disease in Obese Youth With Insulin Resistance and Type 2 Diabetes. Front Endocrinol (Lausanne) 2021;12:639548. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Umano GR, Shabanova V, Pierpont B, Mata M, Nouws J, Tricò D, Galderisi A, Santoro N, Caprio S. A low visceral fat proportion, independent of total body fat mass, protects obese adolescent girls against fatty liver and glucose dysregulation: a longitudinal study. Int J Obes (Lond) 2019;43:673–682. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Schwimmer JB, Deutsch R, Kahen T, Lavine JE, Stanley C, Behling C. Prevalence of fatty liver in children and adolescents. Pediatrics 2006;118:1388–1393. [DOI] [PubMed] [Google Scholar]
- 9.Sahota AK, Shapiro WL, Newton KP, Kim ST, Chung J, Schwimmer JB. Incidence of Nonalcoholic Fatty Liver Disease in Children: 2009–2018. Pediatrics 2020;146:e20200771. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Ekstedt M, Franzén LE, Mathiesen UL, Thorelius L, Holmqvist M, Bodemar G, Kechagias S. Long-term follow-up of patients with NAFLD and elevated liver enzymes. Hepatology 2006;44:865–73. [DOI] [PubMed] [Google Scholar]
- 11.Cali AM, Man CD, Cobelli C, Dziura J, Seyal A, Shaw M, Allen K, Chen S, Caprio S. Primary defects in beta-cell function further exacerbated by worsening of insulin resistance mark the development of impaired glucose tolerance in obese adolescents. Diabetes Care 2009;32:456–461. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Kim ES, Kwon Y, Choe YH, Kim MJ. COVID-19-related school closing aggravate obesity and glucose intolerance in pediatric patients with obesity. Sci Rep 2021;11:5494. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Ross R, Aru J, Freeman J, Hudson R, Janssen I. Abdominal adiposity and insulin resistance in obese men. Am J Physiol Endocrinol Metab 2002;282:E657–663. [DOI] [PubMed] [Google Scholar]
- 14.Tang A, Tan J, Sun M, Hamilton G, Bydder M, Wolfson T, Gamst AC, Middleton M, Brunt EM, Loomba R, Lavine JE, Schwimmer JB, Sirlin CB. Nonalcoholic fatty liver disease: MR imaging of liver proton density fat fraction to assess hepatic steatosis. Radiology 2013;267:422–431. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Loomba R, Friedman SL, Shulman GI. Mechanisms and disease consequences of nonalcoholic fatty liver disease. Cell 2021;184:2537–2564. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.D’Adamo E, Cali AM, Weiss R, Santoro N, Pierpont B, Northrup V, and Caprio S. Central role of fatty liver in the pathogenesis of insulin resistance in obese adolescents. Diabetes Care 2010;33:1817–1822. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985; 28: 412–9. doi: 10.1007/BF00280883. [DOI] [PubMed] [Google Scholar]
- 18.Matsuda M, DeFronzo RA. Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp. Diabetes Care 1999;22:1462–1470. [DOI] [PubMed] [Google Scholar]
- 19.Abdul-Ghani MA, Matsuda M, Balas B, DeFronzo RA. Muscle and liver insulin resistance indexes derived from the oral glucose tolerance test. Diabetes Care 2007;30:89–94. [DOI] [PubMed] [Google Scholar]
- 20.Tricò D, Galderisi A, Mari A, Polidori D, Galuppo B, Pierpont B, Samuels S, Santoro N, Caprio S. Intrahepatic fat, irrespective of ethnicity, is associated with reduced endogenous insulin clearance and hepatic insulin resistance in obese youths: A cross-sectional and longitudinal study from the Yale Pediatric NAFLD cohort. Diabetes Obes Metab 2020;22:1628–1638. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Kim G, Giannini C, Pierpont B, Feldstein AE, Santoro N, Kursawe R, Shaw M, Duran E, Goldberg R, Dziura J, Caprio S. Longitudinal effects of MRI-measured hepatic steatosis on biomarkers of glucose homeostasis and hepatic apoptosis in obese youth. Diabetes Care 2013;36:130–136. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Javed B, Sarwer A, Soto EB, Mashwani ZU. Impact of SARS-CoV-2 (Coronavirus) Pandemic on Public Mental Health. Front Public Health 2020;8:292. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Snape MD, Viner RM. COVID-19 in children and young people. Science 2020;370:286–288. [DOI] [PubMed] [Google Scholar]
- 24.Egli L, Lecoultre V, Cros J, Rosset R, Marques AS, Schneiter P, Hodson L, Gabert L, Laville M, Tappy L. Exercise performed immediately after fructose ingestion enhances fructose oxidation and suppresses fructose storage. Am J Clin Nutr 2016;103:348–355. [DOI] [PubMed] [Google Scholar]
- 25.Softic S, Cohen DE, Kahn CR. Role of Dietary Fructose and Hepatic De Novo Lipogenesis in Fatty Liver Disease. Dig Dis Sci. 2016; 61: 1282–93. doi: 10.1007/s10620-016-4054-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Adams EL, Caccavale LJ, Smith D, Bean MK. Food Insecurity, the Home Food Environment, and Parent Feeding Practices in the Era of COVID-19. Obesity (Silver Spring) 2020;28:2056–2063. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Hu P, Samuels S, Maciejewski KR, Li F, Aloe C, Van Name M, Savoye M, Sharifi M. Changes in Weight-Related Health Behaviors and Social Determinants of Health among Youth with Overweight/Obesity during the COVID-19 Pandemic. Child Obes 2022;18:369–382. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Woo Baidal JA, Chang J, Hulse E, Turetsky R, Parkinson K. Zooming toward a telehealth solution for vulnerable children with obesity during Coronavirus Disease 2019. Obesity (Silver Spring) 2020;28:1184–1186. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Pecoraro P, Gallè F, Muscariello E, Di Mauro V, Daniele O, Forte S, Ricchiuti R, Liguori G, Valerio G. A telehealth intervention for ensuring continuity of care of pediatric obesity during the CoVid-19 lockdown in Italy. Nutr Metab Cardiovasc Dis 2021;31:3502–3507. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Newton KP, Wilson LA, Crimmins NA, Fishbein MH, Molleston JP, Xanthakos SA, Behling C, Schwimmer JB. Nonalcoholic Steatohepatitis Clinical Research Network. Incidence of Type 2 Diabetes in Children with Nonalcoholic Fatty Liver Disease. Clin Gastroenterol Hepatol 2022:S1542-3565(22)00556–0. [Google Scholar]
- 31.Schwimmer JB, Middleton MS, Behling C, Newton KP, Awai HI, Paiz MN, Lam J, Hooker JC, Hamilton G, Fontanesi J, Sirlin CB. Magnetic resonance imaging and liver histology as biomarkers of hepatic steatosis in children with nonalcoholic fatty liver disease. Hepatology 2015;61:1887–1895. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Cusi K, Isaacs S, Barb D, Basu R, Caprio S, Garvey WT, Kashyap S, Mechanick JI, Mouzaki M, Nadolsky K, Rinella ME, Vos MB, Younossi Z. American Association of Clinical Endocrinology Clinical Practice Guideline for the Diagnosis and Management of Nonalcoholic Fatty Liver Disease in Primary Care and Endocrinology Clinical Settings: Co-Sponsored by the American Association for the Study of Liver Diseases (AASLD). Endocr Pract 2022;28:528–562. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.