Researchers identified a gene linked to thinness

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While others may be dieting and hitting the gym hard to stay in shape, some people stay slim effortlessly no matter what they eat.

In a study publishing May 21 in the journal Cell, researchers use a genetic database of more than 47,000 people in Estonia to identify a gene linked to thinness that may play a role in resisting weight gain in these metabolically healthy thin people.

They show that deleting this gene results in thinner flies and mice and find that expression of it in the brain may be involved in regulating energy expenditure.

“We all know these people: it’s around one percent of the population,” says senior author Josef Penninger, the director of the Life Sciences Institute and professor of the department of medical genetics at the University of British Columbia.

“They can eat whatever they want and be metabolically healthy. They eat a lot, they don’t do squats all the time, but they just don’t gain weight.

“Everybody studies obesity and the genetics of obesity,” he says. “We thought, ‘Let’s just turn it around and start a new research field.’ Let’s study thinness.”

Penninger’s team looked at data from the Estonian Biobank, which includes 47,102 people aged 20 to 44 years old. The team compared the DNA samples and clinical data of healthy thin individuals with normal-weight individuals and discovered genetic variants unique to thin individuals in the ALK gene.

Scientists have known that the ALK gene frequently mutates in various types of cancer, and it gained a reputation as an oncogene, a gene that drives the development of tumors.

The role of ALK outside of cancer has remained unclear. But this new finding suggested that the gene may play a role as a novel thinness gene involved in weight-gain resistance.

The researchers also found that flies and mice without ALK remained thin and were resistant to diet-induced obesity. Furthermore, despite having the same diet and activity levels as normal mice, mice with deleted ALK have lower body weight and body fat.

The team’s mouse studies also suggested that ALK, which is highly expressed in the brain, plays a part there by instructing the fat tissues to burn more fat from food.

The researchers say that therapeutics targeting the gene might help scientists fight obesity in the future.

“If you think about it, it’s realistic that we could shut down ALK and reduce ALK function to see if we did stay skinny,” says Penninger. “ALK inhibitors are used in cancer treatments already. It’s targetable. We could possibly inhibit ALK, and we actually will try to do this in the future.”

Further research will be required to see if these inhibitors are effective for this purpose. The team also plans to further study how neurons that express ALK regulate the brain at a molecular level to balance metabolism and promote thinness.

The Estonian Biobank that the team studied was ideal because of its wide age range and its strong phenotype data. But one limitation for replicating these findings is that biobanks that collect biological or medical data and tissue samples don’t have a universal standard in data collection, which makes comparability a challenge.

The researchers say they will need to confirm their findings with other data banks through meta-analysis. “You learn a lot from biobanks,” says Penninger. “But, like everything, it’s not the ultimate answer to life, but they’re the starting points and very good points for confirmation, very important links and associations to human health.”

The team says that its work is unique because of how it combines exploration of the genetic basis of thinness on a population- and genome-wide scale with in vivo analyses in mice and flies of the gene’s function.

“It’s great to bring together different groups, from nutrition to biobanking, to hardcore mouse and fly genetics,” says Penninger. “Together, this is one story including evolutionary trees in metabolism, the evolutionary role of ALK, human evidence, and hardcore biochemistry and genetics to provide causal evidence.”


Given the ever-growing epidemics of obesity and weight-related health complications such as type 2 diabetes and cardiovascular disease, many studies have focused on understanding metabolic changes associated with overweight and obesity.

However, novel insights on metabolic differences in humans might also come from studying people on the extreme opposite side of obesity, a state called constitutional thinness (CT).

CT is characterized by a stable low BMI, generally <18 kg/m2 (1, 2). Unlike in anorexia nervosa or other eating disorders associated with low weight, the nonpathological state of CT is defined by the lack of psychological features such as food aversion, refusal to eat alternated with binge eating, lower self-esteem, perfectionism, and body dissatisfaction (3).

In contrast to lipodystrophic and anorexic individuals, CT subjects have body fat mass percentages considered in the healthy range (4), albeit lower than people generally considered lean (BMI: 19–24 kg/m2).

Hormonal profiles are similar to those of lean subjects: they have no detectable abnormalities of cortisol, growth hormone, insulin-like growth factor 1, or free triiodothyronine secretory patterns (2) and normal menstrual cycles (5).

Lower leptin levels were detected in CT individuals compared with lean women (3). Daily calorie consumption is consistently similar in CT individuals and lean controls, while absolute resting metabolic rate is lower in CT individuals.

In addition, CT women were found to be resistant to weight gain upon fat overfeeding (6). To date, however, none of the studies on CT shed light on the possible molecular mechanisms involved in this thin, weight-resistant phenotype.

Moreover, clinical characterization has focused on female CT individuals only, while the CT phenotype also exists in males. In this study, we evaluated plasma and/or urinary proteins and metabolites before and after a 2-wk overfeeding intervention in both female and male CT individuals compared with lean, normal-weight participants.

We also analyzed mitochondrial function and transcriptomic profiles in the adipose tissue and muscle from CT and control subjects. Our results postulate that CT individuals are characterized by an adipose tissue–specific higher mitochondrial function as well as distinct transcriptomic profiles, testifying for altered lipid metabolism in CT.

This observation suggests that altered adipose tissue metabolic activity is a hallmark of the CT phenotype.

Results
Clinical characterization of CT individuals at baseline
Table 1 summarizes the results of the clinical characterization of the subjects at baseline. As a sex interaction was not observed for most variables, we combined both sexes within each group. Total fat and lean mass were lower in CT individuals compared with controls (both in absolute and relative terms).

We also identified lower systolic and diastolic blood pressure in CT individuals compared with controls. To exclude that the lower weight in CT individuals might be related to defects in gastrointestinal fat absorption, such as steatorrhea, we evaluated potential excess accumulation of fat in the feces (24).

However, neither visual inspection by the Bristol stool scale nor fat quantification in the stool showed any differences between the groups. Food intake was not different between the 2 groups, both in free-living as well as in the calorimetric chambers, and the proportions for each macronutrient category were similar between the groups (consisting on average of 48% carbohydrates, 37% lipids, and 15% protein relative caloric intake).

Snacking frequency and snacking caloric intake were significantly higher in CT individuals than controls, confirming previous results (6). Based on Actiheart measurements, we did not observe higher spontaneous physical activity in CT compared with control individuals. Rather, CT individuals had lower physical activity compared with controls.

Both REE and TEE were significantly lower in CT individuals compared with controls in both free-living and calorimetric chamber conditions, but this difference was blunted when correcting for fat-free mass (P = 0.92 in canopy and P = 0.12 in the calorimetric chamber) (Supplemental Figure 2).

Fasting RQ by canopy measurement was similar between CT individuals and controls while fasting fat oxidation rate was higher in CT individuals than controls, and the carbohydrate oxidation index was highly variable and nominally higher in CT individuals.

To complement standard energy expenditure measures, we also tracked internal Tc for 3 h postingestion with a noninvasive Jonah capsule temperature sensor.

Overall results show a slightly higher Tc in CT individuals compared with controls at baseline (Supplemental Figure 3A; ANOVA, P = 0.0087), with an average increase of +0.14°C pre- and postmeal.

TABLE 1 – Clinical variables in CT (n = 29, 14 females and 15 males) compared with control (n = 29, 15 females and 14 males) individuals at baseline 1

CharacteristicCTControlCT status, PCT status × sex, P
Age, y 24.97 ± 4.7 22.63 ± 2.93   
BMI, kg/m2 16.96 ± 0.74 22.99 ± 1.03   
Waist-hip ratio 0.77 ± 0.06 0.78 ± 0.07 0.042 0.28 
Total fat, kg 9.71 ± 1.86 18.71 ± 5.20 1.7 × 10−230.98 
Total fat body mass, % 19.59 ± 4.97 27.09 ± 7.89 4.5 × 10−080.53 
Total lean body mass, kg 39.28 ± 6.70 49.79 ± 9.70 2.0 × 10−260.72 
Total lean body mass, % 80.41 ± 4.97 72.56 ± 8.13 4.4 × 10−080.61 
Heart rate, bpm 69.21 ± 10.68 65.28 ± 10.38 0.033 0.031 
Systolic blood pressure, mm Hg 115.07 ± 11.86 125.72 ± 9.83 4.2 × 10−050.17 
Diastolic blood pressure, mm Hg 66.21 ± 9.36 71.21 ± 6.67 6.0 × 10−040.079 
Fat stool, g/24 h 3.16 ± 2.13 4.14 ± 2.31 0.25 0.39 
Energy variables 
 Free-living condition 
  Total caloric intake, kcal/24 h 2179.62 ± 433.09 2004.55 ± 482.75 0.45 0.062 
  Carbohydrates, g 264.14 ± 65.22 227 ± 58.01 0.16 0.036 
  Protein, g 79.48 ± 19.53 80.34 ± 20.24 0.3 0.053 
  Lipids, g 88.52 ± 18.64 81.38 ± 20.24 0.77 0.23 
  Snacking calories, kcal/24 h 328.2 ± 192.67 177.08 ± 138.82 2.1 × 10−030.82 
  Snacking frequency 1.47 ± 0.72 0.95 ± 0.48 7.7 × 10−030.88 
  Physical activity level 1.55 ± 0.21 1.75 ± 0.21 9.1 × 10−040.016 
  Fasting respiratory quotient 0.82 ± 0.04 0.8 ± 0.05 0.6 0.07 
  Fat oxidation index, mg/min/kg 1.00 ± 0.31 0.91 ± 0.27 0.016* 0.027 
  Carbohydrate oxidation index, mg/min/kg 1.34 ± 0.84 0.80 ± 0.59 0.022 0.16 
  REE, kcal/24 h 1255.14 ± 228.91 1466.83 ± 239.82 1.4 × 10−100.62 
  TEE, kcal/24 h 1966.14 ± 458.79 2561.8 ± 591.65 2.5 × 10−100.15 
 Calorimetric chamber 
  Total caloric intake, kcal/24 h 1919.71 ± 267.5 2007.39 ± 235.53 0.025 0.800 
  REE, kcal/24 h 1442.53 ± 386.89 1653.98 ± 331.45 8.10 × 10−08* 0.62 
  TEE, kcal/24 h 1819.77 ± 243.94 2180.2 ± 284.21 9.10 × 10−20* 0.88 
Lipid metabolism 
 Cholesterol, mmol/L 4.46 ± 0.78 4.13 ± 0.78 0.74 0.29 
 HDL-C, mmol/L 1.55 ± 0.36 1.36 ± 0.35 0.18 0.82 
 LDL-C, mmol/L 2.48 ± 0.76 2.30 ± 0.72 0.83 0.55 
 Triglycerides, mmol/L 0.93 ± 0.26 0.87 ± 0.29 0.59 0.33 
 Glycerol, μmol/L 31.24 ± 28.47 23.78 ± 20.08 0.55 0.19 
  NEFAs, μmol/L 389.97 ± 202.38 372 ± 223.82 0.29 0.71 
Glucose metabolism 
 Fasting glucose, mmol/L 4.66 ± 0.34 4.68 ± 0.44 0.81 0.46 
 Fasting insulin, mUI/L 6.07 ± 3.67 7.51 ± 2.75 5.7 × 10−040.36 
Blood biochemical parameters 
 IGF-1, µg/L 241.62 ± 86.59 254.8 ± 52.51 0.033 0.097 
 Leptin, µg/L 3.76 ± 2.23 9.34 ± 6.77 0.34 0.27 
 Metadrenaline, nmol/L 0.20 ± 0.09 0.16 ± 0.06 0.18 0.75 
 Normetadrenaline, nmol/L 0.38 ± 0.11 0.34 ± 0.1 0.11 0.063 
 TSH, mUI/L 2.38 ± 1.31 2.06 ± 1.01 0.38 0.33 
 Free T3, pmol/L 5.98 ± 1.99 5.44 ± 0.66 0.66 0.52 
 Free T4, pmol/L 16.83 ± 3.38 17.21 ± 1.68 0.99 0.2 
Urinary metabolites 
 Creatinine, mmol/L 10.05 ± 2.78 13.75 ± 3.6 4.7 × 10−110.56 
 Urea, mmol/L 263.11 ± 95.08 385.52 ± 143.25 3.5 × 10−090.17 
 Uric acid, mmol/L 2.62 ± 0.78 3.53 ± 0.94 7.0 × 10−090.92 

Data are presented as means ± SDs. P values are assessed by repeated-measures ANOVA for outcomes adjusted for sex and age. A Bonferroni correction was applied for each set of measure to define a result as significant (defined by *). bpm, beats per minute; CT, constitutional thinness; HDL-C, HDL cholesterol; IGF-1, insulin-like growth factor 1; LDL-C, LDL cholesterol; NEFA, nonesterified fatty acid; REE, resting energy expenditure; TEE, total energy expenditure; TSH, thyrotropin; T3, triiodothyronine; T4, thyroxine.

Assessment of bone quality of the radius and tibia by HR-pQCT demonstrated that many bone quality parameters were lower in CT individuals compared with controls, with the main differences observed in tibia (Supplemental Table 2), confirming previous results in females (25). The lower bone quality was observed in both sexes.

At baseline, on the molecular level, circulating lipid metabolites, including cholesterol, triglycerides, free fatty acids, and fasting and postprandial kinetics of glucose and kinetics of insulin, were similar between the groups (Table 1, Supplemental Figure 3B and 3C), confirming previous results (2), while fasting insulin levels were lower in CT individuals.

Measured urinary metabolites, including urea, creatinine, and uric acid, were significantly lower in CT individuals than control subjects (FC 0.68, 0.73, and 0.74, respectively). Untargeted proteomic analyses of the plasma samples revealed 11 proteins that were differentially abundant between CT individuals and controls at baseline (Supplemental Table 3), including insulin-like growth factor binding protein 2 and dopamine β-hydroxylase (DBH) among the top plasma proteins with increased levels in CT individuals compared with controls.

Molecular analyses of adipose tissue and muscle demonstrate a distinct adipose-specific mitochondrial signature in CT

To characterize the metabolic state of CT individuals in more depth, we next performed analyses on 2 key metabolic tissues, adipose tissue and muscle. H&E staining of the sWAT did not reveal any visual difference in terms of extracellular matrix remodeling or inflammation. CT individuals had a marked smaller adipocyte size (black bars, 2174 ± 142 μm2) compared with controls (white bars, 3586 ± 216 μm2) at baseline (P < 0.001) (Figure 1A).

This decrease was reflected by a higher frequency of small adipocytes in CT individuals (Supplemental Figure 4). In addition, males overall had on average smaller adipocytes than their female counterparts (males, 2666 ± 253 μm2; females, 3067 ± 197 μm2) (P < 0.05) (Figure 1A).

We then evaluated the mitochondrial function through high-resolution respirometry analyses in both sWAT and skeletal muscle. The oxygen fluxes during leak respiration—independent of ADP-phosphorylating activity—were similar between CT individuals and controls (Figure 1B), suggesting that there was no difference in uncoupling. Immunohistochemical staining of UCP1 also revealed the lack of presence of UCP1 in the sWAT samples compared with a human BAT sample used as positive control (Supplemental Figure 5).

Interestingly, we observed significantly higher mitochondrial CII activity in CT individuals (FC 2.17, P = 0.001), as well as CI + CII activity (FC 1.71, P = 0.03) and maximal ETS activity (FC 2.07, P = 0.001) compared with controls. No significant difference in mitochondrial activity was detected between males and females.

To evaluate if the increase in CII and ETS activity was due to an increase in mitochondrial number, we performed a quantitative mtDNA assay (defined as ratio of mitochondrial compared with nuclear DNA).

The results showed a significantly higher amount of mtDNA per adipocyte (CT 784 ± 27 mtDNA molecules compared with control 675 ± 30 mtDNA molecules per cell) (P = 0.02) (Figure 1C), with no sex difference observed. This suggests that adipocytes of CT individuals have an increased number of mitochondria and that this contributes, at least in part, to the increased CII and ETS activity observed in CT.

In skeletal muscle, uncoupled respiration (leak) was not different in CT individuals compared with controls (Supplemental Figure 6). Similarly, maximal respiration in the coupled state, with electron input through CI alone, CI and CII, CII alone, or maximal ETS capacity, was comparable between CT individuals and controls, even though there was a tendency toward lower CI and CII and ETS capacity, but it was not statistically significant.

This suggests that the higher mitochondrial activity and content observed in sWAT of CT individuals are not due to a systemic difference in mitochondrial regulation, but rather are specific to the white adipose tissue.

Distinct differences in adipose tissue in constitutional thinness (CT). (A) Adipocyte area (μm2) measured by histological analysis. Values are expressed as mean and SEM. Mean values are from at least 10 pictures per slide (capturing more than 1000 cells per sample). Black bars correspond to CT individuals ( n = 29, 15 females and 14 males), while white bars correspond to the controls (n = 29, 15 females and 14 males). (B) Respirometry analyses in subcutaneous white adipose tissue (sWAT) using high-resolution respirometry. Values are expressed as mean and SEM from 10 CT individuals (5 females, 5 males; black bars) and 13 controls (9 females, 4 males; white bars) before and after overfeeding (OF; dashed bars). CI, complex I respiration; CI + CII, complex I and II respiration; CII, complex II activity alone; ETS, maximal electron transport system capacity. (C) Mitochondrial DNA content in sWAT at baseline in CT individuals ( n = 29) compared with controls (n = 29). Associations between CT status and variables were tested using a logistic regression adjusted on confounders (age and sex) at baseline and after intervention. Statistical significance level after correction for multiple testing when comparing CT versus controls is indicated by * for P < 0.05 and ** for P < 0.01, while # indicates a significance level at P < 0.05 of the intervention (before vs after intervention).
FIGURE 1 – Distinct differences in adipose tissue in constitutional thinness (CT). (A) Adipocyte area (μm2) measured by histological analysis. Values are expressed as mean and SEM. Mean values are from at least 10 pictures per slide (capturing more than 1000 cells per sample). Black bars correspond to CT individuals ( n = 29, 15 females and 14 males), while white bars correspond to the controls (n = 29, 15 females and 14 males). (B) Respirometry analyses in subcutaneous white adipose tissue (sWAT) using high-resolution respirometry. Values are expressed as mean and SEM from 10 CT individuals (5 females, 5 males; black bars) and 13 controls (9 females, 4 males; white bars) before and after overfeeding (OF; dashed bars). CI, complex I respiration; CI + CII, complex I and II respiration; CII, complex II activity alone; ETS, maximal electron transport system capacity. (C) Mitochondrial DNA content in sWAT at baseline in CT individuals ( n = 29) compared with controls (n = 29). Associations between CT status and variables were tested using a logistic regression adjusted on confounders (age and sex) at baseline and after intervention. Statistical significance level after correction for multiple testing when comparing CT versus controls is indicated by * for P < 0.05 and ** for P < 0.01, while # indicates a significance level at P < 0.05 of the intervention (before vs after intervention).

Transcriptomic profiling of adipose tissue and muscle confirm altered lipid metabolism in CT

To gain further insights into the molecular basis of the observed differences in mitochondrial respiration and content, we profiled the RNA expression levels of the sWAT and muscle by total RNA-seq. Comparison of the expression profiles in sWAT revealed significant differences between the 2 groups (Figure 2A).

We did not observe any sex differences in the gene expression profiles. Using an FDR of 0.1 and a log2 FC threshold of 0.75, we identified 88 Ensembl gene IDs (ENSG) differentially expressed between CT individuals and controls, of which 62 corresponded to annotated genes.

To validate the gene expression results, we designed probes for those 62 genes and assessed gene expression by Nanostring technology. RNA-seq and Nanostring data amply correlated (Figure 2B, Spearman’s r = 0.8926, P < 0.0001), and 54 genes passed the threshold FDR of 0.1, corresponding to a validation of 87% of the RNA-seq results. Given the higher number of mitochondria found in CT, we first performed a GO cellular component enrichment analysis and found 21 terms that had a significant adjusted P value lower than 0.01, of which 9 belonged to the mitochondria (Supplemental Table 4), showing indeed an enrichment in genes belonging to the mitochondrial cellular component.

In parallel, pathway analyses of the entire data set revealed increased oxidative metabolism as evidenced by significant upregulation of fatty acid oxidation but strikingly also triglyceride biosynthesis (Figure 2C).

In addition, the IL-8 signaling pathway was reduced. IL-8 is a key proinflammatory mediator, known to be associated with obesity (26). Enrichment using the MGI Mammalian Phenotype identified nominal enrichment in abnormal lipid levels (MP0001547), decreased body fat amount (MP0010025), and other interesting enrichments such as altered angiogenesis (MP0000260) and improved glucose tolerance (MP0005292) (Supplemental Table 5).

Consistently, by zooming in on the specific top differentially expressed genes (Table 2), we found that many of the differences were associated with known positive metabolic outcomes in CT. These included decreased LEP, EGFL6, MMP9, and HSD11B1; elevated APOC1; and increased fatty acid synthesis, including elevated expression of FASN, SCD, ELOVL6, and AACS. Several antiangiogenic factors (THBS1, TNMD, and EGFL6), as well as oxidative stress factors (HSPB7, CRYAB, and UCHL1), were significantly downregulated. In addition, we identified multiple differentially expressed genes that have not been previously described for a potential role in metabolism. RNA-seq profiling in skeletal muscle samples did not reveal any significantly differentially expressed genes between CT individuals and controls (passing FDR of 0.1) (Supplemental Figure 7).

The lack of robust transcriptomic alterations in the muscle is in line with the fact that respiration in CT muscle is not significantly affected either. We therefore postulate that the molecular signature correlating with the CT phenotype is most likely specifically linked to an alteration in lipid metabolism in the adipose tissue rather than a systemic difference in mitochondrial biogenesis affecting other metabolically active tissues as well.

Transcriptomic analyses of adipose tissue at baseline. (A) Volcano plot showing differential expression of constitutional thinness (CT) (n = 30) compared with control (n = 30) individuals. Dashed lines correspond to the log2 fold change (FC) and false discovery rate (FDR) of 10%. The top differentially expressed hits are represented by orange dots, the significant hits but not strongly differentially expressed are represented by dark blue dots, and the nonsignificant hits not selected are represented by light blue dots. Differential expression was tested by using a logistic regression model correcting for age and sex followed by a Wald test to assess the statistical significance of the coefficient of interest. As no sex interaction was found, association was evaluated without interaction term. Benjamini–Hochberg multiple testing correction was applied within the fitted model on the derived P values (FDR). (B) Correlation is shown between RNA sequencing (RNA-seq) and Nanostring gene expression analysis of top 65 differentially expressed genes, tested using a nonparametric Spearman correlation (Spearman's r = 0.8926; 95% CI: 0.82, 0.94; P < 0.0001). (C) Ingenuity Pathway Analysis showing the top 5 significantly affected pathways. Genes that passed the FDR-adjusted P value < 10% were used as input data (orange and dark blue dots in the volcano plot, corresponding to 753 genes). Percentage overlap with the corresponding pathway is shown, as well as the number of hits compared with total number of genes in the pathway in parentheses.
FIGURE 2 – Transcriptomic analyses of adipose tissue at baseline. (A) Volcano plot showing differential expression of constitutional thinness (CT) (n = 30) compared with control (n = 30) individuals. Dashed lines correspond to the log2 fold change (FC) and false discovery rate (FDR) of 10%. The top differentially expressed hits are represented by orange dots, the significant hits but not strongly differentially expressed are represented by dark blue dots, and the nonsignificant hits not selected are represented by light blue dots. Differential expression was tested by using a logistic regression model correcting for age and sex followed by a Wald test to assess the statistical significance of the coefficient of interest. As no sex interaction was found, association was evaluated without interaction term. Benjamini–Hochberg multiple testing correction was applied within the fitted model on the derived P values (FDR). (B) Correlation is shown between RNA sequencing (RNA-seq) and Nanostring gene expression analysis of top 65 differentially expressed genes, tested using a nonparametric Spearman correlation (Spearman’s r = 0.8926; 95% CI: 0.82, 0.94; P < 0.0001). (C) Ingenuity Pathway Analysis showing the top 5 significantly affected pathways. Genes that passed the FDR-adjusted P value < 10% were used as input data (orange and dark blue dots in the volcano plot, corresponding to 753 genes). Percentage overlap with the corresponding pathway is shown, as well as the number of hits compared with total number of genes in the pathway in parentheses.

TABLE 2 – List of genes differentially expressed in adipose tissue at baseline from CT (n = 30) compared with control (n = 30) individuals at baseline1

RNA-seqNanostring
Ensembl gene IDSymbolEntrez gene nameExpression log2 FCExpression FDRExpression log2 FCExpression FDRCorrelation FDR
ENSG00000198759 EGFL6 Epidermal GF-like domain multiple 6 –2.422 7.78 × 10−02 –1.829 1.17 × 10−02 7.10 × 10−10 
ENSG00000119125 GDA Guanine deaminase –2.392 7.78 × 10−02 –1.947 1.87 × 10−02 2.50 × 10−13 
ENSG00000123119 NECAB1 N-terminal EF-hand calcium binding protein 1 –2.355 7.78 × 10−02 –3.142 7.00 × 10−03 6.00 × 10−31 
ENSG00000100985 MMP9 Matrix metallopeptidase 9 –1.83 8.02 × 10−02 –1.423 1.47 × 10−02 1.20 × 10−10 
ENSG00000113389 NPR3 Natriuretic peptide receptor 3 –1.519 7.78 × 10−02 –1.683 5.49 × 10−03 2.20 × 10−16 
ENSG00000079689 SCGN Secretagogin, EF-hand calcium binding protein –1.293 7.78 × 10−02 –0.627 5.43 × 10−02 2.20 × 10−16 
ENSG00000174697 LEP Leptin –1.243 7.78 × 10−02 –1.214 3.98 × 10−03 2.20 × 10−16 
ENSG00000154277 UCHL1 Ubiquitin C-terminal hydrolase L1 –1.218 7.78 × 10−02 –1.081 4.25 × 10−03 7.40 × 10−31 
ENSG00000182255 KCNA4 Potassium voltage-gated channel subfamily A m4 –1.132 7.78 × 10−02 –1.407 6.41 × 10−03 2.20 × 10−19 
ENSG00000183117 CSMD1 CUB and Sushi multiple domains 1 –1.122 8.02 × 10−02 –0.748 1.87 × 10−02 2.80 × 10−05 
ENSG00000113739 STC2 Stanniocalcin 2 –1.108 7.78 × 10−02 –1.116 6.61 × 10−03 2.50 × 10−08 
ENSG00000172995 ARPP21 cAMP-regulated phosphoprotein 21 –1.078 8.17 × 10−02 –1.089 5.49 × 10−03 1.30 × 10−04 
ENSG00000137801 THBS1 Thrombospondin 1 –1.041 7.78 × 10−02 –1.020 3.98 × 10−03 2.20 × 10−16 
ENSG00000163075 CFAP221 Cilia flagella-associated protein 221 –1.024 7.78 × 10−02 –1.135 3.98 × 10−03 2.20 × 10−16 
ENSG00000000005 TNMD Tenomodulin –0.975 7.78 × 10−02 –0.768 5.36 × 10−03 2.20 × 10−16 
ENSG00000158714 SLAMF8 SLAM family member 8 –0.975 9.79 × 10−02 –0.892 5.58 × 10−02 4.80 × 10−07 
ENSG00000186439 TRDN Triadin –0.972 9.70 × 10−02 –1.188 1.24 × 10−02 2.20 × 10−16 
ENSG00000178826 TMEM139 Transmembrane protein 139 –0.943 7.78 × 10−02 –1.049 1.47 × 10−02 8.90 × 10−14 
ENSG00000099957 P2RX6 Purinergic receptor P2 × 6 –0.919 7.78 × 10−02 –1.229 5.22 × 10−03 1.60 × 10−10 
ENSG00000110092 CCND1 Cyclin D1 –0.893 7.78 × 10−02 –0.804 4.30 × 10−03 2.20 × 10−16 
ENSG00000164188 RANBP3L RAN binding protein 3 like –0.868 7.78 × 10−02 –1.020 6.41 × 10−03 2.20 × 10−16 
ENSG00000109846 CRYAB Crystallin α–0.858 7.78 × 10−02 –0.935 3.98 × 10−03 2.20 × 10−16 
ENSG00000117154 FCGBP Immunoglobin superfamily member 21 –1.3181 8.97 × 10−02 –0.921 4.49 × 10−02 8.40 × 10−08 
ENSG00000188778 ADRB3 Adrenoceptor β–0.824 9.09 × 10−02 –0.720 4.82 × 10−03 2.20 × 10−16 
ENSG00000102359 SRPX2 Sushi repeat containing protein, X-linked 2 –0.816 7.78 × 10−02 –0.852 3.98 × 10−03 4.90 × 10−31 
ENSG00000173641 HSPB7 Heat shock protein family B (small) m7 –0.811 7.78 × 10−02 –0.813 3.98 × 10−03 2.20 × 10−16 
ENSG00000117594 HSD11B1 Hydroxysteroid 11-β dehydrogenase 1 –0.803 8.99 × 10−02 –0.704 2.54 × 10−02 2.20 × 10−16 
ENSG00000147852 VLDLR Very-low-density lipoprotein receptor –0.767 8.17 × 10−02 –0.883 4.82 × 10−03 2.20 × 10−16 
ENSG00000077274 CAPN6 Calpain 6 0.76 7.78 × 10−02 0.777 3.98 × 10−03 2.20 × 10−16 
ENSG00000169710 FASN Fatty acid synthase 0.769 8.58 × 10−02 0.727 5.49 × 10−03 2.20 × 10−16 
ENSG00000206384 COL6A6 Collagen type VI α6 chain 0.772 7.78 × 10−02 0.903 4.55 × 10−03 2.20 × 10−16 
ENSG00000183798 EMILIN3 Elastin microfibril interfacer 3 0.811 9.27 × 10−02 0.449 5.96 × 10−02 9.30 × 10−05 
ENSG00000081760 AACS Acetoacetyl-CoA synthetase 0.816 8.60 × 10−02 0.783 7.52 × 10−03 2.20 × 10−16 
ENSG00000166126 AMN Amnion-associated transmembrane protein 0.855 7.78 × 10−02 0.568 7.63 × 10−02 1.70 × 10−04 
ENSG00000112394 SLC16A10 Solute carrier family 16 member 10 0.866 7.78 × 10−02 1.447 3.98 × 10−03 8.60 × 10−13 
ENSG00000060709 RIMBP2 RIMS binding protein 2 0.87 9.10 × 10−02 0.708 1.12 × 10−02 2.20 × 10−16 
ENSG00000253379 EYA1 EYA transcriptional coactivator and phosphatase 1 0.902 7.78 × 10−02 1.419 3.98 × 10−03 1.50 × 10−17 
ENSG00000099194 SCD Stearoyl-CoA desaturase 0.911 7.78 × 10−02 1.100 3.98 × 10−03 2.20 × 10−16 
ENSG00000124003 MOGAT1 Monoacylglycerol O-acyltransferase 1 0.912 8.02 × 10−02 1.213 3.98 × 10−03 2.20 × 10−16 
ENSG00000168702 LRP1B LDL receptor-related protein 1B 0.919 7.78 × 10−02 0.717 5.44 × 10−02 2.20 × 10−16 
ENSG00000130208 APOC1 Apolipoprotein C1 0.92 7.78 × 10−02 0.870 2.76 × 10−02 2.20 × 10−16 
ENSG00000140284 SLC27A2 Solute carrier family 27 member 2 0.926 9.88 × 10−02 1.132 1.32 × 10−02 2.20 × 10−16 
ENSG00000113494 PRLR Prolactin receptor 0.979 7.78 × 10−02 1.080 5.27 × 10−03 2.20 × 10−16 
ENSG00000198963 RORB RAR-related orphan receptor B 0.986 7.78 × 10−02 0.877 5.49 × 10−03 2.20 × 10−16 
ENSG00000138678 GPAT3 Glycerol-3-phosphate acyltransferase 3 1.065 7.78 × 10−02 1.087 9.85 × 10−03 2.20 × 10−16 
ENSG00000164879 CA3 Carbonic anhydrase 3 1.072 7.78 × 10−02 1.327 7.52 × 10−03 2.20 × 10−16 
ENSG00000135744 AGT Angiotensinogen 1.087 7.78 × 10−02 1.116 4.82 × 10−03 2.20 × 10−16 
ENSG00000143365 RORC RAR-related orphan receptor C 1.098 7.78 × 10−02 1.660 3.98 × 10−03 2.20 × 10−16 
ENSG00000145428 RNF175 Ring finger protein 175 1.098 7.78 × 10−02 1.119 1.87 × 10−02 7.60 × 10−09 
ENSG00000170522 ELOVL6 ELOVL fatty acid elongase 6 1.167 7.78 × 10−02 1.204 8.19 × 10−02 2.20 × 10−16 
ENSG00000160339 FCN2 Ficolin 2 1.181 7.78 × 10−02 1.294 5.36 × 10−03 2.20 × 10−16 
ENSG00000015520 NPC1L1 NPC1-like intracellular cholesterol transporter 1 1.276 7.78 × 10−02 0.765 3.50 × 10−02 5.50 × 10−05 
ENSG00000103460 TOX3 TOX high-mobility group box family member 3 1.282 7.78 × 10−02 1.378 8.73 × 10−02 1.70 × 10−11 
ENSG00000172425 TTC36 Tetratricopeptide repeat domain 36 1.295 7.78 × 10−02 1.260 6.41 × 10−03 3.50 × 10−10 

Genes passing an FDR of 0.1 and a log2 FC threshold of 0.75 are reported here. Differential expression was tested by using a logistic regression model correcting for age and sex followed by a Wald test to assess the statistical significance of the coefficient of interest. As no sex interaction was found, association was evaluated without interaction term. Benjamini–Hochberg multiple testing correction was applied within the fitted model on the derived P values (FDR). Correlation tests between RNA-seq and Nanostring gene quantification were performed using a nonparametric Spearman correlation test. CT, constitutional thinness; FC, fold change; FDR, false discovery rate; RNA-seq, RNA sequencing.

Short-term overfeeding did not result in a different metabolic response in CT subjects

To investigate whether the metabolic response to overfeeding was different in CT compared with controls, the participants were next given a 600-kcal surplus to their diet for 14 d (300-mL shake in the evening, containing 30 g protein, 72 g carbohydrate, and 21 g fat). Overall compliance to the overfeeding regimen was confirmed by a significant increase in urea excretion in both groups relative to the baseline (FC 1.22, P < 0.001) (Supplemental Table 6).

Both groups gained a small but significant amount of weight during the overfeeding (P < 0.001), but no significant difference in weight gain was observed between the 2 groups (Supplemental Figure 8).

Even though some clinical and energy balance parameters were altered upon overfeeding (Supplemental Table 6), no differences in response to the overfeeding were observed in CT individuals compared with controls. Similarly, tissue respiration (Figure 1BSupplemental Figure 6) and transcriptomic profiling (Supplemental Figures 9 and 10), as well as plasma proteomics (Supplemental Table 3), all showed some differences upon the intervention, but none demonstrated a differential response to the overfeeding between CT individuals compared with controls.

Together, these results demonstrate that the molecular response to the overfeeding is similar in CT individuals compared with controls and that the main molecular differences observed at baseline remain present after the overfeeding.

Supplementary data

nqz144_Supplemental_File – pdf file

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More information:Cell, Orthofer et al.: “Identification of ALK in thinness” www.cell.com/cell/fulltext/S0092-8674(20)30497-9 , DOI: 10.1016/j.cell.2020.04.034

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